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The Rebound Effect: an assessment of the

evidence for economy-wide energy savings

from improved energy efficiency

A report produced by the Sussex Energy Group for

the Technology and Policy Assessment function of

the UK Energy Research Centre

Steve Sorrell

October 2007

ISBN 1-903144-0-35
This report has been produced by the UK Energy Research Centre’s Technology and Policy

Assessment (TPA) function. 

The TPA was set up to address key controversies in the energy field through

comprehensive assessments of the current state of knowledge. It aims to provide

authoritative reports that set high standards for rigour and transparency, while explaining

results in a way that is both accessible to non-technical readers and useful to

policymakers. 

This report summarises the main conclusions from the TPA’s assessment of evidence for

a rebound effect from improved energy efficiency. The subject of this assessment was

chosen after extensive consultation with energy sector stakeholders and upon the

recommendation of the TPA Advisory Group, which is comprised of independent experts

from government, academia and the private sector. The assessment addresses the

following question:

What is the evidence that improvements in energy efficiency will lead to

economy-wide reductions in energy consumption?

The Summary Report seeks to present the main conclusions of this assessment in a

relatively non-technical manner. The results of the full assessment are contained in five

in-depth Technical Reports, as follows:

1. Evidence from evaluation studies

2. Evidence from econometric studies

3. Evidence from elasticity of substitution studies

4. Evidence from CGE modeling studies

5. Evidence from energy, productivity and economic growth studies

A shorter Supplementary Note provides a graphical analysis of rebound effects. All these

reports are available to download from the UKERC website at: www.ukerc.ac.uk

The assessment was led by the Sussex Energy Group (SEG) at the University of Sussex,

with contributions from the Surrey Energy Economics Centre (SEEC) at the University of

Surrey, the Department of Economics at the University of Strathclyde and Imperial

College. The assessment was overseen by a panel of experts and is extremely wide

ranging, reviewing more than 500 studies and reports from around the world.

The Rebound Effect: an assessment of the evidence for economy-wide energy savings from improved energy efficiency 

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Preface
The Rebound Effect: an assessment of the evidence for economy-wide energy savings from improved energy efficiency 

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The UK Energy Research Centre’s mission is to be the UK’s pre-eminent centre of research

and source of authoritative information and leadership on sustainable energy systems. It

undertakes world-class research addressing the whole-systems aspects of energy supply

and use while developing and maintaining the means to enable cohesive research in energy.

UKERC is funded by the UK Research Councils.

Acknowledgements

This report has been written by Steve Sorrell (Sussex Energy Group, University of Sussex),

but it is based upon the work of the full Project Team, which also includes: John

Dimitropoulos (Sussex Energy Group); Lester Hunt and David Broadstock (Surrey Energy

Economics Centre, University of Surrey); Grant Allan, Michelle Gilmartin, Peter McGregor,

Kim Swales and Karen Turner (Fraser of Allander Institute and Department of Economics,

University of Strathclyde); and Matt Sommerville and Dennis Anderson (ICEPT, Imperial

College).

John Dimitropoulos played a central role throughout the project, making an invaluable

contribution to all aspects of the research. Dennis Anderson provided two insightful papers

that greatly improved our understanding of the economy-wide rebound effect. The

remaining members of the Project Team co-authored one of the five Technical Reports that

provide the foundation for this report (see Annex 2). While aspects of all their work have

been incorporated into this report, individual members of the Project Team are not

responsible for its contents.

We have benefited enormously during this project from our interactions with Harry

Saunders (Decision Processes Inc). We are also grateful for the insightful comments

received from our three peer reviewers, namely Manuel Frondel (ZEW), Karsten Neuhoff

(University of Cambridge) and Jake Chapman. Valuable comments have also been received

from Nick Eyre (Energy Savings Trust), Terry Barker (4CMR), Serban Scrieciu (4CMR), Blake

Alcott, Len Brookes, John Feather and Gordon Mackerron, as well as from participants at

the 29th IAEE International Conference (2006). 

We are extremely grateful for the guidance, comments, patience and moral support offered

by Jim Skea (UKERC Research Director), Rob Gross (Head of UKERC TPA function), Dennis

Anderson (Senior Advisor, UKERC TPA) and Phil Heptonstall (UKERC TPA). Thanks also to

Phil for copy editing. 

Useful comments and advice have also been received from our Advisory Group, namely: Jim

Skea (UKERC); Horace Herring (Open University); Hunter Danskin (DEFRA); Tina Dallman

(DEFRA); Ken Double (Energy Saving Trust); Lester Hunt (Surrey Energy Economics

Centre); and Paolo Agnolucci (Policy Studies Institute). 

This project has tried to summarise the work of hundreds of economists and analysts, many

of whom understand these issues much better than ourselves. A particular debt is owed to

Lorna Greening and David Greene for their previous synthesis of empirical work in this area

(Greening and Greene, 1998). 

The above individuals represent a range of views about the size of the economy-wide

rebound effect and none of them are responsible for the content of this report.

The Rebound Effect: an assessment of the evidence for economy-wide energy savings from improved energy efficiency 

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About UKERC
The Rebound Effect: an assessment of the evidence for economy-wide energy savings from improved energy efficiency 

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Most governments are seeking ways to improve energy efficiency in pursuit of their energy

policy goals. The potential ‘energy savings’ from improved energy efficiency are commonly

estimated using basic physical principles and engineering models. However, the energy

savings that are realised in practice generally fall short of these engineering estimates.

One explanation is that improvements in energy efficiency encourage greater use of the

services (for example heat or mobility) which energy helps to provide. Behavioural

responses such as these have come to be known as the energy efficiency “rebound effect”.

While rebound effects vary widely in size, in some cases they may be sufficiently large to

lead to an overall increase in energy consumption - an outcome that has been termed

‘backfire’. There is some evidence to suggest that improvements in the energy efficiency

of certain ‘pervasive’ technologies such as steam engines and electric motors have

contributed to backfire in the past. 

Rebound effects are very difficult to quantify, and their size and importance under different

circumstances is hotly disputed. Also, rebound effects operate through a variety of

different mechanisms and lack of clarity about these has led to persistent confusion. In

general, rebound effects have been neglected when assessing the potential impact of

energy efficiency policies. A key conclusion of this report is that rebound effects are of

sufficient importance to merit explicit treatment. Failure to take account of rebound effects

could contribute to shortfalls in the achievement of energy and climate policy goals.

This report analyses the nature, operation and importance of rebound effects and provides

a comprehensive review of the available evidence on this topic, together with closely

related issues, such as the link between energy consumption and economic growth. It

assesses the strengths and weaknesses of the evidence base, clarifies the underlying

disputes and highlights the implications for energy and climate policy. The key message

is that promoting energy efficiency remains an effective way of reducing energy

consumption and carbon emissions. But more explicit treatment of rebound effects is

needed to assess the contribution that energy efficiency can realistically make. 

Defining the rebound effect

• Many energy efficiency improvements do not reduce energy consumption by the

amount predicted by simple engineering models. Such improvements make energy

services cheaper, so consumption of those services increases. For example, since fuelefficient vehicles make travel cheaper, consumers may choose to drive further and/or

more often, thereby offsetting some of the energy savings achieved. Similarly, if a

factory uses energy more efficiently it becomes more profitable encouraging further

investment and greater levels of output. This is termed the direct rebound effect.

• Even if consumption of energy services remains unchanged, there are reasons why

energy savings across the economy may be less than simple calculations suggest. For

example, drivers of fuel-efficient cars may spend the money saved buying petrol on

other energy-intensive goods and services, such as an overseas flight. Similarly, any

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Executive Summary
reductions in energy demand will translate into lower energy prices which encourage

increased energy consumption. These mechanisms are collectively known as indirect

rebound effects. The sum of direct and indirect rebound effects represents the

economy-wide rebound effect. Rebound effects are normally expressed as a

percentage of the expected energy savings from an energy efficiency improvement,

so a rebound effect of 20% means that only 80% of the expected energy savings are

achieved. 

• Disputes over the magnitude of rebound effects arise in part from lack of clarity about

definitions. Energy efficiency can be measured in a variety of ways, for example using

physical indicators (tonnes of coal per tonne of steel) or economic ones (energy per

unit of output measured in £). Energy efficiency can also be measured at different

levels, for example for an individual manufacturing process, a factory, a company, a

sector or even the economy as a whole. Estimates of the rebound effect therefore

depend upon the indicators chosen and the level of analysis. 

• Improvements in energy efficiency are often associated with improvements in the

productivity of capital, labour and materials. More efficient use of these other inputs

will tend to amplify the rebound effect. 

Estimating the rebound effect

• The available evidence for all types of rebound effect is far from comprehensive. The

evidence is better for direct effects than for indirect effects, but even this focuses on

a small number of consumer energy services, such as home heating and personal

transportation, within developed countries. Both direct and indirect effects appear to

vary widely between different technologies, sectors and income groups and in most

cases they cannot be quantified with much confidence. However the evidence does

not suggest that improvements in energy efficiency routinely lead to economy-wide

increases in energy consumption. At the same time the evidence suggests that

economy-wide rebound effects will be at least 10% and often higher. Rebound effects

therefore need to be factored into policy assessments.

• For household heating, household cooling and personal automotive transport in

developed countries, the direct rebound effect is likely to be less than 30% and may

be closer to 10% for transport. Direct rebound effects for these energy services are

likely to decline in the future as demand saturates. Improvements in energy efficiency

should therefore achieve 70% or more of the reduction in energy consumption

projected using engineering principles. However, indirect effects mean that the

economy-wide reduction in energy consumption will be less. 

• Direct rebound effects are likely to be smaller where energy forms a relatively small

proportion of total costs and has little influence on operating decisions. The use of

electricity in electronic appliances is a good example. However, these effects have only

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been studied over relatively limited time periods. There are also some important

uncertainties, for example about the link between direct rebound effects and

household income. 

• There are very few studies of rebound effects from energy efficiency improvements in

developing countries. Rebound effects may be expected to be larger in developing

countries where demand for energy services is far from saturated. This is supported

by the limited empirical evidence available. In some cases, the direct rebound effect

may exceed unity. 

• Energy-economic models can be used to estimate indirect and economy-wide rebound

effects, but there are few published studies and these have a number of flaws. The

results demonstrate that the economy-wide rebound effect varies widely depending

upon the sector where the energy efficiency improvement takes place. While little

confidence can be placed in the available estimates, several studies suggest that

economy-wide rebound effects may frequently exceed 50% (i.e. less than half of the

expected energy savings will be achieved). Moreover, these estimates do not take into

account the amplifying effect of any associated improvements in the efficiency with

which capital, labour or materials is used. 

Rebound or backfire?

• The so-called ‘Khazzoom-Brookes (K-B) postulate’ claims that, if energy prices do not

change, cost effective energy efficiency improvements will inevitably increase

economy-wide energy consumption above what it would be without those

improvements (‘backfire’). This provocative claim would have serious implications for

energy and climate policy if it were correct. However, the theoretical arguments in

favour of the postulate rely upon stylised models that have a number of limitations,

such as the assumption that economic resources are allocated efficiently. Similarly, the

empirical evidence for the postulate is indirect, suggestive and ambiguous. Since a

number of flaws have been found with both the theoretical and empirical evidence, the

K-B ‘hypothesis’ cannot be considered to have been verified. Nevertheless, the

arguments and evidence used to defend the postulate deserve more serious attention

than they have received to date.

• In developed countries, energy use as conventionally measured has grown more

slowly than the economy as a whole. From this, it is generally concluded that technical

change has improved the efficiency with which energy is used and thereby helped to

‘decouple’ energy consumption from economic growth. However once different energy

sources are weighted by their relative ‘quality’ or economic productivity, the coupling

between energy consumption and economic growth appears far stronger. Taken

together, the evidence reviewed in this report suggests that: a) the scope for

substituting other inputs for energy is relatively limited; b) much technical change has

historically increased energy intensity; c) energy may play a more important role in
economic growth than is conventionally assumed; and d) economy-wide rebound

effects may be larger than is conventionally assumed. 

• If improvements in energy efficiency are associated with improvements in the

efficiency with which other inputs such as capital, labour and materials are used, then

it is quite possible that energy consumption may increase as the K-B postulate

suggests. However, since the link between the efficiency with which energy and other

inputs is used depends on individual technologies and circumstances, there is no a

priori reason to believe that ‘backfire’ is an inevitable outcome in all cases.

• The debate over the K-B postulate would benefit from clearer distinctions between

different types of energy efficiency improvement. For example, the K-B postulate

seems more likely to hold for improvements associated with pervasive ‘generalpurpose technologies’, particularly when these are adopted by producers rather than

final consumers and when the improvements occur at an early stage of development

and diffusion. Steam engines provide a good illustration from the 19th-century, while

electric motors provide a comparable illustration from the early 20th century. The

opportunities offered by these technologies have such significant effects on

innovation, productivity and economic growth that economy-wide energy consumption

is increased over the long-term. Such technologies are generally taken up

enthusiastically by the market and are rarely associated with purposeful public policy. 

• In contrast, the K-B postulate seems less likely to hold for dedicated energy efficiency

technologies such as thermal insulation, particularly when these are used by

consumers or when they play a subsidiary role in economic production. These

technologies have smaller effects on productivity and economic growth, with the result

that economy-wide energy consumption is likely to be reduced.

Policy implications

1. The potential contribution of energy efficiency policies needs to be

reappraised. 

• Energy efficiency may be encouraged through policies that raise energy prices, such

as carbon taxes, or through non-price policies such as building regulations. Both

should continue to play an important role in energy and climate policy. However, many

official and independent appraisals of such policies have undoubtedly overstated the

contribution of non-price policies to reducing energy consumption and carbon

emissions. 

• It would be wrong to assume that, in the absence of evidence, rebound effects are so

small that they can be disregarded. Under some circumstances (e.g. energy efficient

technologies that significantly improve the productivity of energy intensive industries)

economy-wide rebound effects may exceed 50% and could potentially increase energy

consumption in the long-term. In other circumstances (e.g. energy efficiency

The Rebound Effect: an assessment of the evidence for economy-wide energy savings from improved energy efficiency 

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The Rebound Effect: an assessment of the evidence for economy-wide energy savings from improved energy efficiency 

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improvements in consumer electronic goods) economy-wide rebound effects are likely

to be smaller. But in no circumstances are they likely to be zero. 

• Taking rebound effects into account will reduce the apparent effectiveness of energy

efficiency policies. However, many energy efficiency opportunities are highly costeffective and will remain so even when rebound effects are allowed for. Provided

market and organisational failures can be overcome, the encouragement of these

opportunities should increase real income and contribute to economic growth. They

may not, however, reduce energy consumption and carbon emissions by as much as

previously assumed.

2. Rebound effects should be taken into account when developing and

targeting energy efficiency policy

• Rebound effects vary widely between different technologies, sectors and income

groups. While these differences cannot be quantified with much confidence, there

should be scope for including estimated effects within policy appraisals and using

these estimates to target policies more effectively. Where rebound effects are

expected to be large, there may be a greater need for policies that increase energy

prices.

• ‘Win-win’ opportunities that reduce capital and labour costs as well as energy costs

may be associated with large rebound effects. Hence, the implications of encouraging

these opportunities need to be clearly understood and quantified. It may make more

sense to focus policy on ‘dedicated’ energy efficient technologies, leaving the

realisation of wider benefits to the market

3. Rebound effects may be mitigated through carbon/energy pricing –

whether implemented through taxation or an emissions trading scheme 

• Carbon/energy pricing can reduce direct and indirect rebound effects by ensuring that

the cost of energy services remains relatively constant while energy efficiency

improves. Carbon/energy pricing needs to increase over time at a rate sufficient to

accommodate both income growth and rebound effects, simply to prevent carbon

emissions from increasing. It needs to increase more rapidly if emissions are to be

reduced. 

• Carbon/energy pricing may be insufficient on its own, since it will not overcome the

numerous barriers to the innovation and diffusion of low carbon technologies and

could have adverse impacts on income distribution and competitiveness. Similarly,

policies to address market barriers may be insufficient, since rebound effects could

offset much of the energy savings. A policy mix is required.
AEEI Autonomous Energy Efficiency Improvements

AES Allen-Urzwa Elasticity of Substitution

CES Constant Elasticity of Substitution

CGE Computable General Equilibrium

CPE Cross Price Elasticity

DEFRA Department for the Environment, Food and Rural Affairs

EBPP Evidence Based Policy and Practice

GDP Gross Domestic Product

GPT General Purpose Technologies

GWh Gigawatt Hour

IAEE International Association for Energy Economics

IEA International Energy Agency

K-B Khazzoom-Brookes

kW Kilowatt - a measure of power, one thousand W

kWh Kilowatt hours - a measure of energy, one kW of power provided for one hour

MES Morishima Elasticity of Substitution

MRTS Marginal Rate of Technical Substitution

OECD Organisation for Economic Cooperation and Development

SUV Sport Utility Vehicle

TFP Total Factor Productivity

TPA UKERC Technology and Policy Assessment function

UKERC UK Energy Research Centre

ZEW Zentrum für Europäische Wirtschaftsforschung, Mannheim

4CMR Cambridge Centre for Climate Change Mitigation Research

Glossary

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1. Introduction ....................................................................................... 1

1.1 Context and rationale .................................................................... 1

1.2 How the assessment was conducted ................................................ 6

1.3 Report structure ............................................................................ 9

2. What is energy efficiency?.................................................................. 11

2.1 Energy efficiency and energy savings............................................... 11

2.2 Multiple definitions ........................................................................ 12

2.3 Unacknowledged implications .......................................................... 15

2.4 Summary ..................................................................................... 17

3. The evidence for direct rebound effects ............................................. 19

3.1 Understanding the direct rebound effect ........................................... 19

3.2 Estimating direct rebound effects from evaluation studies................... 22

3.3 Estimating direct rebound effects from econometric studies ................ 26

3.4 Direct rebound effects for private automotive transport ...................... 30

3.5 Direct rebound effects for other consumer energy services ................. 32

3.6 Sources of bias in estimates of the direct rebound effect .................... 37

3.7 Summary ..................................................................................... 38

4. The evidence for indirect and economy-wide rebound effects ............ 41

4.1 Embodied energy – the limits to substitution .................................... 41

4.2 Secondary effects .......................................................................... 43

4.3 Evidence for limits to substitution .................................................... 45

4.4 Evidence for secondary effects ........................................................ 48

4.5 Evidence for economy-wide effects from general equilibrium models .... 51

4.6 Evidence for economy-wide effects from macro-econometric models .... 57

4.7 Summary ..................................................................................... 59

5. The evidence for ‘backfire’.................................................................. 61

5.1 Historical perspectives ................................................................... 62

5.2 Energy productivity and economic growth......................................... 64

5.3 Energy productivity and production theory........................................ 71

5.4 Substitution between energy and capital .......................................... 73

5.5 Energy productivity and ecological economics ................................... 77

5.6 Implications.................................................................................. 83

5.7 Summary ..................................................................................... 84

6. Conclusions, research needs and policy implications .......................... 87

6.1 Conclusions .................................................................................. 87

6.2 Research needs ............................................................................. 89

6.3 Policy implications ......................................................................... 92

References.................................................................................................. 95

Annex 1: Project team, expert group and contributors............................... 107

Annex 2: Technical and Supplementary Reports......................................... 108

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Contents
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The UKERC Technology and Policy

Assessment (TPA) function was set up to

address key controversies in the energy

field through comprehensive assessments

of the current state of knowledge. It aims

to provide authoritative reports that set

high standards for rigour and

transparency, while explaining results in a

way that is both accessible to nontechnical readers and useful to

policymakers. This latest report addresses

the following question:

What is the evidence that

improvements in energy efficiency

will lead to economy-wide reductions

in energy consumption?

1.1 Context and rationale

To achieve reductions in carbon

emissions, most governments are seeking

ways to improve energy efficiency

throughout the economy. It is generally

assumed that such improvements will

reduce overall energy consumption, at

least compared to a scenario in which

such improvements are not made. But a

range of mechanisms, commonly grouped

under the heading of rebound effects may

reduce the size of the ‘energy savings’

achieved. Indeed, there is some evidence

to suggest that the introduction of certain

types of energy efficient technology in the

past has contributed to an overall

increase in energy demand – an outcome

that has been termed ‘backfire’. This

appears to apply in particular to pervasive

new technologies, such as steam engines

in the 19th century, that raise overall

economic productivity as well as

improving energy efficiency.

These rebound effects could have farreaching implications for energy and

climate policy, both in the UK and globally.

While cost-effective improvements in

energy efficiency should improve welfare

and benefit the economy, they could in

some cases provide an ineffective or even

a counterproductive means of tackling

climate change. However, it does not

necessarily follow that all improvements

in energy efficiency will increase overall

energy consumption or in particular that

the improvements induced by policy

measures will do so.

The nature, operation and importance of

rebound effects are the focus of a longrunning dispute within energy economics.

On the micro level, the question is

whether improvements in the technical

efficiency of energy use can be expected

to reduce energy consumption by the

amount predicted by simple engineering

calculations. For example, will a 20%

improvement in the fuel efficiency of

passenger cars lead to a corresponding

20% reduction in motor-fuel consumption

for personal automotive travel? Economic

theory suggests that it will not. Since

energy efficiency improvements reduce

the marginal cost of energy services such

as travel, the consumption of those

services may be expected to increase. For

example, since the cost per mile of driving

is cheaper, consumers may choose to

drive further and/or more often. This

increased consumption of energy services

may be expected to offset some of the

predicted reduction in energy

consumption. 

This so-called direct rebound effect was

first brought to the attention of energy

economists by Daniel Khazzoom (1980)

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1. Introduction
and has since been the focus of much

research (Greening, et al., 2000). But

even if the direct rebound effect is zero for

a particular energy service (e.g. even if

consumers choose not to drive any further

in their fuel efficient car), there are a

number of other reasons why the

economy-wide reduction in energy

consumption may be less than simple

calculations suggest. For example, the

money saved on motor-fuel consumption

may be spent on other goods and services

that also require energy to provide. These

so-called indirect rebound effects can take

a number of forms that are briefly

outlined in Box 1.1. Both direct and

indirect rebound effects apply equally to

energy efficiency improvements by

consumers, such as the purchase of a

more fuel efficient car, and energy

efficiency improvements by producers,

such as the use of energy efficient motors

in machine tools. 

As shown in Box 1.2, the overall or

economy-wide rebound effect from an

energy efficiency improvement represents

the sum of these direct and indirect

effects. It is normally expressed as a

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• The equipment used to improve energy efficiency (e.g. thermal insulation) will itself

require energy to manufacture and install and this ‘embodied’ energy consumption

will offset some of the energy savings achieved. 

• Consumers may use the cost savings from energy efficiency improvements to

purchase other goods and services which themselves require energy to provide. For

example, the cost savings from a more energy efficient central heating system may

be put towards an overseas holiday. 

• Producers may use the cost savings from energy efficiency improvements to

increase output, thereby increasing consumption of capital, labour and materials

inputs which themselves require energy to provide. If the energy efficiency

improvements are sector wide, they may lead to lower product prices, increased

consumption of the relevant products and further increases in energy consumption.

• Cost-effective energy efficiency improvements will increase the overall productivity

of the economy, thereby encouraging economic growth. The increased consumption

of goods and services may in turn drive up energy consumption.

• Large-scale reductions in energy demand may translate into lower energy prices

which will encourage energy consumption to increase. The reduction in energy prices

will also increase real income, thereby encouraging investment and generating an

extra stimulus to aggregate output and energy use.

• Both the energy efficiency improvements and the associated reductions in energy

prices will reduce the price of energy intensive goods and services to a greater

extent than non-energy intensive goods and services, thereby encouraging

consumer demand to shift towards the former.

Box 1.1 Indirect rebound effects
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percentage of the expected energy

savings from an energy efficiency

improvement. Hence, a rebound effect of

100% means that the expected energy

savings are entirely offset, leading to zero

net savings.1

Rebound effects need to be defined in

relation to particular time frame (e.g.

short, medium or long term) and system

boundary (e.g. household, firm, sector,

national economy). The economy-wide

rebound effect is normally defined in

relation to a national economy, but there

may also be effects in other countries

through changes in trade patterns and

international energy prices. Rebound

effects may also be expected to increase

in importance over time as markets,

technology and behaviour adjusts. From a

climate change perspective, it is the longterm effect on global energy consumption

that is most relevant, but this is also the

effect that is hardest to estimate. 

The economy-wide rebound effect

represents the net effect of a number of

different mechanisms that are individually

complex, mutually interdependent and

likely to vary in importance from one type

of energy efficiency improvement to

another. Estimating the magnitude of the

economy-wide rebound effect in any

particular instance is therefore very

difficult. Nevertheless, the general

implication is that the energy ‘saved’ from

energy efficiency improvements will be

less than is conventionally assumed. 

The view that energy efficiency

improvements will increase rather than

reduce energy consumption was first put

forward by the British economist, William

Stanley Jevons, as long ago as 1865. It

has subsequently become known as the

‘Khazzoom-Brookes (K-B) postulate’, after

two contemporary economists (Len

Brookes and Daniel Khazzoom) who have

been closely associated with this idea.2 In

its original formulation, the K-B postulate3

states that:

‘with fixed real energy prices, energy

efficiency gains will increase energy

consumption above what it would be

without these gains’ (Saunders, 1992).

If it were correct, the K-B postulate would

have deeply troubling policy implications.

It would imply that many of the policies

used to promote energy efficiency may

neither reduce energy consumption nor

carbon emissions. The conventional

assumptions of energy analysts,

policymakers, business and lay people

alike would be turned on their head.

Alternatively, even if the postulate were

incorrect, the various mechanisms

described above could still make energy

efficiency policies less effective in

reducing energy consumption than is

commonly assumed. In either case, the

rebound effect could have important

implications for global efforts to address

climate change. But despite its potential

importance, the topic is widely neglected

and shrouded in controversy and

confusion.

1 This may be expressed as , where ENG represents the expected energy savings from a particular energy efficiency

improvement without taking rebound effects into account; DIR represents the increase in energy consumption resulting from the

direct rebound effect; and IND represents the increase in energy consumption resulting from the indirect rebound effects.

2 The ‘Jevons-Brookes postulate’ would be a more accurate term, since Khazzoom’s work focuses entirely on the direct rebound

effect. Brookes, in contrast, is a strong advocate of the postulate at a macroeconomic level. An alternative term is ‘Jevons

Paradox’ (Alcott, 2005).

3 The term postulate indicates a starting assumption from which other statements are logically derived. It does not have to be

self-evident or supported by empirical evidence. But since most commentators do not accept the K-B postulate, this assessment

treats it as a hypothesis and seeks out testable implications.
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The economy-wide rebound effect represents the sum of the direct and indirect effects.

For energy efficiency improvements by consumers, it is helpful to decompose the direct

rebound effect into:

a) a substitution effect, whereby consumption of the (cheaper) energy service

substitutes for the consumption of other goods and services while maintaining a

constant level of ‘utility’, or consumer satisfaction; and 

b) an income effect, whereby the increase in real income achieved by the energy

efficiency improvement allows a higher level of utility to be achieved by increasing

consumption of all goods and services, including the energy service. 

Similarly, the direct rebound effect for producers may be decomposed into:

a) a substitution effect, whereby the cheaper energy service substitutes for the use of

capital, labour and materials in producing a constant level of output; and 

b) an output effect, whereby the cost savings from the energy efficiency improvement

allows a higher level of output to be produced - thereby increasing consumption of

all inputs, including the energy service. 

It is also helpful to decompose the indirect rebound effect into:

a) the embodied energy, or indirect energy consumption required to achieve the

energy efficiency improvement, such as the energy required to produce and install

thermal insulation; and 

b) the secondary effects that result as a consequence of the energy efficiency

improvement, which include the mechanisms listed in Box 1.1.

A diagrammatic representation of this classification scheme is provided below (see also

the Supplementary Note). The relative size of each effect may vary widely from one

circumstance to another and in some cases individual components of the rebound effect

may be negative. For example,

if an energy service is an

‘inferior good’, the income

effect for consumers may lead

to reduced consumption of

that service, rather than

increased consumption. It is

theoretically possible for the

economy-wide rebound effect

to be negative (‘super

conservation’), although this

appears unlikely in practice.

Box 1.2 Classifying rebound effects

Secondary effects

Embodied energy

Income / output

effect

Substitution

effect

Actual energy savings

Indirect 

rebound 

effect

Direct

rebound

effect

Economywide

rebound 

effect

‘Engineering’

estimate of

energy

savings
The Rebound Effect: an assessment of the evidence for economy-wide energy savings from improved energy efficiency 

5

Rebound effects tend to be almost

universally ignored in official analyses of

the potential energy savings from energy

efficiency improvements. A rare exception

is UK policy to improve the thermal

insulation of households, where it is

expected that some of the benefits will be

taken as higher internal temperatures

rather than reduced energy consumption

(DEFRA, 2007). But the direct rebound

effects for other energy efficiency

measures are generally ignored, as are

the potential indirect effects for all

measures. Much the same applies to

energy modelling studies and to

independent estimates of energy

efficiency potentials by energy analysts.

For example, the Stern Review of the

economics of climate change overlooks

rebound effects altogether (Stern, 2007),

while the Fourth Assessment Report from

the Intergovernmental Panel on Climate

Change simply notes that the literature is

divided on the magnitude of this effect

(IPCC, 2007). Criticising this stance, the

House of Lords Select Committee on

Science and Technology commented that: 

“....the Khazzoom Brookes postulate,

while not proven, offers at least a

plausible explanation of why in recent

years improvements in ’energy intensity‘

at the macroeconomic level have

stubbornly refused to be translated into

reductions in overall energy demand. The

Government have so far failed to engage

with this fundamental issue, appearing to

rely instead on an analogy between

micro- and macroeconomic effects.” (HoL,

2006)4

While energy economists recognise that

direct and indirect rebound effects may

reduce the energy savings from energy

efficiency improvements, there is dispute

over how important these effects are -

both individually and in combination.

Some argue that rebound effects are of

minor importance for most energy

services, largely because the demand for

those services appears to be inelastic5 in

most cases and because energy typically

forms a small share of the total costs of

those services (Lovins, et al., 1988;

Lovins, 1998; Schipper and Grubb,

2000).6 Others argue that they are

sufficiently important to completely offset

the energy savings from improved energy

efficiency (Brookes, 2000; Herring, 2006).

At first sight, it appears odd for competent

and experienced analysts to hold such

widely diverging views on what appears to

be an empirical question. This suggests

one of three things: first, different authors

may be using different definitions of the

rebound effect, together with different

definitions of associated issues such as

the relevant system boundaries; second,

the empirical evidence for rebound effects

may be sufficiently sparse, ambiguous

and inconclusive to be open to widely

varying interpretations; or third,

fundamental assumptions regarding how

the economy operates may be in dispute. 

4 This is strictly incorrect. First, the K-B postulate could explain a relatively slow rate of improvement in energy intensity; and

second, to reduce overall energy demand the rate of improvement would need to be greater than the rate of GDP growth. 

5 The own-price elasticity of demand for a good or service is the ratio of the relative (or percentage) change in quantity

demanded to the relative change in price, holding other factors constant. In most cases, the own-price elasticity is negative.

Demand is said to be inelastic when the own-price elasticity is less than one in absolute value, and elastic when it is greater

than one.

6 The price of a service depends upon the total cost of providing that service, which includes capital, labour, materials and energy

costs. If energy forms a small proportion of total costs, the elasticity of demand with respect to energy prices should be less

than the own-price elasticity of demand with respect to total costs.
In practice, all of these factors appear to

be relevant. The empirical evidence for

direct rebound effects is very patchy,

mainly focused on a limited number of

consumer energy services such as

personal automotive transport and almost

wholly confined to OECD economies.

Quantitative estimates of indirect and

economy-wide rebound effects are rare,

leaving authors such as Brookes (2000) to

rely on an eclectic mix of theoretical

argument, anecdotal examples and

‘suggestive’ evidence from econometric

analysis and economic history. Much of

this evidence rests upon theoretical

assumptions and methodological

approaches that are both highly technical

and openly contested. There is also

confusion, misunderstanding and

disagreement over basic definitional

issues such as the meaning of

‘improvements in energy efficiency’. The

result is that commentators talk past one

another, with disagreements originating in

part because different people are talking

about different things. For example, some

commentators appear to equate ‘the’

rebound effect solely with the direct

effect, and thereby ignore the indirect

effects that are the primary concern of

economists such as Jevons. In particular,

there appears to be a divergence between

the world views of ‘economists’ and

‘engineers’:

“For the economist the world is an open

field in which different factors of

production and different consumption

goods have to be constantly re-combined

in the most profitable fashion in response

to their prices and marginal productivities.

An efficiency increase in one factor

immediately leads to a complete rearrangement due to implicit price changes

and hence to a large rebound

effect......For the engineer, instead, the

world consists of a set of given

technologies or activities, which

determine demand in which relative

shares are fixed. Any increase in the

productivity of one factor affects only that

factor and hence there is no rebound

effect.” (Birol and Keppler, 2000)

Rebound effects could therefore be of

considerable importance, but they are

widely ignored by policy makers and their

magnitude is greatly disputed, even

among economists who understand the

mechanisms involved. An assessment of

the state of knowledge in this area should

therefore make a valuable contribution to

contemporary policy debates. At the same

time, the diversity and ambiguity of the

evidence base makes such an assessment

challenging to conduct.

1.2 How the assessment

was conducted

This assessment is one in a series being

carried out by the Technology and Policy

Assessment (TPA) function of the UK

Energy Research Centre (UKERC). The

selection of the rebound effect as a topic

followed wide ranging consultation with

stakeholders, together with advice from

the TPA Advisory Group. The Group noted

the persistence of controversy about the

rebound effect, the existence of widely

diverging views on the topic and the

mismatch between the potential

importance of the topic and the

apparently limited research devoted to it.

It also emphasised the importance of

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The Rebound Effect: an assessment of the evidence for economy-wide energy savings from improved energy efficiency 

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addressing rebound effects ‘in the round’,

including the indirect and economy-wide

effects. This led the UKERC to undertake

this study.

The objective of this assessment is not to

undertake new research on rebound

effects. Instead, it is to provide a

thorough review of the current state of

knowledge on this issue and to explain the

findings in an accessible manner. The

general approach was informed by the

systematic review techniques prominent

in medicine and other fields (see Box 1.3).

Following this model, the assessment

began with a Scoping Note that clarified

the definition and nature of the rebound

effect, identified the size and nature of the

evidence base and set out the options for

synthesising this evidence (Sorrell and

Dimitropoulos, 2005b). The diversity of

the evidence base and the extent to which

it is embedded in complex and contested

theoretical issues was found to make the

conventional systematic review

techniques difficult to apply – a feature

that is common to many policy-relevant

questions in the energy field (Sorrell,

2007).

An Advisory Group for the project was

established and the Scoping Note

circulated to key stakeholders. This led to

a series of recommendations on the

appropriate scope and focus of the

assessment, including the relative weight

to be given to different sources of

evidence. The agreed approach was then

set out in an Assessment Protocol (Sorrell

and Dimitropoulos, 2005a) which clarified

that the assessment should update and

The TPA approach to assessment seeks to learn from a range of techniques referred to

as Evidence Based Policy and Practice (EBPP), including meta-analyses of quantitative

data and systematic reviews of quantitative and qualitative evidence. These aspire to

provide more robust evidence for policymakers and practitioners, avoid duplication of

research, encourage higher research standards and identify research gaps. However,

energy policy presents a number of challenges for the application of systematic review

techniques and the approach has been criticised for excessive methodological rigidity in

some policy areas (Sorrell, 2007). The UKERC has therefore set up a process that is

inspired by these approaches, but is not bound to any narrowly defined method or

technique.

The process carried out for each assessment includes the following components:

• Publication of Scoping Note and Assessment Protocol.

• Establishment of a project team with a diversity of expertise.

• Convening an Expert Group with a diversity of opinion and perspective.

• Stakeholder consultation.

• Systematic searches of the the evidence base.

• Categorisation and assessment of evidence.

• Synthesis, review and drafting.

• Expert feedback on initial drafts.

• Peer review of final draft.

Box 1.3 Overview of the TPA approach
extend a previous literature review by

Greening et al. (2000), place greater

emphasis on indirect and economy-wide

effects and focus in particular on clarifying

the underlying conceptual frameworks

and identifying the reasons for the widely

diverging views. Since disputes over the

rebound effect involve some highly

technical concepts from economic theory

and econometric practice, much emphasis

has been placed on clarifying these

concepts and making them accessible to a

non-technical audience.

The assessment was organised around

five broad categories of evidence, as

follows:

• Evaluation studies: micro-level

evaluations of the impact of specific

energy efficiency improvements on

the demand for energy or energy

services;

• Econometric studies: use of

secondary data sources to estimate

the elasticity of the demand for

energy or energy services at different

levels of aggregation;

• Elasticity of substitution studies:

estimates of the elasticity of

substitution between energy and

capital at different levels of

aggregation;

• Computable general equilibrium

modelling studies: estimates of

economy-wide rebound effects from

computable general equilibrium (CGE)

models of the macroeconomy; and

• Energy, productivity and economic

growth studies: a range of evidence

considered in some way relevant to

the K-B postulate, including studies

from economic history, neoclassical

production theory, neoclassical growth

theory, ecological economics,

decomposition analysis, and inputoutput analysis.

The first two categories of evidence relate

to direct rebound effects while the

remainder relate to economy-wide

rebound effects. The last category is

especially diverse and has received the

greatest amount of attention. While the

project began with systematic literature

searches using keyword such as ‘rebound

effect’, the final assessment incorporates

a much broader range of evidence,

reviewing more than 500 studies from

around the world. The full results of the

assessment are reported in five in-depth

Technical Reports, namely:

• Technical Report 1: Evidence from

evaluation studies

• Technical Report 2: Evidence from

econometric studies

• Technical Report 3: Evidence from

elasticity of substitution studies

• Technical Report 4: Evidence from

CGE modeling studies

• Technical Report 5: Evidence from

energy, productivity and economic

growth studies

In addition, a shorter Supplementary Note

provides a graphical analysis of rebound

effects. All these reports are available to

download from the UKERC website. 

The present report summarises the main

conclusions of the assessment and

identifies policy implications and priorities

for further research.

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1.3 Report structure

The report is structured as follows:

Section 2 describes the different

definitions of energy efficiency, the

choices available for the independent and

dependent variables for the rebound

effect and the implications of those

choices for the estimated magnitude of

the effect. It is argued that disputes over

the magnitude of rebound effects result in

part from confusion over these basic

definitions.

Section 3 describes the nature and

operation of direct rebound effects and

summarises the evidence regarding the

magnitude of these effects for a number

of consumer energy services. It concludes

that direct rebound effects appear to be

low to moderate (i.e. <30%) for most

consumer energy services in developed

countries. Direct rebound effects may be

larger in developing countries and for

energy efficiency improvements by

producers, but the empirical evidence for

both is weak.

Section 4 describes the nature and

operation of indirect and economy-wide

rebound effects and summarises the

results of a number of studies that provide

quantitative estimates of these effects.

This evidence base is both small and

subject to a number of important

methodological weaknesses, making it

difficult to draw any general conclusions.

However, the available studies suggest

that economy-wide rebound effects may

frequently exceed 50%.

Section 5 summarises the evidence for

the K-B postulate, focusing in particular

on the work of W.S. Jevons, Len Brookes

and Harry Saunders. Instead of providing

quantitative estimates of rebound effects,

the evidence discussed in this section

provides indirect support for the K-B

postulate and is taken from such diverse

fields as neoclassical growth theory,

economic history and econometric

analysis of productivity trends. A core

argument of this section is that the case

for the K-B postulate hinges on the claim

that energy plays a more important role in

economic growth than is conventionally

assumed. Much of the discussion

therefore focuses on this broader issue

and compares conventional views with the

alternative perspective of ecological

economics. It is argued that satisfactory

resolution of the debate over the K-B

postulate may hinge in part on a

satisfactory resolution of this much

broader question. 

Finally, Section 6 summarises the key

conclusions from the assessment,

identifies the main research needs and

highlights some important policy

implications.
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2. What is energy efficiency?

This section introduces the different

definitions of energy efficiency and the

different ways of measuring energy inputs

and useful outputs. It argues that these

definitional issues have important but

generally unacknowledged implications

for the estimated magnitude of rebound

effects. 

2.1 Energy efficiency and

energy savings

Energy efficiency improvements are

generally assumed to reduce energy

consumption below where it would have

been without those improvements. The

rebound effect may reduce the size of

these energy savings (Box 1.2). However,

estimating the size of any ‘energy savings’

is far from straightforward, since:

• real-world economies do not permit

controlled experiments, so the

relationship between a change in

energy efficiency and a subsequent

change in energy consumption is

likely to be mediated by a host of

confounding variables;

• we can’t observe what energy

consumption ‘would have been’

without the energy efficiency

improvement (the so-called

‘counterfactual’ scenario), so the

estimated ‘savings’ from energy

efficiency improvement will always be

uncertain; and

• energy efficiency is not controlled

externally by an experimenter and

may be influenced by a variety of

technical, economic and policy

variables. In particular, the direction

of causality may run in reverse - with

changes in energy consumption

(whatever their cause) leading to

changes in energy efficiency. 

Energy efficiency may be defined as the

ratio of ‘useful’ outputs to energy inputs

for a system. The system in question may

be an individual energy conversion device

(e.g. a boiler), a building, an industrial

process, a firm, a sector or an entire

economy. In all cases, the measure of

energy efficiency will depend upon how

‘useful’ is defined and how inputs and

outputs are measured. When outputs are

measured in thermodynamic or physical

terms, the term energy efficiency tends to

be used, but when outputs are measured

in economic terms it is more common to

use the term ‘energy productivity’. The

inverse of both measures is termed

‘energy intensity’.

Improvements in energy efficiency may

reduce the energy used by that system.

For example, the installation of a

condensing boiler may reduce the amount

of gas used to heat a house. But a full

accounting of rebound effects requires

attention to be paid to the consequences

for energy consumption within broader

systems, such as the economy as a whole.

For example, the money saved on heating

costs may be spent on other goods and

services which also require energy to

provide.

The definition of inputs and outputs, the

appropriate system boundaries for

measures of energy efficiency and energy

consumption and the timeframe under

consideration can vary widely from one

study to another. The conclusions drawn

regarding the magnitude and importance

of the rebound effect are likely to depend

upon the particular choices that are made.
2.2 Multiple definitions

The most basic definition of energy

efficiency derives from the first-law of

thermodynamics and measures the ratio

of ‘useful’ energy outputs to the heat

content, or calorific value of fuel inputs. A

conventional lightbulb, for example, has a

first-law efficiency of only 6%, since 6%

of the heat content of energy inputs are

converted to light energy and the

remainder is lost as ‘waste’ heat (Berndt,

1978; Patterson, 1996). But the first-law

efficiency of a process depends upon how

‘useful’ is defined. When waste heat and

other losses are taken into account, the

first-law efficiency becomes 100%, since

energy is not ‘used up’ but is merely

transformed from ‘available’ to less

available forms. 

Although widely used, first-law efficiency

measures are misleading because they do

not take into account the availability of

energy inputs and outputs, or their ability

to perform useful work7 (Berndt, 1978).

For example, energy in the form of highpressure steam can perform more useful

work than the same amount of energy in

the form of low temperature heat. The

thermodynamic concept of exergy

provides a general measure of the ability

to perform useful work and may be

applied to both the inputs and outputs of

conversion processes (Wall, 2004). The

exergy content of an energy carrier may

therefore be different from its heat

content, although both are measured in

kWh (kilowatt hours). For example, a heat

unit of electricity will be ranked higher on

exergy basis than a heat unit of oil or

natural gas. Also, unlike energy, exergy is

‘consumed’ in conversion processes. 

The notion of exergy leads to a second

definition of energy efficiency, based upon

the second law of thermodynamics. This

‘second-law’ measure is frequently

smaller than the first-law efficiency,

suggesting a greater potential for

improvement. For example, the first-law

efficiency of electric resistance space

heating may exceed 99%, but this falls to

around 5% when a second-law definition

is used (Rosen, 2004). The difference

arises because resistance heating

converts high exergy electricity to low

exergy space heat. Second-law measures

are preferable since they focus attention

what needs to be conserved - namely

exergy rather than energy per se (Berndt,

1978). 

For most purposes, it is simpler to

measure useful energy outputs in terms of

physical indicators for the relevant energy

service, rather than heat content or

exergy. For example, a suitable output

measure for personal transportation by

private car could be vehicle kilometres. A

physical measure of energy efficiency

would then be vehicle kilometres per litre

of motor fuel. 

Physical measures may be applied at the

level of individual energy conversion

devices, but are more commonly applied

at higher levels of aggregation, such as a

household, an industrial process, an

individual firm or an individual sector. In

each case, changes in physical measures

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7 ‘Work’ may be broadly defined as an increase in the kinetic, potential, physical or chemical energy of a subsystem that is

located within a larger system in which energy - according to the first-law - is always conserved (Ayres, et al., 2003).
The Rebound Effect: an assessment of the evidence for economy-wide energy savings from improved energy efficiency 

13

may result from factors other than

improvements in the thermodynamic

efficiency of conversion devices. For

example, changes in vehicle load factors

will change a physical measure of energy

efficiency that is based upon passenger

kilometres per litre of motor-fuel.

Appropriate physical indicators are likely

to vary from one sector to another and

one type of energy service to another,

making an aggregate economy-wide

physical measure of energy efficiency

inappropriate. 

By replacing the numerator with an

indicator of the economic value of output,

the energy efficiency of different sectors

can be compared (Patterson, 1996). For

example, the energy efficiency of both the

brewing and dairy sectors can be

measured in terms of value added per

Gigawatt hours (GWh) of energy input.

The move from physical to economic

indicators increases the number of factors

that could influence the indicator as does

the use of such indicators at higher levels

of aggregation. The indicator that is

furthest from a thermodynamic measure

of energy efficiency is therefore the ratio

of GDP to total primary energy

consumption within a national economy. 

Hence, when using the terms energy

efficiency and energy productivity, it is

essential to clarify how the inputs and

outputs are being measured. In what

follows, the term useful work will be used

in a generic sense to refer to the useful

outputs of energy conversion systems. 

Whichever measure is used, maximising

energy efficiency is an inappropriate goal

since it does not take into account the

costs associated with other inputs such as

capital and labour. Economists are

therefore more concerned with improving

total factor productivity (TFP), which is a

measure of the efficiency with which all

inputs are used by a firm, sector or

national economy. 

The energy efficiency improvements that

are of most relevance to the rebound

effect are those that are consistent with

the best use of all economic resources.

These are conventionally divided into two

categories: those that are associated with

improvements in total factor productivity

(‘technical change’), and those that are

not (‘substitution’). The former are

conventionally assumed to occur

independently of changes in relative

prices, while the latter are assumed to

occur in response to such changes (Box

2.1). This distinction is potentially

misleading, however, since it assumes

that the producer is making fully efficient

use of all relevant inputs. Many energy

efficiency improvements may be better

described as ‘overcoming inefficiency’,

since they represent the use of existing

technologies to move closer to an

‘optimal’ combination of inputs (Box 2.1).

This type of improvement also improves

total factor productivity and may

frequently be stimulated through nonprice energy efficiency policies.

Rebound effects may be defined in

relation to each type of energy efficiency

improvement, although most studies

classify such improvements as deriving

from either substitution or technical

change. The consequences of technical

change are of particular interest, since

this contributes to the growth in economic

output. Also, many (if not most) energy

efficiency improvements are likely to be
The Rebound Effect: an assessment of the evidence for economy-wide energy savings from improved energy efficiency 

14

Increases in energy prices may lead to improvements in energy efficiency (or energy

productivity) of a system through the substitution of capital or labour inputs for energy.

For example, thermal insulation may reduce the consumption of gas for space heating.

But if the prices of other inputs are unchanged the total cost of producing a given level

of output will have increased. In contrast, improvements in both energy productivity and

total factor productivity may result from technical change. This is conventionally

assumed to occur independently of any change in relative prices and is considered

desirable since it occurs without any reduction in economic output.

In neoclassical economics, a production function represents the maximum possible flow

of output (Y) obtainable from the flow of energy (E) and other inputs (X), given the

current state of technology (see diagram). An ‘isoquant’ of a production function

represents the different possible combination of inputs that may be used to produce a

given level of output. The ‘optimal’ combination depends upon the relative prices of

those inputs. Substitution may be represented a movement along an isoquant in

response to a change in relative prices. This may require investment in technologies that

can combine inputs in

different ways (e.g. energyefficient motors), but these

are assumed to be chosen

from a set of existing

technologies. In contrast,

technical change refers to the

development of new

technologies and methods of

organisation that shift the

isoquant to the left, allowing

the same level of output to be

produced from a lower level

of inputs. 

In practice, the conventional distinction between substitution and technical change can

be misleading. First, the notion of substitution implies a ‘frictionless’ move from one

existing technique to another, but in practice this requires investment and will take time.

Second, technical change is not autonomous but is influenced by changes in relative

prices and much contemporary research is concerned with improving understanding of

this process (Grubb, et al., 2002). Third, the production function represents the most

efficient combination of factor inputs, but in practice firms may use relatively inefficient

combinations. Much investment may be better represented as a move from less efficient

to more efficient combinations, while still remaining within the production function

‘frontier’ - indicated by the ‘overcoming inefficiency’ arrow in the diagram. 

Box 2.1 Types of energy efficiency improvement

Energy - E

Other inputs - X

Overcoming inefficiency

Substitution

Technical

change

Y

Y
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15

the by-product of attempts to improve

total factor productivity, rather than the

result of targeted efforts to improve

energy efficiency. 

Technical change is said to be ‘neutral’ if it

reduces the use of all inputs by an equal

amount and ‘biased’, if it reduces the use

of some inputs more than others. ‘Energysaving’ technical change reduces the

share of energy in the value of output by

proportionately more than the share of

other inputs, while ‘energy-using’

technical change does the reverse. This

bias in technical change is closely related

to (but not the same as) the rate of

growth of energy efficiency over time,

holding relative prices constant, which is

an important parameter in many energyeconomic models (Löschel, 2002;

Sanstad, et al., 2006).

2.3 Unacknowledged

implications

The importance of these definitional

issues for estimates of the rebound effect

is not often recognised. Many

commentators assume that the relevant

independent variable for the rebound

effect is improvements in the

thermodynamic efficiency of individual

conversion devices or industrial

processes. But such improvements will

only translate into comparable

improvements in different measures of

energy efficiency, or measures of energy

efficiency applicable to wider system

boundaries, if several of the mechanisms

responsible for the rebound effect fail to

come into play. For example,

improvements in the number of litres

used per vehicle kilometre will only

translate into improvements in the

number of litres used per passenger

kilometre if there are no associated

changes in average vehicle load factors. 

Rebound effects may be expected to

increase over time and with the widening

of the system boundary for the dependent

variable (energy consumption). For

example, the energy savings for

manufacturing as a whole may be

expected to be less than the energy

savings for an individual firm that invests

in an energy efficient technology. For the

K-B postulate the relevant system

boundary is normally taken as the

national economy. But energy efficiency

improvements may also affect trade

patterns and international energy prices,

thereby changing energy consumption in

other countries. For the purpose of

assessing the contribution of energy

efficiency to reducing carbon emissions,

the relevant system boundary is the whole

world. To capture the full range of

rebound effects, the system boundary for

the independent variable (energy

efficiency) should be relatively narrow,

while the system boundary for the

dependent variable (energy consumption)

should be as wide as possible. For

example, the independent variable could

be the energy efficiency of an electric

motor, while the dependent variable could

be economy-wide energy consumption.

However, measuring or estimating the

economy-wide effects of such micro-level

changes effects is, at best, challenging.

For this reason, the independent variable

for many theoretical and empirical studies

of rebound effects is a physical or

economic measure of energy efficiency
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16

that is applicable to relatively wide system

boundaries – such as the energy efficiency

of an industrial sector. But such studies

may overlook the ‘lower-level’ rebound

effects resulting from improvements in

physical or thermodynamic measures of

energy efficiency appropriate to narrower

system boundaries. For example,

improvements in the energy efficiency of

electric motors in the engineering sector

may lead to rebound effects within that

sector, with the result that the energy

intensity of that sector is reduced by less

than it would be in the absence of such

effects. But if the energy intensity of the

sector is taken as the independent

variable, these lower-level rebound

effects will be overlooked. Also,

improvements in more aggregate

measures of energy efficiency are unlikely

to be caused solely (or even mainly) by

the diffusion of more thermodynamically

efficient conversion devices. 

Aggregate measures of energy efficiency

will depend upon how different types of

energy input are combined. The most

common approach is to aggregate

different energy types according to their

heat content, but this neglects the

‘quality’ of each energy type, or the ability

to perform useful work. The latter, in turn,

is only one of several factors that

determine the economic productivity of

different energy types, with others

including cleanliness, ameanability to

storage, safety, flexibility of use and so

on, together with the use to which the

energy is put (Cleveland, et al., 2000). In

the absence of significant market

distortions, the relative price per kilowatt

hour of different energy carriers can

provide a broad indication of their relative

marginal productivities (Kaufmann,

1994).8

Using data on relative prices, economists

have devised methods for aggregating

energy types of different marginal

productivities (Berndt, 1978). In general,

when the ‘quality’ of energy inputs are

accounted for, aggregate measures of

energy efficiency are found to be

improving more slowly than is commonly

supposed (Hong, 1983; Zarnikau, 1999;

Cleveland, et al., 2000). For example, on

a thermal input basis, per-capita energy

consumption in the US residential sector

decreased by 20% over the period 1970

to 1991, but when adjustments are made

for changes in energy quality (notably the

increasing use of electricity), per capita

energy consumption is found to have

increased by 7% (Zarnikau, et al., 1996).

This difference demonstrates that

technical progress in energy use is not

confined to improvements in

thermodynamic efficiency, but also

includes the substitution of low quality

fuels by high quality fuels (notably

electricity), thereby increasing the

amount of utility or economic output

obtained from the same heat content of

input (Kaufmann, 1992). 

Failure to allow for this can lead to

misleading conclusions. For example, it is

commonly assumed that a combination of

structural change and ‘energy efficiency’

improvements have allowed OECD

countries to decouple GDP growth from

the growth in primary energy

8 The marginal product of an energy input into a production process is the marginal increase in the value of output produced by

the use of one additional heat unit of energy input. 
The Rebound Effect: an assessment of the evidence for economy-wide energy savings from improved energy efficiency 

17

consumption (Geller, et al., 2006).9

However, if energy inputs are weighted by

their relative marginal productivities, the

growth in energy consumption is shown to

be closely coupled to the growth in GDP

(Hong, 1983; Stern, 1993; Cleveland, et

al., 2000; Stern and Cleveland, 2004).

Hence, not only may such ‘decoupling’ be

partly illusory, but the contribution of

improved thermodynamic efficiency to

any decoupling may easily be overstated.

Both fuel switching and improvements in

the thermodynamic efficiency of

conversion devices may be expected to

change aggregate measures of energy

efficiency, but the rebound effects

associated with each may be different.

Also, improvements in any measure of

energy efficiency rarely occur in isolation

but are typically associated with broader

improvements in the productivity of other

inputs, with new technologies frequently

providing both. Pye and McKane (1998),

for example, show how the installation of

energy efficient motors can reduce wear

and tear, extend the lifetime of system

components and achieve savings in

capital and labour costs that exceed the

reduction in energy costs. Hence, if the

full impact on energy consumption of a

new energy-efficient technology is taken

as the appropriate dependent variable,

then the estimated (direct, indirect or

economy-wide) rebound effect may be

larger. Conversely, if only a portion of this

impact is attributed specifically to the

energy efficiency improvement, then the

estimated rebound effect may be smaller.

However, it is both difficult and misleading

to isolate the impact on energy demand of

an improvement in energy efficiency

alone. What matters for policy is how a

new, energy-efficient technology affects

overall energy demand. This frequently

overlooked point is a major theme of this

assessment.

2.4 Summary

• Energy efficiency may be measured in

a variety of ways for a variety of

system boundaries and any one of

these may form an appropriate

independent variable for an estimate

of the rebound effect. While the

appropriate choice depends upon the

objectives of the study, difficulties

may arise if commentators interpret

the term ‘energy efficiency’ in

different ways.

• The dependent variable for the

rebound effect is a change in energy

consumption, which may also be

measured for a variety of system

boundaries. However, wider system

boundaries allow a greater range of

rebound effects to be captured.

• Many theoretical and empirical studies

of rebound effects employ physical or

economic measures of energy

efficiency applicable to relatively wide

system boundaries. But these may

overlook the rebound effects resulting

from improvements in physical or

thermodynamic measures of energy

efficiency appropriate to narrower

system boundaries.

9 If energy is measured in terms of heat content, major OECD countries used one third less primary energy to generate a unit

of GDP than in the early 1970s (Geller, et al., 2006).
• Many theoretical and empirical studies

aggregate different types of energy

carrier on the basis of their thermal

content, thereby neglecting

differences in energy quality. This may

lead such studies to overlook the

contribution of changes in fuel mix to

changes in aggregate measures of

energy efficiency. If energy inputs are

weighted by their relative marginal

productivities, the historical growth in

energy consumption appears to be

closely coupled to the growth in GDP.

• Economists are primarily interested in

energy efficiency improvements that

are consistent with the best use of all

economic resources. These are

commonly categorised as either priceinduced substitution or technical

change, but this neglects the

importance of organisational and

market failures in contributing to

inefficiencies as well as the role of

relative prices in stimulating technical

change.

• Energy efficiency improvements by

producers may often be associated

with improvements in the productivity

of capital, labour and materials

inputs. Similarly, energy efficiency

improvements by consumers may

often save costs on more than energy

alone. This needs to be allowed for

when estimating rebound effects, as

does any associated changes in fuel

mix. Failure to do so may lead to

rebound effects being underestimated

and to the potential for decoupling

between energy consumption and

economic output to be overestimated.

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18
This section summarises the empirical

evidence for direct rebound effects,

focusing in particular on energy services

in the household sector, since this is

where the bulk of the research has been

undertaken. A full examination of this

evidence is contained in Technical Report

1 and Technical Report 2. 

Section 3.1 describes the operation of the

direct rebound effect, highlighting some

key issues concerning the measurement

of this effect and the conditions under

which it may be expected to be larger or

smaller. The Supplementary Note contains

a simple graphical analysis of rebound

effects that complements the discussion

in this section. 

Section 3.2 describes one approach to

estimating direct rebound effects that use

quasi-experimental methodologies

adopted from the field of policy

evaluation. It then summarises the results

of a limited number of studies that use

this approach to estimate direct rebound

effects from energy efficiency

improvements in space heating.

Section 3.3 describes an alternative and

more commonly used approach to

estimating direct rebound effects that

uses econometric analysis of secondary

data sources to estimate the elasticity of

demand for either useful work or energy

consumption. Sections 3.4 and 3.5

summarise the results of a number of

studies that use this approach to estimate

direct rebound effects for personal

automotive transport, household heating

and a limited number of other consumer

energy services. Section 3.6 discusses a

number of potential sources of bias with

this approach that may lead the direct

rebound effect to be overestimated.

Section 3.7 concludes.

3.1 Understanding the

direct rebound effect

Direct rebound effects relate to individual

energy services, such as heating, lighting

and refrigeration and are confined to the

energy required to provide that service.

Improved energy efficiency will decrease

the marginal cost of supplying that service

and should therefore lead to an increase

in consumption of the service. For

example, consumers may choose to drive

further following the purchase of energy

efficient car because the price per

kilometre has fallen. The resulting

increase in energy service consumption

will tend to offset the expected reduction

in energy consumption provided by the

energy efficiency improvement. 

As shown in Box 1.2, the direct rebound

effect for consumers may be decomposed

into a substitution effect, whereby

consumption of the (cheaper) energy

service substitutes for the consumption of

other goods and services while

maintaining a constant level of ‘utility’;

and an income effect, whereby the

increase in real income achieved by the

energy efficiency improvement allows

increased consumption of the energy

service. In a similar manner, the direct

rebound effect for producers may be

decomposed into a substitution effect and

an output effect (see Supplementary

Note). Also, for most energy services, the

direct rebound effect may be expected to

increase over time as markets,

technologies and behaviours adjust. For

example, energy efficiency improvements

may lower production costs for a firm, but

it may take time for the firm to increase

output and market share.

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3. The evidence for direct rebound effects
Energy services are provided through a

combination of capital equipment, labour,

materials and energy. An essential feature

of an energy service is the useful work

obtained which, as shown in Section 2,

may be measured by a variety of ways.

For example, the useful work from

passenger cars may be measured in

vehicle kilometres, passenger kilometres

or (rather unconventionally) in tonne

kilometres. Energy services may also

have broader attributes that may be

combined with useful work in a variety of

ways. For example, all cars deliver

passenger kilometres, but they may vary

widely in terms of features such as speed,

comfort, acceleration and prestige.

Consumers and producers may therefore

make trade-offs between useful work and

other attributes of an energy service;

between energy, capital and other market

goods in the production of an energy

service; and between different types of

energy service.

By reducing the marginal cost of useful

work, energy efficiency improvements

may, over time, lead to an increase in the

number of energy conversion devices,

their average size, their average

utilisation and/or their average load

factor. For example, people may buy more

cars, buy larger cars, drive them further

and/or share them less. Similarly, people

may buy more washing machines, buy

larger machines, use them more

frequently and/or reduce the size of the

average load. The relative importance of

these variables may be expected to vary

widely between different energy services

and over time. For example, technological

improvements in the energy efficiency of

new refrigerators are unlikely to increase

the average utilisation of the refrigerator

stock (measured in hours/year) but could

lead to a long-term increase in both the

number of refrigerators and their average

size (since the cost per cubic metre of

refrigeration has fallen). Over the very

long-term, the lower cost of energy

services may contribute to fundamental

changes in technologies, infrastructures

and lifestyles - such as a shift towards

car-based commuting and increasing

distances between residential, workplace

and retail locations. But as the time

horizon extends, the effect of such

changes on the demand for the energy

service (as well as the demand for other

goods and services) becomes increasingly

difficult to separate from the effect of

income growth and other factors.

The estimated size of the direct rebound

effect depends upon how useful work and

hence energy efficiency is defined. For

example, the majority of estimates of the

direct rebound effect for personal

automotive transport measure useful

work in terms of vehicle kilometres

travelled, which is sometimes

decomposed into the product of the

number of vehicles and the mean distance

travelled per vehicle per year (Greene, et

al., 1999b; Small and Van Dender, 2005).

Energy efficiency is then defined as

vehicle kilometres per litre of fuel and

rebound effects are measured as

increases in distance driven. But this

overlooks any changes in mean vehicle

size and weight as a result of energy

efficiency improvements (e.g. more

SUVs), as well as any decrease in average

vehicle load factor (e.g. less car sharing).

If energy efficiency was measured instead

as tonne kilometres per litre of fuel,

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rebound effects would show up as an

increase in tonne kilometres driven, which

may be decomposed into the product of

the number of vehicles, the mean vehicle

weight and the mean distance travelled

per vehicle per year. To the extent that

vehicle weight provides a proxy for factors

such as comfort, safety and carrying

capacity, this approach effectively

incorporates some features normally

classified as attributes of the energy

service into the measure of useful work. It

also moves closer to a thermodynamic

measure of energy efficiency, by focusing

upon the movement of mass rather than

the movement of people.

The magnitude of direct rebound effects

may be expected to be proportional to the

share of energy in the total cost of energy

services10, as well as the extent to which

those costs are ‘visible’ to the consumer.11

But as the consumption of a particular

energy service increases, saturation

effects (technically, declining marginal

utility) should reduce the size of any direct

rebound effect. For example, direct

rebound effects from improvements in the

energy efficiency of household heating

systems should decline rapidly once

whole-house indoor temperatures

approach the maximum level for thermal

comfort. One important implication is that

direct rebound effects will be higher

among low income groups, since these

are further from satiation in their

consumption of many energy services

(Boardman and Milne, 2000). 

Increases in demand for an energy service

may derive from existing consumers of

the service, or from consumers who were

previously unable or unwilling to purchase

that service. For example, improvements

in the energy efficiency of home airconditioners may encourage consumers to

purchase portable air-conditioners for the

first time. The abundance of such

‘marginal consumers’ (Wirl, 1997) in

developing countries points to the

possibility of large rebounds in these

contexts, offset to only a limited extent by

saturation effects among existing

consumers (Roy, 2000).

While energy efficiency improvements

reduce the energy cost of energy services,

the size of the direct rebound effect will

depend upon how other costs are

affected. For example, direct rebound

effects may be smaller if energy efficient

equipment is more expensive than less

efficient alternatives, because such

improvements should not encourage an

increase in the number and capacity of

conversion devices. However, once

purchased, such devices may be expected

to have a higher utilisation. In practice,

many types of equipment appear to have

both improved in energy efficiency over

time and fallen in total cost relative to

income. 

Even if energy efficiency improvements

are not associated with changes in capital

or other costs, certain types of direct

rebound effect may be constrained by the

10 For example, if energy accounts for 50% of the total cost of an energy service, doubling energy efficiency will reduce the

total costs of the energy service by 25%. But if energy only accounts for 10% of total costs, doubling energy efficiency will reduce

total cost by only 5%. In practice, improvements in energy efficiency may themselves be costly.

11 For example, Kempton and Montgomery (1982) have compared the information value of the average household energy bill

to that of receiving a single monthly bill from the supermarket for ‘food’. Recent developments in smart metering and electronic

display technologies offer the opportunity to improve the information available to consumers, and this could potentially make

the demand for consumer energy services more price elastic.
real or opportunity costs associated with

increasing demand. Two examples are the

opportunity cost of space (e.g. increasing

refrigerator size may not be the best use

of available space) and the opportunity

cost of time (e.g. driving longer distances

may not be the best use of available

time). However, space constraints may

become less important over time if

technological improvements reduce the

average size of conversion devices per

unit of output or if rising incomes lead to

an increase in average living space (e.g.

compare refrigerator sizes in the US and

the UK) (Wilson and Boehland, 2005). In

contrast, the opportunity cost of time

should increase with rising incomes. As

with energy price elasticities, therefore,

the direct rebound effect for a particular

energy service may be expected to vary

over time and to be influenced by income

and other variables.

3.2 Estimating direct

rebound effects from

evaluation studies

One approach to estimating the direct

rebound effect is to measure the change

in demand for useful work following an

energy efficiency improvement: for

example, measuring the change in

internal temperatures following the

installation of a fuel-efficient boiler. The

demand for useful work before the energy

efficiency improvement could be taken as

an estimate for what demand ‘would have

been’ in the absence of the improvement.

However, various other factors may also

have changed the demand for useful work

which needs to be controlled for. 

Since it can be very difficult to measure

useful work for many energy services

(Box 3.1), an alternative approach is to

measure the change in energy

consumption for that service following an

energy efficiency improvement. But to

estimate direct rebound effects, this

needs to be compared with a

counterfactual scenario for energy

consumption that has at least two sources

of error, namely: a) the energy

consumption that would have occurred

without the energy efficiency

improvement; and b) the energy

consumption that would have occurred

following the energy efficiency

improvement had there been no

behavioural change. The first of these

gives an estimate of the energy savings

from the energy efficiency improvement,

while the second isolates the rebound

effect. Estimates for the latter can be

derived from engineering models, but

these frequently require data on the

circumstances of individual installations

and are prone to error.

Both these approaches are analogous to

the policy evaluation strategies employed

in areas such as health and labour

economics, but these appear to be

relatively rare in the energy field owing in

part to measurement difficulties (Frondel

and Schmidt, 2005). There are relatively

few published studies and nearly all of

these studies focus on consumer energy

services. Nadel (1993) reports the results

of a number of evaluation studies by US

utilities, which suggest direct rebound

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effects of 10% or less for lighting and

approximately zero for water heating,

with inconclusive results for refrigeration.

We have not been able to access these

studies, which appear to be small-scale,

short-term and methodologically weak.

Instead, we summarise the results of 15

studies of household heating.

These studies use a variety of approaches

to study a range of energy efficiency

improvements for different types of

household and different income groups,

mostly within the US or UK. The

methodological quality of most of these

studies is relatively poor, with the majority

using simple before and after

comparisons, without the use of a control

group or explicitly controlling for

confounding variables. This is the weakest

evaluation strategy and prone to bias

(Meyer, 1995; Frondel and Schmidt,

2005). Also, several studies are subject to

selection bias, since households choose to

participate rather being randomly

assigned (Hartman, 1988). While

techniques are available to control for

this, they are rarely used (Berry, 1983;

Train, 1994). Other methodological

weaknesses include: small sample sizes

contributing to low statistical power;12

high variance in results and a frequent

failure to present the error associated

with estimates; large variation in the

relevant independent variable both within

and between studies (e.g. participating

households receiving different types of

energy efficiency measure, or

combinations of measures); monitoring

periods that are too short (e.g. one

month) to capture either long-term

behavioural changes or seasonal

variations in behavioural response; and so

on. These weaknesses reduce the degree

of confidence in the results and create

difficulties in comparing the results of

different studies. 

For household heating, it is helpful to

distinguish between:

• shortfall, representing the difference

between actual savings in energy

consumption and those expected on

the basis of engineering estimates; 

• temperature take-back, representing

the change in internal temperature

following the energy efficiency

improvement; and 

• behavourial change, representing the

proportion of change in internal

temperature that derives from

adjustments of heating controls and

other variables by the user (e.g.

opening windows). 

Typically, only a portion of temperature

take-back is due to behavioural change,

with the remainder being due to physical

and other factors (Sanders and Phillipson,

2006).13 Similarly, only a portion of

shortfall may be due to temperature takeback, with the remainder being due to

12 The power of a statistical test is the probability that the test will reject a false null hypothesis.

13 For example, daily average household temperatures will generally increase following improvements in thermal insulation,

even if the heating controls remain unchanged. This is because insulation contributes to a more even distribution of warmth

around the house, reduces the rate at which a house cools down when the heating is off and delays the time at which it needs

to be switched back on (Milne and Boardman, 2000). 
poor engineering estimates of potential

savings, inadequate performance of

equipment, deficiencies in installation and

so on.14 Hence, behavioural change is

one, but not the only (or necessarily the

most important) explanation of

temperature take-back and the latter is

one, but not the only explanation of

shortfall. Several studies misleadingly

equate shortfall with behavioural change

and fail to specify the relevant

uncertainties. 

Direct rebound effects are normally

interpreted as a behavioural response to

lower energy service prices and hence

may be best approximated by the

behavioural change identified above. But

it may be misleading to interpret this

change solely as a rational response to

lower heating costs, partly because

energy efficiency improvements

frequently change other variables (e.g.

airflow) that also encourage behavioural

responses. Also, measures of temperature

take-back may be difficult to translate into

estimates of shortfall because of a nonlinear and household-specific relationship

between energy consumption and internal

temperature. 

Measurement of behavioural responses

requires thermostat settings to be

monitored directly, which is neither

straightforward nor common. Instead,

most studies monitor internal

temperatures and/or energy consumption

and focus upon temperature take-back

and/or shortfall, which are frequently

more relevant to public policy.

Unfortunately, different studies using

different terms for the above concepts as

well as the same term for different

concepts. The UK government uses the

term ‘comfort factor’ to refer to the

shortfall from household insulation

improvements, but it could be misleading

to equate this with the direct rebound

effect. 

The results from evaluation studies of

household heating are difficult to compare

owing to different approaches,

inconsistent methodologies and different

definitions of both the dependent and

independent variables. Some studies

report temperature take-back, some

report shortfall and some make an

estimate of behavioural change.

Without exception, the studies

demonstrate a clear shortfall in energy

savings due to space heating efficiency

measures. The absolute magnitude of this

difference varies between studies, but is

generally between 10% and 50% of

expected savings. In some studies a

larger shortfall was found – for example,

an evaluation of a UK programme for low

income households found that the

introduction of a more efficient heating

system had no impact on reducing fuel

consumption (Hong, et al., 2006).

However, overall percent shortfall is highly

contingent on the accuracy of the

engineering models, and attempts to

calibrate these models to specific

household conditions generally result in a

lower shortfall. The studies use a wide

range of variables to explain shortfall, but

only initial energy consumption and the

age of the home consistently influence the

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24

14 For example, Hong et al. (2006) report the results of thermal camera imaging of houses following insulation improvements,

which showed that 20% of the cavity wall area was missing insulation as well as 13% of the loft area. 
Measurement difficulties make estimation of direct rebound effects problematic at best and

impossible at worst. For many energy services, the relevant data is simply unavailable, while for

others the data must either be estimated or is subject to considerable error. 

Measurement of useful work is extremely problematic, which partly explains why the empirical

literature is largely confined to passenger transport, household heating and household cooling

where suitable proxies are available. Aggregate measures of useful work are only available for a

subset of energy services (e.g. vehicle kilometres for passenger transport), while disaggregate

measures may require expensive monitoring of individual firms or households. In the case of

household heating, a number of studies have monitored thermostat settings or internal

temperatures for individual households (Greening and Greene, 1998). But to assess the full

benefits of the efficiency improvement, the temperature change in each room of the building

would need to be measured, while controlling for outside temperature, changes in occupancy and

other factors. In addition, internal temperature is only one of the determinants of thermal comfort,

with others including activity levels, air velocity, relative humidity and the mean radiant

temperature of the surrounding surfaces (Fanger, 1970; Frey and Labay, 1988). Failure to take

these into account could lead to biased estimates of the direct rebound effect (Friedman, 1987;

Greening and Greene, 1998).

Data on the energy consumption for individual consumer energy services is rarely available,

except when those services are sub-metered as part of an evaluation exercise. Data on total

household energy consumption may be relatively accurate, but techniques such as ‘conditional

demand analysis’ are required to estimate the proportion going to lighting, water heating, cooking

and so on (Parti and Parti, 1980). Problems may even arise in well-studied areas such as personal

automotive transport, where there is uncertainty over the proportion of total petrol and diesel

consumption attributable to passenger cars, as well as the distance travelled by different types of

vehicle (Schipper, et al., 1993).

If data on both energy consumption and useful work is available, system-wide energy efficiencies

can be estimated. Alternatively, the demand for useful work could be estimated from data on

energy consumption and system-wide energy efficiency. But the latter is difficult to obtain, subject

to inaccuracy and (depending upon the system boundary) frequently dependent upon variables

other than the thermodynamic efficiency of conversion devices. 

Empirical studies are more informative if the demand for useful work can be decomposed into the

product of the number, capacity and utilisation of the relevant energy conversion devices, but

usually this data is unavailable or subject to error. For example, several studies of personal

automotive transport combine data on the number of vehicles, the distance driven, total energy

consumption and average fuel efficiency that have not been independently and accurately

determined. (Schipper, et al., 1993). Also, the available data on useful work may not reflect the

full range of direct rebound effects.

Measurement difficulties also apply to exogenous variables that affect the demand for energy,

useful work or energy efficiency, such as demographic and geographical factors. Omission of such

variables could lead to bias if they are correlated with the dependent or independent variables,

while inclusion of such variables could lead to error if they are measured or estimated inaccurately.

The Rebound Effect: an assessment of the evidence for economy-wide energy savings from improved energy efficiency 

25

Box 3.1 Measurement issues for the direct rebound effect
extent to which predicted savings are

likely to be achieved. 

Some positive temperature take-back was

observed in most studies; however it was

frequently small and not always

statistically significant. Take-back

appeared to average between ~0.4oC and

0.8oC. Of this, approximately half was

estimated to be accounted for by the

physical characteristics of the house and

the remainder by behavioural change. In

homes with higher temperature takeback, the physical contribution remains

the same and the behavioural contribution

increases. This take-back is not trivial: a

1oC increase in internal temperature may

increase the energy consumption for

space heating by 10% or more.

Only a subset of the studies that measure

temperature take-back provide estimates

of the effect on energy savings and those

that do vary widely, partly as a

consequence of the different metrics used

to present the effect. The likelihood of

observing temperature take-back appears

to be negatively correlated with income,

as well as internal household

temperatures prior to the efficiency

measure. However, these two explanatory

variables are likely to be correlated. As

pre-intervention room temperatures

approach 21oC the magnitudes of

temperature take-back decreases owing

to saturation effects. 

Estimates of temperature take-back

appear comparable between US and UK

studies, with most showing a relatively

small temperature increase for the

average home and higher temperature

increases for homes with low initial

temperatures. UK average indoor

temperatures appear to be lower than in

the US, but have been increasing over

recent years irrespective of the energy

efficiency measures installed. 

In summary, the evaluation studies

suggest that standard engineering models

may overestimate the energy savings

from energy efficiency improvements in

household heating systems by up to one

half – and potentially by more than this

for low income households. However,

temperature take-back only accounts for

a portion of this shortfall and behavioural

change only accounts for a portion of the

take-back. This suggests that the direct

rebound effect for this energy service

should typically be less than 30%.

Rebound effects may be expected to

decrease over time as average internal

temperatures increase.

3.3 Estimating direct

rebound effects from

econometric studies

The ‘evaluation’ approach to estimating

the direct rebound effect requires data to

be collected on the demand for energy or

useful work both before and after an

energy efficiency improvement. This

‘program focused’ approach is relatively

uncommon. Instead, most studies rely

upon secondary data sources, frequently

collected for other purposes that include

information on the demand for energy,

useful work and/or energy efficiency. This

data can take a number of forms (e.g.

cross-sectional, time-series) and apply to

different levels of aggregation (e.g.

household, region, country). Such studies

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typically use a variety of econometric

techniques to estimate elasticities,

meaning the percentage change in one

variable following a percentage change in

another, holding other variables constant.

If time-series data is available, an

estimate can be made of short run

elasticities, where the stock of conversion

devices is assumed to be fixed, as well as

long-run elasticities where it is variable.

Cross-sectional data is usually assumed to

provide estimates of long-run elasticities.

Depending upon data availability, the

direct rebound effect may be estimated

from one of two energy efficiency

elasticities:15

E1 the elasticity of the demand for

energy with respect to energy

efficiency

E2 the elasticity of the demand for useful

work with respect to energy efficiency

(E2) is generally taken as a direct

measure of the rebound effect. Under

certain assumptions, it can be shown

that: (E1)=(E2)-1 (see Technical Report

2). The actual saving in energy

consumption will only equal the predicted

saving from engineering calculations

when the demand for useful work remains

unchanged following an energy efficiency

improvement (i.e. (E2)=0).15a

But instead of using (E1) or (E2), the

majority of studies estimate the rebound

effect from one of three price elasticities:

E3 the elasticity of the demand for useful

work with respect to the price of

useful work

E4 the elasticity of the demand for useful

work with respect to the price of

energy

E5 the elasticity of the demand for

energy with respect to the price of

energy

Under certain assumptions, the negative

of (E3), (E4) or (E5) can be taken as an

approximation to (E2). The use of price

elasticities in this way implicitly equates

the direct rebound effect to the

behavioural responses identified in

Section 3.2 and ignores the other reasons

why the demand for useful work may

change following an improvement in

energy efficiency.

Estimates of (E1) (E2) and (E3) require

data on energy efficiency for the relevant

energy service, while estimates of (E3)

(E4) and (E5) require data on energy

prices. Generally, the latter tends to be

both more available and more accurate

than the former. Similarly, estimates of

(E2) (E3) and (E4) require data on the

demand for useful work, while estimates

of (E1) and (E5) require data on the

demand for energy. Again, the latter

tends to be both more available and more

accurate than the former.

In principle, estimates of either (E1) or

(E2) should provide the best

15 The rationale for the use of these elasticities, and the relationship between them, is explained in detail in Technical Report 2.

In the case of personal automotive transport, they could correspond to: (E1) the elasticity of the demand for motor-fuel (for

passenger cars) with respect to kilometres per litre (E2) the elasticity of the demand for vehicle kilometres with respect to

kilometres per litre; (E3) the elasticity of the demand for vehicle kilometres with respect to the cost per kilometre; (E4) the

elasticity of the demand for vehicle kilometres with respect to the price of motor-fuel; and (E5) the elasticity of the demand for

motor-fuel with respect to the price of motor-fuel. 

15a Under these circumstances, (E1)=-1. A positive rebound effect implies that (E2)>0 and 0>(E1)>-1, while backfire implies

that (E2)>1 and (E1)>0.
approximation to the direct rebound

effect. However, most data sets provide

only limited variation in energy efficiency,

with the result that estimates of (E1) and

(E2) have a large variance. This is another

reason why estimates of (E1) and (E2) are

relatively rare.

In contrast, (E3) generally provides

significantly greater variation in the

independent variable. This is because the

price of useful work depends upon the

ratio of energy prices to energy efficiency

and most data sets include considerable

cross-sectional or longitudinal variation in

energy prices. In principle, rational

consumers should respond in the same

way to a decrease in energy prices as they

do to an improvement in energy efficiency

(and vice versa), since these should have

an identical effect on the price of useful

work. However, there may be a number of

reasons why this ‘symmetry’ assumption

does not hold (see below). If so,

estimates of the direct rebound effect that

are based upon (E3) could be biased.

In many cases, data on energy efficiency

is either unavailable or inaccurate. In

these circumstances, (E4) and (E5) allow

the direct rebound effect to be estimated

solely from data on energy prices. But

these are only valid if: first, consumers

respond in the same way to a decrease in

energy prices as they do to an

improvement in energy efficiency (and

vice versa); and second, energy efficiency

is unaffected by changes in energy prices.

Both these assumptions are likely to be

flawed, but the extent to which this leads

to biased estimates of the direct rebound

effect may vary widely from one energy

service to another and between the short

and long term.

Under certain assumptions, the own-price

elasticity of energy demand (E5) can be

shown to provide an upper bound for the

direct rebound effect (see Technical

Report 2). As a result, the voluminous

literature on energy price elasticities may

be used to place some bounds on the

likely magnitude of the direct rebound

effect for different energy services in

different sectors. This was the approach

taken by Khazzoom (1980), who pointed

to evidence that the long-run own-price

elasticity of energy demand for water

heating, space heating and cooking

exceeded (minus) unity in some

circumstances, implying that energy

efficiency improvements for these

services could lead to backfire (Taylor, et

al., 1977). However, reviews of this

literature generally suggest that energy

demand is inelastic in the majority of

sectors in OECD countries (i.e. ) (Dahl and

Sterner, 1991; Dahl, 1993; 1994; Espey,

1998; Graham and Glaister, 2002; Hanley,

et al., 2002; Espey and Espey, 2004).

As an illustration, the upper bound for the

direct rebound effect for personal

automotive transport can be estimated

from Hanley et al’s (2002) metaanalysis16 of 51 empirical estimates of the

long-run own-price elasticity of motor-fuel

demand (E5). Taking the mean values,

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28

16 A meta-analysis combines the results of several empirical studies that provide comparable quantitative estimates of a

particular variable, such as the own-price elasticity of energy demand for a particular sector. In energy economics, a common

approach is to use the estimated elasticities as the dependent variable in a multiple regression analysis that seeks to explain the

variation in the results in terms of factors such as model structure (Bergh and Button, 1999). 
The Rebound Effect: an assessment of the evidence for economy-wide energy savings from improved energy efficiency 

29

this suggests an upper bound for the longrun direct rebound effect for this energy

service of 64%.17 For comparison, three

estimates of the elasticity of distance

travelled by private cars with respect to

the price of motor-fuel (E4) suggest a

smaller long-run direct rebound effect of

only 30% (Hanley, et al., 2002). The

difference between the two suggests that

fuel prices have a significant influence on

vehicle fuel efficiency over the long term

and shows the extent to which the use of

(E5) may lead to the direct rebound effect

being overestimated.

For the purpose of estimating rebound

effects, own-price elasticities for energy

demand are most useful when the

demand relates to a single energy service,

such as refrigeration. They are less useful

when (as is more common) the measured

demand derives from a collection of

energy services, such household fuel or

electricity consumption. In this case, a

large own-price for fuel or electricity

demand may suggest that improvements

in the ‘overall’ efficiency of fuel or

electricity use by the household will lead

to large direct rebound effects (and vice

versa). It may also suggest that the direct

rebound effect for the energy services

that dominate fuel or electricity

consumption may be large. This is

relevant for household heating, for

example, since this typically accounts for

more than two thirds of household fuel

consumption. But many households use

more than one energy carrier for heating

and there may also be scope for long-term

substitution between different energy

carriers. In these circumstances the ownprice elasticity for fuel consumption could

overestimate the own-price elasticity of

fuel use for heating. At the same time,

such aggregate data could underestimate

the own-price elasticity of energy demand

for other energy services. For example, it

is possible that the energy demand for

refrigeration is elastic – suggesting the

possibility of large direct rebound effects -

but if overall household electricity demand

is inelastic, such a possibility is disguised. 

Estimates of the own-price elasticity of

household energy demand vary with the

energy carrier, the type of household, the

particular country and/or region, the level

of household income18 and the

methodological approach. For electricity,

the results of a meta-analysis of more

than 120 estimates suggests an upper

bound for the short-term rebound effect

for all household electricity services

combined of 20-35% and an upper bound

for the long-term effect of 80-85% (Espey

and Espey, 2004). Evidence on fuel price

elasticities is more variable and less

robust, but generally suggests a lower

price elasticity than for electricity (Baker

and Blundell, 1991; Ferrer-i-Carbonell, et

al., 2002). Overall, and contrary to

Khazzoom (1980), the evidence suggests

that own-price elasticities of household

fuel and electricity demand are less than

17 However, the mean values disguise a large variance in the results and using the full range of estimates suggests an upper

bound of between 0 and 181%. This partly reflects the wide range of countries and time period on which the estimates are based.

The corresponding mean value for the short run is 25%.

18 Price elasticities are frequently found to be higher among low income groups. For example, Baker et al. (1989) found that,

conditional upon appliance ownership, the own-price elasticity of gas consumption by UK households was two times larger for

the lowest income decile than for top income decile. However, Nesbakken (1999) found that the price elasticity of Norwegian

household energy demand increases with income. Also, low income households often live in rented accommodation and hence

have less scope for changing equipment such as heating systems over the long term (Poyer and Williams, 1992). 
unity and hence that energy efficiency

improvements are unlikely to lead to

backfire (at least, not from direct rebound

effects alone). 

Whatever their origin, estimates of price

elasticities should be treated with great

caution. Aside from the difficulties of

estimation, behavioural responses are

contingent upon technical, institutional,

policy and demographic factors that vary

widely between different groups and over

time. Demand responses are known to

vary with the level of prices, the origin of

price changes (e.g. exogenous versus

policy induced), expectations of future

prices, government fiscal policy (e.g.

recycling of carbon tax revenues),

saturation effects and other factors

(Barker, et al., 1995). The past is not

necessarily a good guide to the future in

this area, and it is possible that the very

long-run response to price changes may

exceed those found in empirical studies

that rely upon data from relatively short

time periods.

Instead of relying upon contested

estimates of (E5), more accurate

information on the magnitude of direct

rebound effects may be obtained from

studies that directly estimate (E3).

However, data is only available on

relevant measures of useful work for a

subset of energy services. As a

consequence, the reliable evidence base

for the direct rebound effect is largely

focused on personal automotive

transportation, household heating and

space cooling, where proxy measures of

useful work are most readily available.

These energy services form a significant

component of household energy demand

in OECD countries and may be expected

to be relatively price elastic. Econometric

evidence for other consumer energy

services is very limited, while that for

producers is almost non-existent. The

evidence base also exhibits a notable

geographical bias, with the majority of

studies referring to the United States and

with very few studies of energy services in

developing countries.

Our review of the empirical literature in

this area is therefore largely confined to

studies that estimate (E1), (E2) or (E3).

These vary widely in terms of type of

data, model structure, functional form and

estimation techniques, which complicates

the comparison of results. Given this

diversity and the limited number of

studies available we have not conducted a

formal meta-analysis, but have instead

summarised key results and identified

possible sources of error. The result of this

survey are summarised in the next two

sections.

3.4 Direct rebound effects

for private automotive

transport 

By far the best studied area for the direct

rebound effect is personal transport by

private car. Most studies refer to the US,

which is important since fuel prices, fuel

efficiencies and residential densities are

lower than in Europe, car ownership levels

are higher and there is less scope for

switching to alternative transport modes.

Studies estimating (E1), (E2) or (E3) vary

considerably in terms of the data used

and specifications employed. Most studies

use aggregate data which can capture

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long-term effects on demand such as fuel

efficiency standards, while household

survey data can better describe individual

behaviour at the micro level. Aggregate

studies face numerous measurement

difficulties, however, while disaggregate

studies produce results that are more

difficult to generalise. The relevant

measure for useful work varies between

total distance travelled, distance travelled

per capita, distance travelled per licensed

driver, distance travelled per household

and distance travelled per vehicle.

Studies using aggregate time-series and

cross-sectional data estimate the long-run

direct rebound effect for personal

automotive transport to be somewhere

between 5% and 30%. While there is

disagreement over the appropriate

specification the limited number of data

points available makes it difficult to settle

the issue from this type of data alone

(Greene, 1992; Jones, 1993).19

Aggregate panel data20 provides a more

robust basis for estimates of the direct

rebound effect, owing to the greater

number of observations. Johansson and

Schipper’s (1997) cross-country study

gives a best guess for the long-run direct

rebound effect of 30%, while both

Haughton and Sakar (1996) and Small

and van Dender (2005) converge on a

long-run value of 22% for the US. Small

and van Dender’s study incorporates

some important methodological

innovations and is one of the few to test

the hypothesis that the direct rebound

effect declines with income (Box 3.2).

Studies using household survey data

sources provide less consistent estimates

of the direct rebound effect for personal

automotive transport and several of these

estimates are higher than those from

aggregate data sources. Three US studies

use data from the same source but

produce estimates of the direct rebound

effect that range from 0% to 87%

(Goldberg, 1996; Puller and Greening,

1999; West, 2004).21 This diversity

suggests that the results from

disaggregate studies should be

interpreted with more caution. One of the

most rigorous studies using disaggregate

data is by Greene et al. (1999), who

estimate the US average long-run direct

rebound effect to be 23% - consistent

with the results of studies using aggregate

data. 

Taken together, our review of 17 studies

suggests that the long-run direct rebound

effect for personal automotive transport

lies somewhere between 10% and 30%.

The relative consensus on estimates,

despite wide differences in data and

methodologies suggests that the findings

are relatively robust. Moreover, most of

19 The dispute relates to the appropriate treatment of serial correlation and lagged dependent variables. Serial correlation means

that the error in one time period is correlated with the error from one or more previous time periods, perhaps as a result of the

influence of unobserved variables that persist over time. Identifying and correcting for serial correlation is a major issue in timeseries econometrics and is relevant to many studies of the direct rebound effect.

20 A pooled cross-section is a cross-sectional sample from a population, taken at two or more intervals in time. Panel data is

similar, but with data from the same units in each sample period.An example would be data on motor-fuel consumption and

related variables from each US state over a period 1972 to 2000.

21 All use the US Consumer Expenditure Survey, although covering different time periods and supplemented by differing sources

of information on vehicle fuel efficiency. The figure of 87% is from West (2004) and is likely to be an overestimate of the direct

rebound effect since it derives from the estimated elasticity of distance travelled with respect to operating costs, which includes

maintenance and tyre costs.
these studies assume that the response to

a change in fuel prices is equal in size to

the response to a change in fuel efficiency,

but opposite in sign. Few studies test this

assumption explicitly and those that do

are either unable to reject the hypothesis

that the two elasticities are equal, or find

that the fuel efficiency elasticity is less

than the fuel cost per kilometer elasticity.

The implication is that the direct rebound

effect may lie towards the lower end of

the above range.

The extent to which the direct rebound

effect for personal automotive travel

declines with income remains unclear,

although the methodologically rigorous

studies by Small and van Dender (2005)

and Greene et al. (1999a) both suggest

that it does. Measurement problems

remain an issue for aggregate studies

(Schipper, et al., 1993), as does the

geographical bias towards the United

States. The available evidence is

insufficient to determine whether direct

rebound effects are larger or smaller in

Europe, but it is notable that the metaanalysis by Espey (1998) found no

significant difference in long-run ownprice elasticities of gasoline demand.

Overall, it must be concluded that direct

rebound effects in this sector have not

obviated the benefits of technical

improvements in vehicle fuel efficiency.

Between 70% to 100% of the potential

benefits of such improvements appear to

have been realised in reduced

consumption of motor-fuels.

3.5 Direct rebound effects

for other consumer energy

services

The next best studied area for direct

rebound effects is household heating,

although relatively few studies estimate

(E1), (E2) or (E3) for this energy service

and even fewer investigate rebound

effects specifically. The available studies

rely upon detailed household survey data

and exhibit even greater diversity in terms

of the variables measured and the

methodologies adopted. Four of the most

rigorous studies are briefly described

here. 

Schwarz and Taylor (1995) use crosssectional data from 1188 single family US

households, including measurements of

thermostat settings. They estimate an

equation for the thermostat setting as a

function of energy prices, external

temperature, heated area, household

income and an engineering estimate of

the thermal resistance of the house. Their

data also allows them to estimate the

demand for useful work for space heating.

On the basis of thermostat settings, their

estimate of (E2) suggests a long-run

direct rebound effect of 0.6% to 2.0%,

while on the basis of the demand for

useful work their estimate suggests a

larger effect of 1.4% to 3.4%. While

these estimates are smaller than those

from other studies, the difference

between the two is comparable to that

between behavioural responses and

temperature take-back identified by the

evaluation studies.

Hseuh and Gerner (1993) use comparable

data from 1281 single family detached

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Small and van Dender (2005) provide one of the most methodologically rigorous

estimates of the direct rebound effect for personal automotive transport. They estimate

an econometric model explaining the amount of travel by passenger cars as a function

of the cost per mile and other variables. By employing simultaneous equations for

vehicle numbers, average fuel efficiency and vehicle miles travelled, they are able allow

for the fact that fuel efficiency is endogenous: i.e. more fuel-efficient cars may

encourage more driving, while the expectation of more driving may encourage the

purchase of more fuel-efficient cars. Their results show that failing to allow for this can

lead the direct rebound effect to be substantially overestimated.

Small and Van Dender use aggregate data on vehicle numbers, fuel efficiency, gasoline

consumption, vehicle miles travelled and other variables for 50 US states and the

District of Columbia covering the period 1961 to 2001. This approach provides

considerably more observations than conventional aggregate time-series data, while at

the same time providing more information on effects that are of interest to policymakers

than do studies using household survey data. The effect of the CAFE standards on

vehicle fuel efficiency are estimated by incorporating a variable representing the gap

between the fuel efficiency standard and an estimate of the efficiency that would have

been chosen in the absence of the standards, giving prevailing fuel prices.

Small and Van Dender estimate the short-run direct rebound effect for the US as a whole

to be 4.5% and the long-run effect to be 22%. The former is lower than most of the

estimates in the literature, while the latter is close to the consensus. However, they

estimate that a 10% increase in income reduces the short-run direct rebound effect by

0.58%. Using US average values of income, urbanisation and fuel prices over the period

1997-2001, they find a direct rebound effect of only 2.2% in the short-term and 10.7%

in the long-term - approximately half the values estimated from the full data set. If this

result is robust, it has some important implications. However, two-fifths of the estimated

reduction in the rebound effect derives from the assumption that the magnitude of this

effect depends upon the absolute level of fuel costs per kilometre. But since the relevant

coefficient is not statistically significant, this claim is questionable.

Although methodologically sophisticated, the study is not without its problems. Despite

covering 50 states over a period of 36 years, the data provides relatively little variation

in vehicle fuel efficiency making it difficult to determine its effect separately from that

of fuel prices. Direct estimates of (E2) are small and statistically insignificant, which

could be interpreted as implying that the direct rebound effect is approximately zero,

but since this specification performs rather poorly overall, estimates based upon (E3)

are preferred. Also, the model leads to the unlikely result that the direct rebound effect

is negative some states (Harrison, et al., 2005). This raises questions about the use of

the model for projecting declining rebound effects in the future, since increasing

incomes could make the estimated direct rebound effect negative in many states.

Box 3.2 The declining direct rebound effect
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34

households in the US, dating back to

1981. Their dataset includes

comprehensive information on appliance

ownership and demographic

characteristics, which allows them to

combine econometric and engineering

models to estimate the energy use for

space heating. On the basis of an estimate

of (E5), conditional on the existing level of

energy efficiency, the short-run direct

rebound effect can be estimated as 35%

for electrically heated homes and 58% for

gas heated homes. 

Klein (1987; 1988) uses comparable data

from more than 2000 US households,

supplemented with an engineering model

of the thermal performance of buildings

and data on the capital cost of equipment.

This allows the estimation of a

sophisticated ‘household production’

model, involving simultaneous equations

for the total cost of space heat, the share

of energy in the total cost of space heat

and the demand for space heat. On the

basis of (E3), Klein’s model suggests a

short-run direct rebound effect in the

range 25% to 29%.22 

Guertin et al. (2003) use detailed survey

data from 440 single family Canadian

households and apply ‘frontier analysis’ to

estimate the energy efficiency of the

relevant appliances. This in turn allows

the energy consumption for space

heating, water heating and

appliances/lighting to be estimated. On

the basis of (E3), Guertin et al’s model

suggests a long-run mean rebound effect

of 38%, varying from 29% for high

income groups to 47% for low income

groups.23

Other estimates of direct rebound effects

for household heating include Haas and

Biermayer (2000) who use a variety of

techniques to reach an estimate of 15-

30% for German households, and Douthitt

(1986) whose study of 370 Canadian

households suggest short-run effects in

the range 10-17% and long-run effects in

the range 25-60%.

Reliable estimates of the direct rebound

effects for household heating therefore

range from 10% to 58% in the short-run

and 1.4% to 60% in the long-run, with a

suggestion that rebound effects are larger

for low income groups. As with the

evaluation studies, the definition of the

direct rebound effect is not consistent

between the above studies, the response

varies between different households and

the results from one time period and

geographical area may not translate to

other circumstances. Nevertheless, for the

purpose of policy evaluation, a figure of

30% for the direct rebound effect for

household heating would appear a

reasonable assumption.

For space cooling in households, two

studies provide estimates of direct

rebound effects that are within the range

of those reported for space heating (i.e.

1-26%) (Hausman, 1979; Dubin, et al.,

1986). However, these relatively old

studies use small sample sizes and were

conducted during period of rising energy

22 Greening et al’ (1998) quote a figure of 40% from the study, but this overestimates the direct rebound effect since it is based

upon the elasticity of heat demand with respect to the generalised cost of space heat, which includes capital and maintenance

costs. 

23 The variation in (E3) between income groups was much greater for space heating than for other energy services.
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35

prices. Their results may not be

transferable to many European countries,

given the differences in house types and

climatological conditions. Also, both

studies focus solely upon changes in

equipment utilisation. To the extent that

ownership of space cooling technology is

rapidly increasing in many countries,

demand from ‘marginal consumers’ may

be an important consideration, together

with increases in system capacity among

existing users. 

Evidence for water heating is also very

limited, although Guertin et al. (2003)

provides estimates in the range 34 to

38%, which is greater than the results

from US evaluation studies reported by

Nadel (1993). A methodologically rigorous

study of direct rebound effects for clothes

washing suggests that direct rebound

Davis (2007) provides a unique example of an estimate of direct rebound effects for

household clothes washing - which together with clothes drying accounts for around one

tenth of household energy consumption. The estimate is based upon a US governmentsponsored field trial of high-efficiency washing machines involving 98 participants.

These machines use 48% less energy per wash than standard machines and 41% less

water.

While participation in the trial was voluntary, both the utilisation of existing machines

and the associated consumption of energy and water was monitored for a period of two

months prior to the installation of the new machine. This allowed household specific

variations in utilisation patterns to be controlled for and permitted unbiased estimates

to be made of the price elasticity of machine utilisation.

The monitoring allowed the marginal cost of clothes washing for each household to be

estimated. This was then used as the primary independent variable in an equation for

the demand for clean clothes in kg/day (useful work). Davis found that the demand for

clean clothes increased by 5.6% after receiving the new washers, largely as a result of

increases in the weight of clothes washed per cycle rather than the number of cycles.

While this could be used as an estimate of the direct rebound effect, it results in part

from savings in water and detergent costs. If the estimate was based solely on the

savings in energy costs, the estimated effect would be smaller. This suggests that only

a small portion of the gains from energy efficient washing machines will be offset by

increased utilisation.

Davis estimates that time costs form 80-90% of the total cost of washing clothes. The

results therefore support the theoretical prediction that, for time intensive activities,

even relatively large changes in energy efficiency should have little impact on demand.

Similar conclusions should therefore apply to other time-intensive energy services that

are both produced and consumed by households, including those provided by

dishwashers, vacuum cleaners, televisions, power tools, computers and printers. 

Box 3.3 Direct rebound effects for clothes washing 
effects for ‘minor’ energy services should

be relatively small (Box 3.3). However,

this confines attention to households that

already have automatic washing machines

and therefore excludes rebound effects

from marginal consumers.

Table 3.1 summarises the results of our

survey of econometric estimates of the

direct rebound effect. Despite the

methodological diversity, the results for

individual energy services are broadly

comparable. This suggests that the

evidence for direct rebound effects is

relatively robust to different datasets and

methodologies.

These relatively modest estimates for

direct rebound effects are based upon

studies in OECD countries and are unlikely

to be representative of conditions in

developing countries. Hence, two studies

from developing countries deserve a

mention. In a rare study of traditional

fuels, Zein-Elabdin (1997) estimates

direct rebound effects from fuel-efficient

stoves in Khartoum by multiplying the fuel

efficiency improvement by the product of

the estimated income elasticity of demand

for charcoal and the share of charcoal in

the household budget. Allowing for the

additional (indirect) effects on the price of

charcoal leads to an estimated rebound

effect of 42%. 

Roy (2000) reports the results of a

government programme that freely

distributed solar-charged battery lamps to

a rural village in India. These lamps

provided significantly better lighting

quality than the kerosene lamps they

displaced. Daily hours of lighting

increased from two to four, provided by a

combination of new and old lamps since

the solar lamps were limited in operating

hours. This led to a direct rebound effect

of approximately 50% (80% for some

households). Moreover, since the ‘saved’

kerosene was either used for cooking or

sold, accounting for indirect effects

suggests that the programme achieved no

energy savings - although significant

benefits to welfare. Since the lamps were

free and kerosene subsidised, this may be

an atypical example. However, the

primary source of the large rebound effect

was the large unsatisfied demand for

lighting. This condition applies to a range

of energy services in developing countries

and especially for the 1.6 billion

households who currently lack access to

The Rebound Effect: an assessment of the evidence for economy-wide energy savings from improved energy efficiency 

36

End-Use Range of ‘Best guess’ No. of Studies Degree 

Values in of Confidence

Evidence Base

Personal automotive 5-87% 10-30% 17 High

transport

Space heating 1.4-60% 10-30% 9 Medium

Space cooling 1-26% 1-26% 2 Low

Other consumer 0-49% <20% 3 Low

energy services

Table 3.1 Estimates of the long-run direct rebound effect for consumer energy

services in the OECD
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37

electricity and the 2.5 billion who rely

upon biomass for cooking.

3.6 Sources of bias in

estimates of the direct

rebound effect

The use of price elasticities to estimate

the direct rebound effect creates the risk

of bias. Most studies assume that changes

in energy prices have an opposite effect to

comparable changes in energy efficiency

and that any changes in energy efficiency

derive solely from outside the model (i.e.

energy efficiency is ‘exogenous’). In

practice, both of these assumptions may

be incorrect.

First, while changes in energy prices are

generally not correlated with changes in

other input costs, changes in energy

efficiency may be. In particular, higher

energy efficiency may only be achieved

through the purchase of new equipment

with higher capital costs than less efficient

models. Hence, estimates of the direct

rebound effect that rely primarily upon

historical and/or cross-sectional variations

in energy prices could overestimate the

direct rebound effect, since the additional

capital costs required to improve energy

efficiency will not be taken into account

(Henly, et al., 1988).

Second, energy price elasticities tend to

be higher for periods with rising prices

than for those with falling prices (Gately,

1992; 1993; Dargay and Gately, 1994;

1995; Haas and Schipper, 1998). For

example, Dargay (1992) found that the

reduction in UK energy demand following

the price rises of the late 1970s was five

times greater than the increase in

demand following the price collapse of the

mid-1980s. An explanation may be that

higher energy prices induce technological

improvements in energy efficiency, which

may also become embodied in regulations

(Grubb, 1995). Also, investment in

measures such as thermal insulation is

largely irreversible over the short to

medium-term. But the appropriate proxy

for improvements in energy efficiency is

reductions in energy prices. Since many

studies based upon time series data

incorporate periods of rising energy

prices, the estimated price elasticities

may overestimate the response to falling

energy prices. As a result, such studies

could overestimate the direct rebound

effect. 

Third, while improved energy efficiency

may increase the demand for useful work

(e.g. you could drive further after

purchasing an energy-efficient car), it is

also possible that the anticipated high

demand for useful work may increase the

demand for energy efficiency (e.g. you

purchase an energy-efficient car because

you expect to drive further). In these

circumstances, the demand for useful

work depends on the price of useful work,

which depends upon energy efficiency,

which depends upon the demand for

useful work (Small and Van Dender,

2005). Hence, the direct rebound effect

would not be the only explanation for any

measured correlation between energy

efficiency and the demand for useful

work. This so-called ‘endogeneity’ can be

addressed through the use of

simultaneous equation models, but these

are relatively uncommon owing to their

greater data requirements. If, instead,
studies include the ‘endogenous’

variable(s) within a single equation and do

not use appropriate techniques to

estimate this equation, the resulting

estimates could be biased. Several studies

of direct rebound effects could be flawed

for this reason.

Finally, consumers may be expected to

take the full costs of energy services into

account when making decisions about the

consumption of those services and these

include the time costs associated with

producing and/or using the relevant

service - for example, the time required to

travel from A to B. Indeed, the increase in

energy consumption in industrial societies

over the past century may have been

driven in part by attempts to ‘save time’

(and hence time costs) through the use of

technologies that allow tasks to be

completed faster at the expense of using

more energy. For example, travel by

private car has replaced walking, cycling

and public transport; automatic washing

machines have replaced washing by hand;

fast food and ready meals have replaced

traditional cooking and so on. While not all

energy services involve such trade-offs,

many do (compare rail and air travel for

example). Time costs may be

approximated by hourly wage rates and

since these have risen more rapidly than

energy prices throughout the last century,

there has been a strong incentive to

substitute energy for time (Becker, 1965).

If time costs continue to increase in

importance relative to energy costs, the

direct rebound effect for many energy

services should become less important –

since improvements in energy efficiency

will have an increasingly small impact on

the total cost of useful work (Binswanger,

2001). This suggests that estimates of the

direct rebound effect that do not control

for increases in time costs (which is

correlated with increases in income) could

potentially overestimate the direct

rebound effect. Box 3.2 shows how this

could be particular relevant to direct

rebound effects in transport.

The consideration of time costs also points

to an important but relatively unexplored

issue: increasing time efficiency may lead

to a parallel ‘rebound effect with respect

to time’ (Binswanger, 2001; Jalas, 2002).

For example, faster modes of transport

may encourage longer commuting

distances, with the time spent commuting

remaining broadly unchanged.24 So in

some circumstances energy consumption

may be increased, first, by trading off

energy efficiency for time efficiency (e.g.

choosing air travel rather than rail) and

second, by the rebound effects with

respect to time (e.g. choosing to travel

further). 

3.7 Summary

• Evidence for the direct rebound effect

for automotive transport and

household heating within developed

countries is relatively robust.

Evidence for direct rebound effects for

other consumer energy services is

much weaker, as is that for energy

efficiency improvements by

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24 In the UK, the time spent travelling increased by only 17% between 1975 in 2005, while the total distance travelled increased

by 52% (Department of Transport, 2006).
The Rebound Effect: an assessment of the evidence for economy-wide energy savings from improved energy efficiency 

39

producers. Evidence is particularly

weak for energy efficiency

improvements in developing countries

although theoretical considerations

suggest that direct rebound effects in

this context will be larger than those

in developed countries. 

• Under certain assumptions, estimates

of the own-price elasticity of energy

demand for an individual energy

service should provide an upper

bound for the direct rebound effect for

that service. If the measured energy

demand relates to a group of energy

services (e.g. household fuel

demand), the own price elasticity

should provide an approximate upper

bound for the weighted average of

direct rebound effects for those

services. Since the demand for energy

is generally found to be inelastic in

OECD countries, the long-run direct

rebound effect for most energy

services should be less than 100%. 

• For personal automotive transport,

household heating and household

cooling in OECD countries, the mean

value of the direct rebound effect is

likely to be less than 30% and may be

closer to 10% for transport. Moreover,

the direct rebound effect is expected

to decline in the future as demand

saturates and income increases. Both

theoretical considerations and the

available empirical evidence suggest

that direct rebound effects should be

smaller for other consumer energy

services where energy forms a small

proportion of total costs. Hence, at

least for OECD countries, direct

rebound effects should only partially

offset the energy savings from energy

efficiency improvements in consumer

energy services.

• These conclusions are subject to a

number of qualifications, including the

relatively limited time periods over

which direct rebound effects have

been studied and the restrictive

definitions of ‘useful work’ that have

been employed. For example, current

studies only measure the increase in

distance driven for automotive

transport and do not measure

changes in vehicle size. Rebound

effects for space heating and other

energy services are also higher

among low income groups and most

studies do not account for ‘marginal

consumers’ acquiring services such as

space cooling for the first time.

• The methodological quality of many

‘evaluation studies’ is poor, while the

estimates from many econometric

studies appear vulnerable to bias. The

most likely effect of the latter is to

lead the direct rebound effect to be

overestimated. Considerable scope

exists for improving estimates of the

direct rebound effect for the energy

services studied here and for

extending estimates to include other

energy services.
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4. The evidence for indirect and economy-wide rebound effects

This section summarises the empirical

evidence for indirect and economy-wide

rebound effects, focusing upon the limited

number of studies that provide

quantitative estimates of these effects.

This is separate from the more ‘indirect’

evidence for economy-wide rebound

effects that is the subject of Section 5. A

full examination of this evidence is

contained in Technical Report 4 and

Technical Report 5.

Indirect rebound effects derive from two

sources: the energy required to produce

and install the measures that improve

energy efficiency, such as thermal

insulation, and the indirect energy

consumption that results from such

improvements. The first of these relates

to energy consumption that occurs prior

to the energy efficiency improvement,

while the second relates to energy

consumption that follows the

improvement. Sections 4.1 and 4.2

describe the mechanisms responsible for

each. 

Section 4.3 examines the limited

empirical evidence for indirect rebound

effects, focusing on studies that estimate

the ‘embodied’ energy associated with

different categories of consumer goods

and services. Despite the apparent

potential of this approach, there appear to

be very few applications to the rebound

effect.

Section 4.4 examines the evidence for

economy-wide rebound effects available

from computable general equilibrium

(CGE) models of the macro-economy.

These simulate the full range of

mechanisms responsible for rebound

effects, but the realism and policy

relevance of this approach is open to

question. Also, despite the widespread

use of such models, there are only a

handful of applications to the rebound

effect. Section 4.5 describes an

alternative ‘macro-econometric’ approach

that can overcome some of these

weaknesses, and summarises the results

of a recent application of this approach to

the UK economy. Section 4.6 concludes.

4.1 Embodied energy – the

limits to substitution

Many improvements in energy efficiency

can be understood as the ‘substitution’ of

capital for energy within a particular

system boundary. For example, thermal

insulation (capital) may be substituted for

fuel (energy) to maintain the internal

temperature of a building at a particular

level. It is these possibilities that form the

basis of estimated ‘energy saving’

potentials in different sectors. However,

estimates of energy savings typically

neglect the energy consumption that is

required to produce and maintain the

relevant capital - frequently referred to as

embodied energy. For example, energy is

required to produce and install home

insulation materials and energy efficient

motors. Substituting capital for energy

therefore shifts energy use from the

sector in which it is used to sectors of the

economy that produce that capital. As a

result, energy use may increase

elsewhere in the economy (Kaufmann and

Azary-Lee, 1990).

Figure 4.1 provides a graphical

interpretation of this process (Stern,

1997). Here, the function F(K) represents
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the different combinations of energy (E)

and capital (K) that may be used to

provide a given level of output for a

particular firm or sector. But this capital

also has indirect energy consumption

associated with it, elsewhere in the

economy, represented by the function

G(K). The summation of the two gives the

total energy H(K) used in the economy as

a whole to produce the given level of

output. It can be seen that: first, the net

energy savings from the substitution of

capital for energy will be less than the

energy savings within the firm or sector in

which the substitution takes place; and

second, when capital inputs exceed a

certain level (K’), the indirect energy

consumption will exceed the direct energy

savings – leading to backfire for the

economy as a whole, even when the

individual firm or sector reduces energy

consumption and when output from this

sector is unchanged. 

In practice, backfire from this source

alone appears rather unlikely, since the

cost of an energy efficient technology

should reflect the cost of the embodied

energy (Webb and Pearce, 1975). If the

latter exceeds the saving in energy costs,

it is unlikely that the investment would be

cost-effective.25 However, this assumes

that the sole benefit of the investment is

the reduced energy costs, which may not

always be the case. Also, market

imperfections may distort the relevant

prices and costs. 

In contrast to other sources of the

economy-wide rebound effect, the

contribution from this source may be

expected to be smaller in the long-term

than in the short-term. This is because the

embodied energy associated with capital

equipment is analogous to a capital cost

and hence diminishes in importance

relative to ongoing energy savings as the

lifetime of the investment increases. 

Some authors argue that similar

conclusions apply to the substitution of

labour for energy, since energy is also

required to feed and house workers and

thereby keep them economically

productive (Kaufmann, 1992). However,

there is some dispute over whether and

how to account for the ‘energy cost of

labour’ (Costanza, 1980).26 Similarly,

while economists conventionally

distinguish between substitution and

technical change (Box 2.1), the latter is

also associated with indirect energy

consumption since it is embodied in

capital goods and skilled workers (Stern

and Cleveland, 2004). 

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25 Note that this argument applies to measures of energy consumption weighted by the relative price of different energy carriers

and not to energy consumption measured simply in terms of heat content. 

26 Costanza (1980) estimates the energy cost of labour as the energy associated with all personal consumption expenditures.

These energy costs are then assigned to individual goods and services in proportion to the labour required to produce them.

Double counting is avoided by changing the boundaries of the traditional economic input-output analysis. The net result is to

greatly increase the embodied energy estimated to be associated with labour-intensive goods and services. With this approach,

the ‘embodied energy intensity’ of most sectors (excluding primary energy) is found to be broadly comparable. However, this

conclusion depends entirely upon the particular methodology for calculating the energy cost of labour. This is quite different from

conventional accounting approaches, which estimate much lower energy intensities for many sectors and greater variation

between them. It also implicitly assumes that all personal consumption expenditures are necessary to support labour, which

appears unjustified. 
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In principle, an assessment of the

embodied energy associated with a

particular energy efficiency improvement

should take into account the relevant

alternatives. For example, a mandatory

requirement to replace existing

refrigerators with more energy efficient

models may either increase or decrease

aggregate energy consumption over a

particular period of time, depending upon

the age of the existing stock, the lifetime

of the new stock, and the direct and

indirect energy consumption associated

with different models of refrigerator. In

practice, however, such estimates appear

to be rare, with most analysts focusing

instead upon the ‘energy return on energy

invested’27 for different energy supply

options (Cleveland, 1992).28

4.2 Secondary effects

Energy efficiency improvements may

themselves change the demand for other

goods and services. For example, the

purchase of a more fuel-efficient car may

reduce demand for public transport, but at

the same time increase the demand for

leisure activities that can only be accessed

with a private car (Binswanger, 2001).

Each of these goods and services will have

an indirect energy consumption

associated with them and the changed

pattern of demand may either increase

overall energy consumption or reduce it.

The net impact of such effects will be

specific to both individual technologies

and individual consumers and can also be

very difficult to estimate. 

Figure 4.1 The limits to substitution Source: Stern (1997)

Energy - E

Capital - K

H (K)

F (K)

G (K)

K’

27 The Energy Returned on Energy Invested (EroEI) is the ratio of the usable energy acquired from a particular energy resource

to the amount of energy expended to obtain that energy resource. In principle, when the EROEI of a resource is equal to or

lower than 1, that energy source can no longer be used as a primary source of energy. However, this measure neglects the

relative economic productivity of different energy forms. When this is taken into account, resources with an EroEI of less than

unity may still be economic to extract. 

28 Studies based upon embodied energy have fallen out of favour since the 1980s, when they were often associated with

somewhat controversial ‘energy theories of value’ (Söllner, 1997). But there is no necessary link between these theories and use

of ‘embodied energy’ estimates in empirical research. Consideration of rebound effects may provide a motivation for reviving

this area of research.
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Very similar effects will result from energy

efficiency improvements by producers.

For example, energy efficiency

improvements in steel production should

reduce the cost of steel and (assuming

these cost reductions are passed on in

lower product prices)29 reduce the input

costs of manufacturers that use steel. This

in turn should reduce the cost of steel

products and increase demand for those

products. Such improvements could, for

example, lower the cost of passenger

cars, increase the demand for car travel

and thereby increase demand for motorfuel.

This example demonstrates how energy

efficiency improvements could lead to a

series of adjustments in the prices and

quantities of goods and services supplied

throughout an economy. If the energy

efficiency improvements are widespread,

the price of energy intensive goods and

services may fall to a greater extent than

that of non-energy intensive goods and

services, thereby encouraging consumer

demand to shift towards the former. If

energy demand is reduced, the resulting

fall in energy prices will encourage greater

energy consumption by producers and

consumers and will feed through into

lower product prices, thereby encouraging

further shifts towards energy intensive

commodities. Reductions in both energy

prices and product prices will increase

consumers’ real income, thereby

increasing demand for products,

encouraging investment, stimulating

economic growth and further stimulating

the demand for energy. In some

circumstances, such improvements could

also change trade patterns and

international energy prices and therefore

impact on energy consumption in other

countries. 

A number of analysts have claimed that

the secondary effects from energy

efficiency improvements in consumer

technologies are relatively small (Lovins,

et al., 1988; Greening and Greene, 1998;

Schipper and Grubb, 2000). This is

because energy makes up a small share of

total consumer expenditure and the

energy content of most other goods and

services is also small. For example,

suppose energy efficiency improvements

reduce natural gas consumption per unit

of space heated by 10%. If there is no

direct rebound effect, consumers will

reduce expenditure on natural gas for

space heating by 10%. If natural gas for

heating accounts for 5% of total

consumer expenditure, consumers will

experience a 0.5% increase in their real

disposable income. If all of this were

spent on motor-fuel for additional car

travel, the net energy savings (in kWh

thermal content) will depend upon the

ratio of natural gas prices to motor-fuel

prices, and could in principle be more or

less than one.30 In practice, however,

motor-fuel only accounts for a portion of

the total cost of car travel and car travel

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29 Product prices will only fall if a sufficiently large number of domestic firms within the sector benefit from the energy efficiency

improvement and will be limited by the extent to which the product market is exposed to international competition. 

30 Chalkley et al. (2001) provide an example of the replacement of an inefficient (C-rated) refrigerator with an efficient (A-rated)

model. Lifetime carbon savings for the refrigerator are estimated at 1645 kgCO2 and lifetime cost savings at £120.57. If these

cost savings were spent wholly on gasoline, the indirect CO2 emissions would be 358 kg, giving an indirect rebound effect, in

carbon terms, of 22%. However, spending all of the cost savings on gasoline is unrealistic. 
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only accounts for a portion of total

consumer expenditure. For the great

majority of goods and services, inputoutput data suggests that the effective

expenditure on energy should be less than

15% of the total expenditure. Hence, by

this logic, the secondary effect should be

only around one tenth of the direct effect

(Greening and Greene, 1998). 

Entirely analogous arguments apply to the

secondary effects for producers, such as

in the steel example above. Since energy

forms a small share of total production

costs for most firms and sectors (typically

<3%) and since intermediate goods form

a small share of the total costs of most

final products, the product of these

suggests an indirect effect that is much

smaller than the direct effect (Greening

and Greene, 1998). 

However, while plausible, these

arguments are not supported by the

results of several of the quantitative

studies discussed below. Moreover, they

assume that the only effect of the energy

efficiency improvement is to reduce

expenditure on energy. But as argued in

Section 2, improvements in the energy

efficiency of production processes are

frequently associated with improvements

in the productivity of capital and labour as

well and therefore lead to cost savings

that exceed the savings in energy costs

alone. In some cases, similar arguments

may apply to energy efficiency

improvements by consumers: for

example, a shift from car travel to cycling

could save on depreciation and

maintenance costs for vehicles as well as

motor-fuel costs (Alfredsson, 2004). In

these circumstances, the secondary

effects that result from the adoption of a

particular technology could be substantial

and may even exceed the direct energy

savings.

4.3 Evidence for limits to

substitution

Some indication of the importance of

embodied energy may be obtained from

estimates of the own-price elasticity of

aggregate primary, secondary or final

energy demand. In principle, this

measures the scope for substituting

capital, labour and materials for energy,

while holding output constant. Most

energy price elasticities are estimated at

the level of individual sectors and

therefore do not reflect all the embodied

energy associated with capital, labour and

materials inputs. Since the own-price

elasticity of aggregate energy reflects this

indirect energy consumption, it should in

principle be smaller than a weighted

average of energy demand elasticities

within each sector. However, the

aggregate elasticity may also reflect

price-induced changes in economic

structure and product mix which in

principle could make it larger than the

average of sectoral elasticities (Sweeney,

1984). These two mechanisms therefore

act in opposition.

Based in part upon modelling studies,

Sweeney (1984) puts the long-run

elasticity of demand for primary energy in

the range -0.25 to -0.6. In contrast,

Kaufmann (1992) uses econometric

analysis to propose a range from -0.05 to

-0.39, while Hong (1983) estimates a

value of -0.05 for the US economy. A low

value for this elasticity may indicate a
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limited scope for substitution and hence

the potential for large indirect rebound

effects.31 But this interpretation is not

straightforward, since direct rebound

effects also contribute to the behaviour

being measured. Also, measures of the

quantity and price of ‘aggregate energy’

are sensitive to the methods chosen for

aggregating the prices and quantities of

individual energy carriers, while the price

elasticity will also depend upon the

particular composition of price changes

(e.g. increases in oil prices relative to gas)

(EMF 4 Working Group, 1981). In

particular, when different energy types

are weighted by their relative marginal

productivity, the estimated elasticities

tend to be lower (Hong, 1983) As a result,

the available estimates of aggregate price

elasticities may be insufficiently precise to

provide much indication of the magnitude

of indirect rebound effects. 

Relatively few empirical studies have

investigated the embodied energy

associated with specific energy efficiency

improvements and those that have appear

to focus disproportionately upon domestic

buildings. In a rare study of energy

efficiency improvements by producers,

Kaufmann and Azary Lee (1990) estimate

that, in the US forest products industry

over the period 1954 to 1984, the

embodied energy associated with capital

equipment offset the direct energy

savings from that equipment by as much

as 83% (Box 4.1). But since their

methodology is crude and the results

specific to the US context, this study

provides little indication of the magnitude

of these effects more generally. 

Estimates of the embodied energy of

different categories of goods and services

can be obtained from input-output

analysis, life-cycle analysis (LCA) or a

combination of the two (Chapman, 1974;

Herendeen and Tanak, 1976; Kok, et al.,

2006). A full life-cycle analysis is time

consuming to conduct and must address

problems of ‘truncation’ (i.e. uncertainty

over the appropriate system boundary)32

and joint production (i.e. how to attribute

energy consumption to two or more

products from a single sector) (Leach,

1975; Lenzen and Dey, 2000). Hence,

many studies combine standard economic

input-output tables with additional

information on the energy consumption of

individual sectors, to give a

comprehensive and reasonably accurate

representation of the direct and indirect

energy required to produce rather

aggregate categories of goods and

services. More detailed, LCA-based

estimates are available for individual

products such as building materials, but

results vary widely from one context to

another depending upon factors such as

the fuel mix for primary energy supply

(Sartori and Hestnes, 2007).

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31 This is in contrast to the own-price elasticity of energy demand for an individual energy service, where high values may

indicate the potential for large direct rebound effects.

32 For example, should the indirect energy costs of a building also include the energy used to make the structural steel and mine

the iron ore used to make the girders? This is referred to as the truncation problem because there is no standard procedure for

determining when energy costs become small enough to neglect 
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Kaufmann and Azary Lee (1990) examined the embodied energy associated with energy

efficiency improvements in the US forest products industry over the period 1954 to

1984. First, they estimated a production function for the output of this industry and used

this to derive the ‘marginal rate of technical substitution’ (MRTS) between capital and

energy in a given year - in other words, the amount of gross fixed capital that was used

to substitute for a thermal unit of energy in that year. Second, they approximated the

embodied energy associated with that capital by means of the aggregate energy/GDP

ratio for the US economy in that year - hence ignoring the particular type of capital used,

as well as the difference between the energy intensity of the capital producing sectors

and that of the economy as a whole. The product of these two variables gave an

estimate of the indirect energy consumption associated with the gross capital stock used

to substitute for a unit of energy. This was then multiplied by a depreciation rate to give

the energy associated with the capital services used to substitute for a unit of energy.

Finally, they compared the estimated indirect energy consumption with the direct energy

savings in the forest products sector in each year. Their results showed that the indirect

energy consumption of capital offset the direct savings by between 18 and 83% over

the period in question, with the net energy savings generally decreasing over time. The

primary source of the variation was the increase in the MRTS over time, implying that

an increasing amount of capital was being used to substitute for a unit of energy.

However, the results were also influenced by the high energy/GDP ratio of the US

economy, which is approximately twice that of many European countries. Overall, the

calculations suggest that the substitution reduced aggregate US energy consumption,

but by much less than a sector-based analysis would suggest. Also, their approach did

not take into account any secondary effects resulting from the energy efficiency

improvements. 

The simplicity of this approach suggests the scope for further development and wider

application. Accuracy could be considerably improved by the use of more flexible

production functions and more precise estimates for the indirect energy consumption

associated with specific types and vintages of capital goods. However, to date no other

authors appear to have applied this approach to particular industrial sectors or to have

related it to the broader debate on the rebound effect.

Box 4.1 Limits to substitution for producers
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As an illustration, Sartori and Hestnes

(2007) reviewed 60 case studies of

buildings, and found that the share of

embodied energy in life-cycle energy

consumption ranged between 9 and 46%

for low energy buildings and between 2

and 38% for conventional buildings – with

the wide range reflecting different building

types, material choices and climatic

conditions. Two studies that controlled for

these variables found that low energy

designs could achieve substantial

reductions in operating energy

consumption with relatively small

increases in embodied energy, leading to

‘payback periods’ for energy saving of as

little as one year (Feist, 1996; Winther

and Hestnes, 1999). Similar calculations

were performed by the Royal Commission

on Environmental Pollution (2007), who

estimate a 15 year simple payback (in

energy terms) for low energy new build

houses in the UK. However, Casals (2006)

shows how the embodied energy of such

buildings could offset operational energy

savings, even with an assumed 100 year

lifetime. Such calculations typically

neglect differences in energy quality and

the results are sensitive to context, design

and building type. However, the increasing

availability of embodied energy

coefficients at a national level (e.g. Alcorn

and Baird (1996)) suggests the scope for

greater use of such estimates in policy

evaluation. 

In the case of existing buildings, several

studies suggest that retrofits of thermal

insulation pay for themselves in terms of

energy savings within a few months

(compared to a useful life in excess of 25

years), while the corresponding period for

double glazing is several years. In other

cases, for example condensing boilers

compared to conventional boilers, the

variation of embodied energy within

individual categories of boiler exceeds the

difference between them. Hence, the

contribution of embodied energy to the

economy-wide rebound effect appears to

vary widely from one situation to another

and is inversely proportional to the

lifetime of the energy saving measure.

But the patchy nature of this evidence

base, the lack of systematic comparisons

of energy efficiency options and the

dependence of the results on particular

contexts all make it difficult to draw any

general conclusions. 

4.4 Evidence for secondary

effects

By combining estimates of the embodied

energy associated with different

categories of goods and services with

survey data on household consumption

patterns, it is possible to estimate the

total (direct plus indirect) energy

consumption of different types of

household; together with the indirect

energy consumption associated with

particular categories of expenditure (Kok,

et al., 2006). It is often found that the

indirect energy consumption of

households exceeds the direct

consumption. Moreover, while indirect

energy consumption increases with

income, direct energy consumption shows

signs of saturation - suggesting that

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indirect energy consumption is becoming

increasingly important over time.33 

If this data is available at a sufficiently

disaggregated level, it could also be used

to estimate the secondary effects

associated with energy efficiency

improvements by households - provided

that additional information is available on

either the cross price elasticity between

different product and service categories,

or the marginal propensity to spend34 of

different income groups. By combining

the estimates of embodied energy and

secondary effects, an estimate of the total

indirect rebound effect may be obtained.

Such approaches are ‘static in that they

do not capture the full range of price and

quantity adjustments, but could

nevertheless be informative.35 However,

of the 19 studies in this area reviewed by

Kok, et al. (2006), only three were

considered to have sufficient detail to

allow the investigation of such micro-level

changes – largely because they combined

input-output with LCA data (Bullard, et al.,

1978). Hence, estimation of secondary

effects by this route appears to be in its

infancy.

Three studies that use this general

approach are summarised here. First,

Brännlund et al. (2007) examine the

effect of a 20% improvement in the

energy efficiency of personal transport (all

modes) and space heating in Sweden.

They estimate an econometric model of

aggregate household expenditure, in

which the share of total expenditure for

thirteen types of good or service is

expressed as a function of the total

budget, the price of each good or service

and an overall price index. This allows the

own-price, cross-price and income

elasticities of each good or service to be

estimated.36 Energy efficiency

improvements reduce the cost of

transport and heating and lead to

substitution and income effects that

change overall demand patterns (e.g.

improvements in transport efficiency are

estimated to increase demand for clothes

but to decrease demand for beverages).

By combining these estimated changes in

demand patterns with CO2 emission

coefficients for each category of good and

service (based upon estimates of direct

and indirect energy consumption)

Brännlund et al. find that energy

efficiency improvements in transport and

heating lead to (direct + indirect)

rebound effects (in carbon terms) of

120% and 170% respectively. 

Brännlund et al.’s results are heavily

dependent on the assumed carbon

33 Results vary widely with country, time period and methodology. For example, Herendeen (1978) found that indirect energy

consumption in Norway accounted for one third of total energy consumption for a poor family and approximately two thirds for

a rich family. Vringer and Blok (1995) found that 54% of total energy demand in Dutch households was indirect, while Lenzen

(1998) found that 30% of total energy demand in Australian households was indirect.

34 Defined as the change in expenditure on a particular product or service, divided by the change in total expenditure. The

marginal propensity to spend on different goods and services varies with income and it is an empirical question as to whether

the associated indirect energy consumption is larger or smaller at higher levels of income. However, the greater use of energy

intensive travel options by high income groups (notably flying) could be significant in some cases.

35 In technical terms, these provide a partial equilibrium analysis, as distinct from the general equilibrium analysis provided by

CGE models.

36 Brännlund et al. employ Almost Ideal Demand (AID) model, which has been shown to have a number of advantages over

other models of consumer demand (Deaton and Mulbauer, 1980; Xiao, et al., 2007). The model relies on the assumption of

‘staged-budgeting’: for example consumers are assumed to first decide on the proportion of their budget to spend on transport,

and then to decide how to allocate their transport budget between different modes. While analytically convenient, this

assumption is likely to be flawed. 
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emission coefficients, but the source of

these is not made explicit. The results also

contradict the econometric evidence on

direct rebound effects, since carbon

emissions for heating and transport are

estimated to increase. Furthermore,

Brännlund et al. use an iterative

estimation procedure, but only present

the results from the first estimation step.

This weakness is overcome by Mizobuchi

(2007), who follows a very similar

approach to Brännlund et al, but applied

to Japanese households. Despite the

differences in data sources and estimation

procedures, the estimated rebound effects

are broadly the same. However, Mizobuchi

also examines the effect of the additional

capital cost of energy efficient equipment

and finds that these reduce the rebound

effect significantly.

The third example adopts a different

approach, using data on the marginal

propensity to spend of different income

groups in Sweden. Alfredsson (2004)

calculates the direct and indirect energy

consequences of ‘greener’ consumption

patterns, which include both technical

changes, such as buying a more fuelefficient car, and behavioural changes

such as car sharing. In the case of

‘greener’ food consumption (e.g. shifts

towards a vegetarian diet), the total

energy consumption associated with food

items is reduced by around 5% and total

expenditure on food items is reduced by

15%. But the re-spending of this money

on a variety of items, notably travel and

recreation, leads to indirect energy

consumption that more than offsets the

original energy savings (i.e. backfire). The

results for a shift towards ‘greener’ travel

patterns are less dramatic, but the

secondary effects from re-spending

reduce the overall energy savings by

almost one third. A comprehensive switch

to green consumption patterns in travel,

food and housing is estimated to have a

rebound effect of 35%. 

Secondary effects are relatively large in

this example because ‘green’ consumption

reduces expenditure on more than energy

alone. Also, the results from such studies

depend upon the methodology and

assumptions used, as well as the types of

household analysed and the particular

shifts in consumption patterns that are

explored. For example, a more recent

study (Kanyama, et al., 2006) using a

similar model and approach to Alfredsson,

but employing Swedish rather than Dutch

data on energy intensity, finds that a shift

to ‘green’ food consumption could reduce

overall energy consumption. Closer

examination reveals that this result

follows largely from the assumption that

greener diets are more expensive (owing

to the higher cost of locally produced

organic food), thereby leading to a

negative ‘re-spending’ effect.

In sum, the potential of embodied energy

approaches to estimating secondary

effects has yet to be fully explored. While

the studies reviewed here suggest that

secondary effects may sometimes be

larger than commonly assumed, the

conclusions may change once

methodological weaknesses are

addressed or a different choice of

independent variable is made. Hence, at

present the available evidence is too small

to permit any general conclusions to be

drawn.

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4.5 Evidence for economywide effects from general

equilibrium models

The economy-wide rebound effect

represents the sum of the direct and

indirect rebound effects and will depend

upon the size, nature and location of the

energy efficiency improvements. Given

the number of confounding variables, it is

likely to be very difficult to estimate

through the econometric analysis of

secondary data. 

One approach, although it would not

permit quantification of the overall effect,

would be to identify those variables that

should influence the size of direct and

indirect effects and to estimate these

empirically. Direct rebound effects for

producers, for example, should be

influenced by the own-price elasticity of

demand for the relevant products, the

share of energy in the total cost of

production and the extent to which the

cheaper energy services are able to

substitute for capital, labour and materials

(Saunders, 1992; Allan, et al., 2006).

Rebound effects may be expected to be

larger in energy intensive sectors and also

where the input mix is fairly flexible and

where the demand for products is

relatively price-elastic. 

An alternative approach is to estimate the

magnitude of economy-wide effects

through energy-economic models of the

macro-economy (Grepperud and

Rasmussen, 2004). Despite the

widespread use of such models within

energy studies (Bhattacharyya, 1996), it

is only recently that attempts have been

made to quantify economy-wide rebound

effects in this way.37 The literature is

therefore extremely sparse, but now

includes two insightful studies

commissioned by the UK government

(Allan, et al., 2006; Barker and Foxon,

2006). A key distinction is between

Computable General Equilibrium (CGE)

models of the macroeconomy and those

based upon econometrics. This section

discusses some results from CGE models,

while Section 4.5 discusses an application

of a macro-econometric model to the

rebound effect.

Computable General Equilibrium (CGE)

models are widely used in the

investigation of energy and climate policy,

partly as a consequence of the ready

availability of modelling frameworks and

the associated benchmark data. This

approach is informed by neoclassical

economic theory, but can deal with

circumstances that are too complex for

analytical solutions. 

CGE models are calibrated to reflect the

structural and behavioural characteristics

of particular economies and in principle

can indicate the approximate order of

magnitude of direct and indirect rebound

effects from specific energy efficiency

improvements. A CGE model should allow

the impacts of such improvements to be

isolated, since the counterfactual is simply

a model run without any changes in

energy efficiency, as well as allowing the

rebound effect to be decomposed into its

constituent components, such as

37 A special edition of the Energy Policy journal published in 2000 provides a invaluable account of the state of the debate at

that time, but does not include any modelling estimates of economy-wide rebound effects (Schipper, 2000)
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substitution and output effects. In

principle, CGE models also provide scope

for sensitivity analysis, although in

practice this appears to be rare.

CGE models have a number of limitations

(see Box 4.2) that have led many authors

to question their realism and policy

relevance (Barker, 2005). While

developments in CGE methodology are

beginning to overcome some of these

weaknesses, most remain. Also, the

predictive power of such models is rarely

tested and different models appear to

produce widely varying results for similar

policy questions (Conrad, 1999). Hence,

while CGE models may provide valuable

insights, the quantitative results of such

models should be interpreted with caution.

Eight CGE modelling studies of rebound

effects have been identified and reviewed

(Table 4.1). These models vary

considerably in terms of the production

functions used, the manner in which

different inputs are combined (the

‘nesting’ structure), the assumed scope

for substitution between different inputs,

the treatment of labour supply, the

manner in which government savings are

recycled and other key parameters. These

studies also vary widely in their simulation

of energy efficiency improvements, with

some introducing an across the board

improvement and others introducing a

specific improvement in an individual

sector, or combination of sectors. This

diversity, combined with the limited

number of studies available again makes it

difficult to draw any general conclusions.

The most notable result is that all of the

studies find economy-wide rebound

effects to be greater than 37% and most

studies show either large rebounds

(>50%) or backfire. The latter was found

in two studies of economies in which

energy forms an important export and

import commodity, suggesting that this is

a potentially important and hitherto

neglected variable. Allan et al. (2006) find

a long-term rebound effect of 31% from

across-the-board improvements in the

energy efficiency of UK production sectors,

including primary energy supply. This

study is summarised in Box 4.3.

The results suggest that the magnitude of

rebound effect depends upon a wide range

of variables. While Saunders (1992) and

others stress the importance of the

‘elasticity of substitution’ between energy

and other inputs in production (Box 4.4),

other characteristics such as the elasticity

of supply of capital and labour, the ownprice elasticity of demand for the product

of each sector, the energy intensity of

producing sectors, the scope for

substitution between different

consumption goods, the income elasticity

of the demand for goods and the manner

in which government revenue is

redistributed are also potentially

important. The contrasting results of

Hanley et al. (2005) for the Scottish

economy (rebound >100%) and Allan et

al. (2006) for the UK economy (rebound ~

37%) are revealing, since the assumed

improvement in energy efficiency is the

same in both cases (5% in all production

sectors). The difference is primarily due to

the relative sensitivity of export demand

to changes in energy efficiency. While both

studies assume that energy efficiency

improvements are made in electricity

generation, it is only in the Scottish case

that this leads to a substantial increase in

electricity exports – and hence in domestic

energy consumption.

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• Market and behavioural assumptions: CGE models are based upon a number of standard neoclassical assumptions (e.g. utility maximization; perfect competition; constant returns to scale

in production; etc.) that are poorly supported by empirical evidence. In particular, the

possibility of ‘win-win’ policies, such as those aimed at encouraging energy efficiency, may be

excluded if an economy is assumed to be at an optimal equilibrium.

• Functional Forms: The choice of utility and production functions is governed by the need for

convenience and solvability, rather than realism and generally imposes restrictions on the

behaviour of the economy being modelled. The choice of a different production function can

lead models to predict different outcomes to an identical policy change. The most common

approach is the ‘nested constant elasticity of substitution’ (CES) production function, but the

manner in which this is implemented varies widely from one model to another. This structure

also assumes that the optimum ratio of two inputs that are grouped together in a ‘nest’ is

unaffected by either the level or price of other inputs (‘separability’). However, this assumption

is generally not supported by empirical evidence (Frondel and Schmidt, 2004).

• Calibration and parameters: CGE models are calibrated to benchmark data from a single year

and adjustments are made to the data to ensure that the equilibrium assumption holds. The

choice of base year for calibration is somewhat arbitrary and may influence the results. Some

parameter values will be determined through this calibration, while others will be set externally

on the basis of econometric literature. However, the time periods/regions/sectors on which this

literature is based may not be appropriate to the model application, and the process of

compiling parameter values may not always be transparent. Assumed parameter values could

have a significant influence on the model results, but the calibration approach provides no

information on the statistical reliability of individual estimates. Sensitivity tests are possible,

but these are frequently confined to a small number of relevant parameters. Also, the modeling

typically rests on the implicit assumption that such parameters will remain stable over time.

• Sensitivity and transparency: There exists considerable variation between CGE models so that

care needs to taken when comparing results. Changing some assumptions can sometimes

generate very different simulated outcomes. The model results can be driven by assumptions

that are not apparent to a reader not acquainted with the model. But CGE models are not

necessarily a ‘black box’: transparency may be considerably improved by providing information

on key features and assumptions and explaining model results with reference to economic

theory.

• Treatment of Technical Change: In most CGE models, technological change is assumed to be

costless and exogenous and thus not related to or determined from within the system. A

common approach is to assume that energy efficiency improves at a constant rate over time,

but this fails to capture price/policy induced innovation and other characteristics of endogenous

technical change. 

• Time scales: Many (but not all) CGE models simulate equilibrium states of the economy and

do not consider the dynamics of adjustment and the associated costs. The solutions are

assumed to refer to the ‘long-run’, but the time period that this represents may be unclear. For

example, transport and building infrastructures take much longer to adjust than other types of

capital equipment.

Box 4.2 Limitations of CGE models
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Table 4.1 Summary of CGE studies of rebound effects Author/ Date Country or region Nesting structure ESUB with energy Assumed energy efficiency improvements Estimated rebound effect Comments Semboja, 1994a Kenya Elec, fuel, K,

L

1.0 Two scenarios: an improvement of

energy production efficiency and animprovement in energy useefficiency.

>100% Intuitive presentation, no sensitivity tests, lack of transparency.

Dufournaud

et al,

1994

Sudan Utility

functions

0.2 and

0.4.

100%, 150% and 200%improvement in efficiency of woodburning stoves

47-77%. Models efficiency improvements in domestic stoves. Wide range of sensitivity tests and

good explanation of the factors at work.

Vikstrom,2004

Sweden (KE)L Values

range

from 0.07

to 0.87.

15% increase in energy efficiency innon-energy sectors and 12%increase in energy sectors.

50-60% Applies to 1957-1962 period in which known changes in energy efficiency productivity

and structure are combined in turn. Results apply only to energy efficiency component

Washida,2004

Japan (KL)E 0.5. 1% in all sectors modelled as

change in efficiency factor for use ofenergy in production

53% in

central

scenario

Presentation unclear, although there is some sensitivity analysis, including varyingelasticity of substitution from 0.3 to 0.7 jointly with other parameters. Rebound effectincreases as energy/value added, labour/capital and level of energy compositesubstitution elasticities increase.

GrepperudandRasmussen

2004

Norway (KE)L Between

0&1

Doubling of growth rates of energyproductivity. Four sectors haveelectricity efficiency doubled, & twohave oil efficiency doubled.

Small for

oil but

>100% for

electricity

Model is simulated dynamically with a counterfactual case in which projections of worldeconomic growth, labour force growth, technological progress and net foreign debt areassumed until 2050.

GlomsrødandTaoyuan,2005

China Elec,

fuel, K,

L

1.0 Business-as-usual scenario

compared to case where costlessinvestments generate increasedinvestments and productivity in coalcleaning - lowering price andincreasing supply

>100% Coal intensive sectors benefit, as does whole economy due to high use of coal in primary

energy consumption. Also examines cases where coal use is subject to emissions tax.

Hanley etal, 2005

Scotland

(KL)(EM) 0.3. 5% improvement in efficiency of

energy use across all productionsectors

>100% Region is significant electricity exporter and result depends in part on increased

electricity exports

Allan et al,2006

UK (KL)(EM) 0.3 5% improvement in efficiency of

energy use across all productionsectors (including energy sectors).

37% in

central

scenario

See Box 4.3

Notes: The production functions combine inputs into pairs, or ‘nests’. For example, a nested production function with capital (K), labour (L) and energy (E) inputs, couldtake one of three forms, namely: K(LE); (KL)E; (KE)L. ESUB means the elasticity of substitution between energy and other inputs. Interpretation depends upon the nestingstructure. For example, for K(LE) ESUB refers to the elasticity of substitution between L and E, while for (KL)E it refers to elasticity of substitution between (KL) and E.
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Allan et al. (2006) estimate economy-wide rebound effects for the UK following a 5%

across-the-board improvement in the efficiency of energy use in all production sectors.

Since their model allows for the gradual updating of capital stocks, they are able to

estimate a short run rebound effect of 50% and a long-run effect of 31% (which

contradicts most of the literature, which estimates long-run effects to be larger).

The energy efficiency improvements increase long-run GDP by 0.17% and employment

by 0.21%. They have a proportionally greater impact on the competitiveness of energy

intensive sectors which is passed through in lower product prices despite a 0.3%

increase in real wages. Output is increased in all sectors, with the iron and steel and

pulp and paper sectors benefiting the most with long-run increases of 0.67% and 0.46%

respectively. In contrast, the output of the oil refining and electricity industries (i.e. oil

and electricity demand) is reduced, with the price of conventional electricity falling by

24% in the long-run. This fall in energy prices contributes a significant proportion of the

overall rebound effect and results from both cost reductions in energy production - due

to the energy efficiency improvements - and reduced energy demand.

Allan et al. are using an economic rather than thermodynamic measure of energy

efficiency, but a 5% improvement may nevertheless be impractical for industries such

as electricity generation and oil refining which are operating close to thermodynamic

limits. It would also require major new investment and take time to be achieved. The

results suggest that the economy-wide rebound effect would be smaller if energy

efficiency improvements were confined to energy users, but this scenario was not

investigated. Moreover, the results suggest that energy efficiency improvements in the

energy supply industry may be associated with large rebound effects. 

A notable feature of this study is the use of sensitivity tests. Varying the assumed

elasticity of substitution between energy and non-energy inputs between 0.1 and 0.7

(compared to a baseline value of 0.3) had only a small impact on economic output (from

0.16% in the low elasticity case to 0.10% in the high case), but a major impact on the

rebound effect. This varied from 7% in the low case to 60% in the high case. Grepperud

and Rasmussen (2004) report similar results, which highlights the importance of this

parameter for CGE simulations. Unfortunately, as shown in Box 4.4, the empirical basis

for this parameter is weak.

Varying the elasticity of demand for exports was found to have only a small impact on

GDP and energy demand, suggesting that the energy efficiency improvements had only

a small impact on the international competitiveness of the relevant industries. However,

different treatments of the additional tax revenue were found to be important, with

recycling through lower taxes increasing the rebound effect from 31% to 40%.

Box 4.3 CGE estimates of economy-wide rebound effects for the UK
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All but one of the models explore the

implications of energy efficiency

improvements in production sectors and

the CGE literature offers relatively little

insight into the implications of energy

efficiency improvements in consumer

goods. Since there are differences across

income groups, this would require a much

greater detail on the demand side of the

CGE models than is commonly the case,

together with more accurate

representations of household behaviour. 

CGE models also simulate energy

efficiency improvements as pure ‘energy

augmenting technical change’, which is

assumed to be costless. Of the studies

reviewed here, only Allan et al. (2006)

consider the implications of additional

costs associated with energy efficiency

improvements and they find that rebound

effects are correspondingly reduced.

In summary, given the small number of

studies available, the diversity of

approaches and the methodological

A key parameter in CGE models is the so-called elasticity of substitution between

different inputs. This determines the extent to which one input can substitute for

another while keeping output constant. The elasticity of substitution between energy

and other inputs has a strong influence on the estimated magnitude of rebound effects

(Grepperud and Rasmussen, 2004).

There are a large number of empirical estimates of elasticities of substitution between

different inputs, within different countries and sectors and over different periods of time.

In principle, this literature could be used as a basis for selecting appropriate parameter

values in individual CGE models. In practice, however, there is only a tenuous

relationship between the parameters estimated by empirical studies and those assumed

by the models. In particular, CGE models:

• use different types of production function from those estimated within empirical

studies; 

• use different definitions of the elasticity of substitution from those estimated with

empirical studies;

• combine inputs into nests while most empirical studies do not;

• assume that the scope for substitution within a nest is independent of the level or

prices of other inputs, while most empirical studies do not;

• make assumptions about the elasticity of substitution between different nests, while

most empirical studies provide estimates of the elasticity of substitution between

individual pairs of inputs. 

In addition: the process of compiling parameter values is rarely transparent; sensitivity

tests are uncommon; the empirical studies frequently apply to different sectors, time

periods and levels of aggregation to those represented by the model; and different

models use widely different assumptions. These observations suggest that the empirical

basis for most CGE models is relatively weak. 

Box 4.4 Parameterising CGE models
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weaknesses of the CGE approach, it is not

possible to draw any general conclusions

regarding the size of economy-wide

rebound effect. Indeed, since the most

important insight from this literature is

that the economy-wide rebound effect

varies widely from one circumstance to

another, a general statement on the size

of such effects may be misleading.

Perhaps the most useful application of this

approach would be to investigate the

determinants of rebound effects more

systematically. It is worth noting,

however, that the available studies

suggest that economy-wide effects are

frequently large and that the potential for

backfire cannot be ruled out. Moreover,

these effects derive from ‘pure’ energy

efficiency improvements and therefore do

not rely upon simultaneous improvements

in the productivity of capital and labour

inputs. 

4.6 Evidence for economywide effects from macroeconometric models

Macro-econometric models can overcome

several of the weaknesses of CGE

modelling while at the same time providing

a greater level of disaggregation that

permits the investigation of specific

government policies. Their usefulness can

be further enhanced if they can be

effectively linked to ‘bottom-up models’38

of particular sectors, such as electricity

generation, where representation of

specific technologies is desirable. In

contrast to their CGE counterparts, macroeconometric models do not rely upon

restrictive assumptions such as constant

returns to scale and perfect competition.

They also replace the somewhat ad-hoc

use of parameter estimates with

econometric equations estimated for

individual sectors which implicitly reflect

non-optimising behaviour, such as the

apparent neglect of cost-effective

opportunities to improve energy efficiency.

However, this greater realism is achieved

at the expense of greater complexity, more

onerous data requirements and higher

costs in developing and maintaining such

models. This section describes the use of

one such model (MDM-E3)39 to explore

economy-wide rebound effects in the UK

(Barker and Foxon, 2006; Barker, et al.,

2007). At present, this appears to be the

only application of such models to the

economy-wide rebound effect.

MDM-E3 combines time-series econometric

relationships and cross-sectional inputoutput relationships with detailed

modelling of the energy sector (Junankar,

et al., 2007). It distinguishes 27

investment sectors, 51 categories of

household expenditure and 41

industries/commodities. The model

includes equations for consumption,

investment, employment and trade for

each of the 41 industries, together with

equations for wage rates, output and

38 ‘Bottom-up’ or engineering-economic models usually focus upon individual sectors and energy services and contain detailed

information on the performance and cost of energy-using equipment. They simulate the ageing and replacement of this

equipment and seek to minimise the net cost of energy services given appropriate assumptions about individual and

organisational investment behaviour.

39 The Cambridge Multisectoral Dynamic Model of the UK energy-environment-economy (MDM-E3) is developed and maintained

by Cambridge Econometrics Ltd and also used by the Cambridge Centre for Climate Change Mitigation Research (4CMR) at the

Department of Land Economy, University of Cambridge.
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commodity prices. The projected activity

levels are used as inputs to an energy submodel, along with data on temperature and

energy prices and assumptions about

technical change. This leads to estimates of

total energy demand, fuel and electricity

use by 13 categories of user and electricity

prices. The sub-model then feeds this

information back into the main model by

adjusting the input-output coefficients for

producers and consumer expenditures on

energy.40 The two models are run

iteratively to reach a stable solution over

the projection period. Both the main model

and electricity sub-model use modern

‘cointegration’ techniques to distinguish

short-term dynamic responses from longterm relationships (Engle and Granger,

1987; Cambridge Econometrics, 2005)

In a study conducted on behalf of the UK

government, MDM-E3 was used to simulate

the macroeconomic impact of a number of

UK energy efficiency policies over the

period 2000 to 2010 (Barker and Foxon,

2006). The first stage was to develop

exogenous estimates of the ‘gross’ energy

savings from each of these policies, the

‘net’ energy savings which allow for the

estimated direct rebound effects associated

with each policy and the associated

investment costs. While Barker and Foxon

(2006) state that the direct rebound effect

from energy efficiency improvements by

producers was assumed to be zero, the

output effects from such improvements are

subsequently modelled. Hence, it is the

substitution effect from such

improvements that is assumed to be zero. 

The next stage was to run the model for a

base case that included the full set of

energy efficiency policies, together with

reference scenario that did not. The

estimated net energy savings were

included directly in the energy demand

equations for each sector, the results of

which were then fed back into the

macroeconomic model. Given the

projected lower energy demand, lower

energy prices and hence lower energy

costs for each sector, the model projected

a range of secondary effects including

increased output and changes in trade

patterns. The estimated investment costs

of the energy efficiency measures were

also taken into account by including them

in the relevant investment equations.

Overall, the cost-effective energy efficiency

measures contributed to a 1.26% increase

in GDP by 2010 (relative to the reference

scenario) a 0.84% increase in employment

and a slight increase in imports. Energy

consumption was 13.8% lower than in the

reference scenario, even after allowing for

economy-wide rebound effects. 

Indirect rebound effects were estimated as

the difference between the energy savings

projected by the model and the estimated

net energy savings, expressed as a

percentage of the estimated gross energy

savings. The base run led to an estimated

indirect rebound effect of 11% by 2010.

Closer inspection shows that indirect

rebound effects were higher in the energy

intensive industries (25%) and lower for

households and transport (7%) (the

opposite to what was assumed for direct

effects). The primary source of the indirect

effects was substitution between energy

and other goods by households, together

with increases in output by (particularly

energy intensive) industry, which in turn

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40 This contrasts with other model frameworks, in which projections from a macroeconomic model are simply used as inputs into

an energy sub-model, without any feedback from the latter.
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led to increased demand for both energy

and energy-intensive intermediate goods.

Increases in consumers’ real income

contributed relatively small rebound effects

(0.2%).

The direct rebound effects were estimated

to reduce overall energy savings by 15%,

leading to an estimated economy-wide

rebound effect of 26% in 2010. Indirect

effects appeared relatively insensitive to

variations in energy and carbon prices. The

authors claim that the results support

Grubb’s (1990) argument that policyinduced energy efficiency improvements

are associated with small rebound effects

because they focus on energy services with

a low own-price elasticity as a consequence

of various market failures. This is

misleading, however: first, because this

argument applies to direct rebound effects

rather than the indirect effects being

modelled; and second because these

services should become more price elastic

if the market failures are overcome.

The macro-econometric approach

exemplified by MDM-E3 appears to offer a

more robust approach to estimating

economy-wide rebound effects than CGE

modelling. Nevertheless, there are a

number of reasons why this particular

study may underestimate economy-wide

rebound effects. First, while output effects

are estimated, the substitution between

(cheaper) energy services and other inputs

within production sectors is ignored. Also,

the direct rebound effects for consumers

have to be estimated rather than modelled.

Second, the modelling implicitly assumes

‘pure’ energy efficiency improvements,

with no associated improvements in the

productivity of other inputs. But if energy

efficient technologies are commonly

associated with such improvements,

rebound effects could be larger. Third, the

model does not reflect the indirect energy

consumption embodied within the energy

efficient technologies themselves. While

the extra investment is taken into account,

it appears to reduce the overall energy

consumption associated with investment

rather than increase it. Finally, the model

confines attention to national energy use

and ignores the indirect energy

consumption associated with increased

imports and tourism. This omission could

be significant from a climate change

perspective, since it corresponds to ~40%

of the extra domestic output.

In sum, while greater confidence can be

placed in this estimate of economy-wide

rebound effects than those from CGE

models, there are a number of reasons why

this study may underestimate the full

effect. Also, any similarity between this

result and that of Allan et al. (2006) is

spurious, since they use different

approaches to model different types of

rebound effect from different types and

size of energy efficiency improvement in

different sectors. 

4.7 Summary 

• There are very few quantitative

estimates of indirect and economywide rebound effects and those that

are available have a number of flaws.

While a number of methodological

approaches are available to estimate

these effects, the limited number of

studies to date provides an insufficient

basis to draw any general conclusions.

• Techniques are available to estimate

the embodied energy associated with

energy efficiency improvements, but
15

20 25

30

the potential of these techniques has

yet to be fully exploited. Most studies

focus on domestic buildings and

demonstrate that embodied energy

associated with energy efficient

buildings varies widely between

different building types, designs and

contexts. In most cases, the embodied

energy is more than outweighed by the

direct energy savings during the

building life-cycle, but the contribution

of embodied energy to the economywide rebound effect is generally

ignored.

• The secondary effects associated with

energy efficiency improvements by

households may be estimated through

a combination of embodied energy

analysis and econometric models of

consumer behaviour. Only a handful of

studies have adopted this approach,

and these give estimates of between

33% and 170% for direct and

secondary effects combined. However,

these studies have a number of

weaknesses, and the results are very

dependent upon the particular

application as well as the data and

methodologies employed. 

• CGE studies suggest that the

magnitude of economy-wide rebound

effects depend very much upon the

sector where the energy efficiency

improvements take place and are

sensitive to a number of variables.

While only a handful of studies are

available, they suggest that economywide rebound effects may frequently

exceed 50% and the potential for

backfire cannot be ruled out. Moreover,

these rebound effects derive from

‘pure’ energy efficiency improvements

and therefore do not rely upon

simultaneous improvements in the

productivity of capital and labour. 

• The results of CGE studies results apply

solely to energy efficiency

improvements by producers, so

therefore cannot be extended to

energy efficiency improvements by

consumers. Also, the small number of

studies available, the diversity of

approaches used and the

methodological weaknesses of CGE

modeling all suggest the need for

caution when interpreting these

results.

• In principle, more robust estimates of

the economy-wide rebound effect may

be obtained from macro-econometric

models of national economies. Barker

and Foxon (2006) use this approach to

estimate an economy-wide rebound

effect of 26% from current UK energy

efficiency policies. However, there are a

number of reasons why this study may

have underestimated the economywide rebound effect.

• The main insight from this evidence

base is the dependence of the

economy-wide rebound effects on the

nature and location of the energy

efficiency improvement, which makes

any general statements regarding the

magnitude of such effects

questionable. However, while little

confidence can be placed in the

quantitative estimates, the frequent

finding that economy-wide rebound

effects exceed 50% should give cause

for concern. Extending and improving

this evidence base should therefore be

a priority for future research.

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15

20 25

30
This section summarises the empirical

evidence for the Khazzoom-Brookes

postulate, focusing in particular on the

work of W.S. Jevons, Len Brookes and

Harry Saunders. An in-depth examination

of this evidence is contained in Technical

Report 5.

The ‘Khazzoom-Brookes’ (K-B) postulate

claims that economy-wide rebound effects

exceed unity, so that energy efficiency

improvements lead to backfire. The term

was first coined by Saunders (1992) who

refers specifically to the energy efficiency

improvements that result from technical

change, rather than from the substitution

of other inputs for energy (Box 2.1). Also,

Saunders’s statement of the K-B postulate

implies that ‘pure’ energy efficiency

improvements will increase energy

consumption, regardless of any

associated improvements in the

productivity of capital, labour and

materials. However, this distinction is not

made by other advocates of the K-B

postulate, including Len Brookes himself

(Brookes, 2000).

Neither Brookes nor Saunders cite

empirical estimates of direct, indirect and

economy-wide rebound effects, but

instead develop their case through a mix

of theoretical argument, mathematical

modelling, anecdotal examples and

‘suggestive’ evidence from econometric

analysis and economic history (Box 5.1).

It is these ‘indirect’ sources of evidence

that are reviewed in this section. None of

this research provides quantitative

estimates for the size of the economywide rebound effect and the majority of

the studies make no reference to the

rebound effect at all. Instead, they

provide evidence that may arguably be

used in support of the K-B postulate,

either on theoretical grounds or on the

basis of historical experience. Like the KB postulate itself, this evidence frequently

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5. The evidence for ‘backfire’

• The relationship between macro-level energy productivity and micro-level

thermodynamic efficiency (Berndt, 1978; 1990; Ang, 2006). 

• The relationship between energy productivity and total factor productivity (Schurr,

1982; 1983; 1985; Jorgenson, 1996). 

• The contribution of energy to economic growth (Toman and Jemelkova, 2003; Stern

and Cleveland, 2004). 

• The implications of energy augmenting technical change within neoclassical

production and growth theory (Saunders, 1992; 2007). 

• Decomposition analysis of historical trends in energy consumption (Schipper and

Grubb, 2000). 

• Econometric studies of the ‘causal’ relationships between energy consumption and

economic growth (Kraft and Kraft, 1978; Stern, 1993). 

• Empirically validated alternatives to conventional models of economic growth that

include energy or useful work as a factor of production (Kummel, et al., 2002; Ayres

and Warr, 2005). 

Box 5.1 Sources of evidence considered indirectly relevant to the K-B postulate
runs counter to conventional wisdom.

However, in all cases it is suggestive

rather than definitive. 

This section is structured as follows:

Section 5.1 provides a historical context

to the debate, including Jevons’ 19th

century example of the effect of energy

efficiency improvements in steam

engines, together with more

contemporary examples of comparable

‘general-purpose’ technologies. This

introduces the central theme of this

section: namely that the evidence used in

support of the K-B postulate is closely

linked to a broader question regarding the

contribution of energy to economic

growth.

Section 5.2 summarises Brookes’

arguments in favour of the postulate,

identifies some empirical and theoretical

weaknesses and examines whether more

recent research supports Brookes’ claims.

Section 5.3 provides a non-technical

summary of Saunders’ work, highlighting

the dependence of these results on

specific theoretical assumptions and the

questions it raises for standard economic

approaches.

Section 5.4 investigates the empirical

evidence regarding the scope for

substitution between energy and capital.

It shows how 30 years of research have

failed to reach a consensus on this issue

and argues that the relationship between

this and the rebound effect is more

complex than some authors have

suggested. Section 5.5 examines further

evidence on the contribution of energy to

economic growth and points to the

interesting parallels between Brookes’

arguments and those of contemporary

ecological economists. While the evidence

remains ambiguous and open to

interpretation, the central argument is

that energy – and by implication energy

efficiency - plays a significantly more

important role in economic growth than is

assumed within mainstream economics.

Section 5.6 highlights some of the

implications of this finding for the

economy-wide rebound effect. Section 5.7

concludes. 

5.1 Historical perspectives

The argument that improved energy

efficiency will increase economy-wide

energy consumption was first made by

W.S. Jevons in 1865, who used the

example of the steam engine:

“... it wholly a confusion of ideas to

suppose that the economical use of fuel is

equivalent to a diminished consumption.

The very contrary is the truth.......Every

improvement of the engine when effected

will only accelerate anew the consumption

of coal...” (Jevons, 1865)

Jevons cites the example of the Scottish

iron industry, in which:

“..... the reduction of the consumption of

coal, per ton of iron, to less than one third

of its former amount, has been

followed....by a tenfold increase in total

consumption, not to speak of the indirect

effect of cheap iron in accelerating other

coal consuming branches of industry...”

(Jevons, 1865)

According to Jevons, the early Savory

engine for pumping floodwater out of coal

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mines “...consumed no coal because it rate

of consumption was too high.” It was only

with the subsequent improvements by

Watt and others that steam engines

became widespread in coal mines,

facilitating greater production of lower

cost coal which in turn was used by

comparable steam engines in a host of

applications. One important application

was to pump air into blast furnaces,

thereby increasing the blast

temperatures, reducing the quantity of

coal needed to make iron and reducing

the cost of iron (Ayres, 2002). Lower cost

iron, in turn, reduced the cost of steam

engines, creating a positive feedback

cycle. It also contributed to the

development of railways, which lowered

the cost of transporting coal and iron,

thereby increasing demand for both.

Jevons highlighted the fact that

improvements in the thermodynamic

efficiency of steam engines were

intertwined with broader technical

change, including: “.... contrivances, such

as the crank, the governor, and the minor

mechanism of an engine, necessary for

regulating, transmitting, or modifying its

power....” (Jevons, 1865).41 These

developments were essential to the

increased use of steam engines as a

source of motive power and demonstrate

how improvements in thermodynamic

efficiency are frequently linked to broader

improvements in technology and total

factor productivity.

More recently, Rosenberg (1989) has cited

the comparable example of the Bessemer

process for steelmaking:

“[the Bessemer process] was one of the

most fuel saving innovations in the history

of metallurgy [but] made it possible to

employ steel in a wide variety of uses that

were not feasible before Bessemer,

bringing with it large increases in

demand. As a result, although the process

sharply reduced fuel requirements per

unit of output, its ultimate effect was to

increase....the demand for fuel.”

(Rosenberg, 1989) 

The low cost Bessemer steel initially found

a large market in the production of steel

rails, thereby facilitating the growth of the

rail industry, and later in a much wider

range of applications including

automobiles. However, the mild steel

produced by the Bessemer process is a

very different product to wrought iron

(which has a high carbon content) and is

suitable for a much wider range of

applications. Hence, once again, the

improvements in the thermodynamic

efficiency of production processes are

deeply entwined with broader

developments in process and product

technology.

These examples relate to energy

efficiency improvements in the early

stages of development of energy intensive

process technologies and intermediate

goods that have the potential for

widespread use in multiple applications. It

41 Another factor was the development of high pressure boilers after 1840 (Hills, 1989). Compared to the early Newcomen

engine, the Watt engine used only 2/5 of the amount of coal per unit of output, while compared to the early Watt engine, high

pressure steam turbines in the earlier 20th-century used only one sixth (Crafys, 2003). According to Atack (1979), the annual

cost of a horsepower of steam fell by just over 80% between the 1820s and the 1890s.
is possible that the same consequences

may not follow for energy efficiency

improvements in mature and/or nonenergy intensive process technologies and

intermediate goods that have a relatively

narrow range of applications. Similarly,

the same consequences may not follow

from improvements in consumer

technologies that supply energy services

with a low own-price elasticity and where

energy represents only a small share of

total costs. (Ayres, 2002). The relevance

and importance of these distinctions is

discussed further below.

A historical perspective on rebound effects

is provided by Fouquet and Pearson

(2006), who present some remarkable

data on the price and consumption of

lighting services in the UK over a period of

seven centuries (Table 5.1). Per capita

consumption of lighting services grew

much faster than per capita GDP

throughout this period, owing in part to

continuing reductions in the price of

lighting services (£/lumen hour). This, in

turn, derived from continuing

improvements in the thermodynamic

efficiency of lighting technology, in

combination with continuing reductions in

the real price of lighting fuel (itself, partly

a consequence of improvements in the

thermodynamic efficiency of energy

supply). In this case, improvements in

lighting technology were substantially

more important than improvements in

energy supply (in the ratio of 180 to 1

over the period 1800 to 2000). 

Per capita lighting consumption increased

by a factor of 6566 between 1800 and

2000, largely as a consequence of the

falling cost of lighting services relative to

income, but also as a result of the boost

to per capita GDP provided by the

technical improvements in lighting

technology. Since lighting efficiency

improved by a factor of 1000, the data

suggest that per capita energy

consumption for lighting increased by a

factor of six. In principle, the direct

rebound effect could be estimated by

constructing a counterfactual scenario in

which lighting efficiencies remained at

1800 levels. But this would be a

meaningless exercise over such a time

interval, given the co-evolution and

interdependence of the relevant variables

and the interrelationship between energy

consumption and economic growth (Box

5.2). To the extent that the demand for

lighting is approaching saturation in many

OECD countries, future improvements in

lighting efficiency may be associated with

smaller rebound effects. Nevertheless,

this historical perspective gives cause for

concern over the potential of technologies

such as compact fluorescents to reduce

energy consumption in developing

countries.

5.2 Energy productivity and

economic growth 

Despite their far-reaching implications,

Jevons’ ideas were neglected until

comparatively recently and contemporary

advocates of energy efficiency are

frequently unaware of them. While a 1980

paper by Daniel Khazzoom stimulated

much research and debate on the direct

rebound effect (Besen and Johnson,

1982; Einhorn, 1982; Henly, et al., 1988;

Lovins, et al., 1988), most researchers

ignored the long-term, macroeconomic

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The Rebound Effect: an assessment of the evidence for economy-wide energy savings from improved energy efficiency 

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Year Price of Lighting Price of Consumption Total Real

lighting efficiency lighting of light per consumption GDP per 

fuel services capita of light capita

1300 1.50 0.50 3.0 - - 0.25

1700 1.50 0.75 2.0 0.17 0.1 0.75

1750 1.65 0.79 2.1 0.22 0.15 0.83

1800 1.0 1 1 1 1 1

1850 0.40 4.4 0.27 3.9 7 1.17

1900 0.26 14.5 0.042 84.7 220 2.9

1950 0.40 340 0.002 1528 5000 3.92

2000 0.18 1000 0.0003 6566 25630 15

Table 5.1 Seven centuries of lighting in the UK 

Note: 1800=1.0 for all indices Source: Fouquet and Pearson (2006)

Numerous studies have demonstrated strong correlations between economic output and energy

consumption at different levels of aggregation and over different periods of time. But this leaves

open the question of to what extent the growth in economic output can be considered a cause of

the increased energy consumption, and to what extent the growth in energy inputs can be

considered a cause of the increased economic output. Alternatively there could be a synergistic

relationship between the two, with each causing the other as part of a positive feedback

mechanism (Ayres and Warr, 2002).

The conventional wisdom (as represented by both neoclassical and ‘endogenous’ growth theory)

is that increases in energy inputs play a relatively minor role in economic growth, largely because

energy accounts for a relatively small share of total costs (Jones, 1975; Denison, 1985; Gullickson

and Harper, 1987; Barro and Sala-I-Martin, 1995). Economic growth is assumed to result instead

from the combination of increased capital and labour inputs, changes in the quality of those inputs

and increases in total factor productivity that are frequently referred to as technical change.

However, this view has been contested by ecological economists, who argue that the increased

availability of high quality energy inputs has been the primary driver of economic growth over the

last two centuries (Cleveland, et al., 1984; Beaudreau, 1998; Kummel, et al., 2000). According to

this view, the productivity of energy inputs is substantially greater than is suggested by its small

share of total costs. 

The conventional and ecological perspectives reflect differing assumptions and are supported by

conflicting empirical evidence. A difficulty with both is that they confine attention to the

relationship between energy consumption and economic growth. But the reason that energy is

economically significant is that it is used to perform useful work - either in the form of mechanical

work (including electricity generation) or in the production of heat (Ayres and Warr, 2005). More

useful work can be obtained with the same, or less, energy consumption through improved energy

efficiency. Hence, if increases in energy inputs contribute disproportionately to economic growth,

then improvements in energy efficiency may do the same. Conversely, if increases in energy

inputs contribute little to economic growth, then neither should improvements in energy

efficiency. This suggests that differing views over the size of the economy-wide rebound effect

may partly reflect differing views over the relationship between energy and economic growth.

Box 5.2 Energy consumption and economic growth - neoclassical and ecological perspectives 
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implications that were Jevons’ primary

concern. However, Jevons’ arguments

have been taken up with some vigour by

the British economist, Len Brookes, who

has developed coherent arguments in

favour of the K-B postulate and combined

these with critiques of government energy

efficiency policy (Brookes, 1978; 1984;

1990; 2000; 2004). Brookes work has

prompted a fierce response from critics

(Grubb, 1990; Herring and Elliot, 1990;

Toke, 1990; Grubb, 1992), to which

Brookes has provided a number of robust

responses (Brookes, 1992; 1993).42

Brookes (2000) argues that “...The claims

of what might be called the Jevons school

are susceptible only to suggestive

empirical support”, since estimating the

macroeconomic consequences of

individual improvements in energy

efficiency is practically impossible. He

therefore relies largely on theoretical

arguments, supported by indirect sources

of evidence, such as historical correlations

between various measures of energy

efficiency, total factor productivity,

economic output and energy consumption

(Schurr, 1984; 1985). A key argument

runs as follows: 

“...it has been claimed since the time of

Jevons (1865) that the market for a more

productive fuel is greater than for less

productive fuel, or alternatively that for a

resource to find itself in a world of more

efficient use is for it to enjoy a reduction

in its implicit price with the obvious

implications for demand.”

However, Brookes’ use of the term

‘implicit price’ is confusing. Individual

energy efficiency improvements do not

change the price of input energy, but

instead lower the effective price of output

energy, or useful work. For example,

motor-fuel prices may be unchanged

following an improvement in vehicle fuel

efficiency, but the price per vehicle

kilometre is reduced. The ‘obvious

implications’ therefore relate to the

demand for useful work, and not to the

demand for energy commodities

themselves. While the former may be

expected to increase, energy demand

may either increase or decrease

depending upon the price elasticity of

demand for useful work and the

associated indirect rebound effects. 

Of course, the combined impact of

multiple energy efficiency improvements

could lower energy demand sufficiently to

reduce energy prices and thereby

stimulate a corresponding increase in

economy-wide energy demand. This

forms one component of the economywide rebound effect. But while it is

obvious that the overall reduction in

energy consumption will be less than

microeconomic analysis suggests, this

theoretical argument appear to be an

insufficient basis for claiming that backfire

is inevitable.

Brookes also criticises the assumption

that energy service demand will remain

fixed while the marginal cost of energy

services falls under the influence of raised

energy efficiency, and the related

assumption that individual energy savings

can be added together to produce an

42 One of these critics, Horace Herring, is now more sympathetic to the K-B postulate (Herring, 2006)
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67

estimate of what can be saved over the

economy as a whole. In both cases,

Brookes is highlighting the persistent

neglect of both direct and indirect

rebound effects in the conventional

assessment of energy efficiency

opportunities. However, arguing that the

economy-wide rebound effect is greater

than zero is different from arguing that it

is greater than one – as the K-B postulate

suggests. 

Brookes marshals a number of other

arguments in support of the K-B postulate

that appear more amenable to empirical

test. In doing so, he highlights some

important issues regarding the

relationship between energy consumption

and economic growth. The three most

important arguments may be

characterised as follows:

• The productivity argument: The

increased use of higher quality forms

of energy (especially electricity) has

encouraged technical change,

substantially improved total factor

productivity and driven economic

growth. Despite the substitution of

energy for other inputs, this technical

change has stimulated a sufficiently

rapid growth in economic output that

aggregate energy efficiency has

improved at the same time as

aggregate energy consumption has

increased.43 This pattern may be

expected to continue in the future.

The productivity argument rests upon

two separate, but related sources of

empirical evidence. First, the work of

Sam Schurr and colleagues on the

historical importance of changes in

energy quality (notably electrification)

in driving productivity growth (Schurr,

et al., 1960; Schurr, 1984; 1985).

Second, the work of Jorgenson and

others on the direction of technical

change (Jorgenson, 1984; Hogan and

Jorgenson, 1991). Contrary to

standard assumptions, Jorgenson’s

results suggest that, at the level of

individual sectors, the contribution of

technical change has been to increase

energy intensity over time, rather

than reduce it.44 This work is also

cited as suggestive evidence for the KB postulate by Saunders (1992).

• The endogeneity argument: A

common approach to quantifying the

‘energy savings’ from energy

efficiency improvements is to hold

energy intensity fixed at some historic

value and estimate what consumption

‘would have been’ in the absence of

those improvements (Geller, et al.,

2006). The energy savings from

energy efficiency improvements are

then taken to be the difference

between the actual demand and the

counterfactual scenario. But if the

energy efficiency improvements are a

necessary condition for the growth

in economic output, the construction

43Between 1918 and 1973, the US economy experienced substantial increases in both total factor productivity and energy

efficiency, while energy prices fell in real terms. Even though energy use increased relative to labour and capital, the increased

use of more flexible forms of energy (oil and electricity) enhanced the productivity of capital and labour sufficiently that energy

use per unit of output fell (Schurr, 1982). 

44 In their original study, Jorgenson and Fraumeni (1981) estimated the rate of change in the share of energy costs in the value

of output of US manufacturing sectors, holding input prices constant. Contrary to conventional wisdom, Jorgenson and Fraumeni

found ‘energy-using’ technical change in 29 out of 35 sectors. Generally, we would expect energy saving (using) technical change

to be associated with falling (increasing) energy intensity. However, this may not always be the case, since it also depends upon

the rate of change in total factor productivity (Sanstad, et al., 2006). With energy-using technical change a decrease (increase)

in the price of energy will raise (lower) total factor productivity.
of a counterfactual in this way is

misconceived.45 The endogeneity

argument is not developed in detail by

Brookes, but raises questions over the

use of ‘decomposition analysis’46 to

explore the rebound effect (Schipper

and Grubb, 2000).

• The accommodation argument:

Energy efficiency improvements are

claimed to ‘accommodate’ an energy

price shock so that the energy

supply/demand balance is struck at a

higher level than if energy efficiency

had remained unchanged (Brookes,

1984). While not immediately

obvious, this argument rests on the

assumption that the income elasticity

of ‘ useful’ energy demand falls

steadily as an economy develops, but

is always greater than unity (Brookes,

1972). ‘Useful’ energy consumption is

a quality-adjusted measure of

aggregate energy consumption in

which different energy types are

weighted by their relative economic

productivities (Adams and Miovic,

1968).

Technical Report 5 describes the historical

research that forms the basis for these

arguments, summarises how Brookes

uses this research to support his case,

identifies potential empirical and

theoretical weaknesses and examines in

detail whether more recent research

confirms or contradicts Brookes’ claims. It

points to a number of flaws, both in the

evidence itself and in the manner in which

Brookes uses this evidence to support his

case. Specific criticisms include the

following:

• Schurr’s work applies primarily to the

causal effect of shifts to higher quality

fuels (notably electricity), rather than

improvements in thermodynamic

conversion efficiency or other factors

that affect aggregate measures of

energy efficiency. The effect of the

latter on total factor productivity may

not be the same as the effect of the

former. Also, the patterns Schurr

uncovered may not be as ‘normal’ as

Brookes suggests and the link

between energy efficiency

improvements and improvements in

total factor productivity appears to

vary greatly, both over time and

between different sectors and energy

services.

• Neither Jorgenson’s work itself, nor

those of comparable studies

consistently find technical change to

be ‘energy-using’. Instead, the

empirical results vary widely between

different sectors, countries and time

periods and are sensitive to minor

changes in econometric specification

(Norsworthy, et al., 1979; Roy, et al.,

1999; Welsch and Ochsen, 2005;

Sanstad, et al., 2006). Jorgenson’s

results rest on the erroneous

assumption that the rate and direction

of technical change is fixed, and more

sophisticated models suggest that the

magnitude and sign of technical

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68

45 Technically, economic output and energy efficiency should be considered endogenous variables, since each influences the

other. Conventional methodological approaches to estimating historical ‘energy-savings’, such as decomposition analysis, fail to

do this.

46 Decomposition analysis expresses trends in aggregate quantities as the product of a number of different variables. For

example, economy-wide energy consumption may be expressed into the product of population, GDP per capita and energy use

per unit of GDP. An additive decomposition expresses the change in energy use over a particular period as the sum of the change

in each of the right-hand side variables, while a multiplicative decomposition expresses the ratio of energy use at the end of the

period to that at the beginning of the period as the product of comparable ratios for each of the right-hand side variables. Similar

expressions can be developed at varying levels of detail for energy use within individual sectors. Thanks in part to the work of

Lee Schipper and colleagues, decomposition analysis has become a widely used tool within energy economics (Ang, 1999).
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69

change varies between sectors and

types of capital as well as over time

(Sue Wing and Eckaus, 2006). Also,

the failure to check for the presence of

‘cointegration’47 in the data or to

account for changes in energy quality

means that some of the estimates

could be either biased or spurious

(Kaufmann, 2004). Moreover, even if

energy-using technical change were

to be consistently found, the

relationship between this finding and

the K-B postulate remains unclear.48

• The endogeneity argument is

rhetorically persuasive but lacks a

firm empirical basis. The relative

importance of energy efficiency

improvements (however defined)

compared to other forms of technical

change in encouraging economic

growth remains to be established.

• The ‘accommodation’ argument is

based upon a highly simplified

theoretical model of the world

economy (Brookes, 1984), which is

both unconventional in approach and

difficult to interpret and calibrate. The

model rests on the assumption that

the income elasticity of ‘useful’ energy

demand is always greater than unity,

thereby allowing economic output to

be represented as a linear function of

useful energy inputs. An earlier study

by Brookes (1972) provides some

support for this hypothesis, but this

has not been updated. Contemporary

research on ‘Environmental Kuznets

Curves’ has generally not tested this

hypothesis, since it aggregates

energy consumption on the basis of

thermal content (Stern, 2004c;

Richmond and Kaufmann, 2006b;

2006a). However, analysis by Dargay

(1992) suggests that the income

elasticity of useful energy demand has

declined from above unity in the

1960s to around 0.7 in 1990, which

contradicts Brookes’ claims.

In sum, not only does each of the sources

of evidence have empirical and theoretical

weaknesses, but the extent to which they

(individually and collectively) support the

K-B postulate is open to question. Hence,

while Brookes has highlighted some

important issues and pointed to sources of

evidence that challenge conventional

wisdom, he has not provided a convincing

case in support of the K-B postulate.

Perhaps the most important insight from

Brookes’ work is that improvements in

energy productivity are frequently

associated with improvements in the

productivity of capital, labour and

materials. In particular, historical

experience suggests that improvements in

47 The presence of cointegration between two or more variables implies that one variable cannot move ‘too far’ away from

another, because there is a long-term relationship between them. This may be because one variable ‘causes’ the other, or that

they are both driven by a third, possibly omitted, variable. Cointegration analysis seeks to identify this relationship by detecting

whether the irregular trends in a group of variables are shared by the group, so that the total number of unique trends is less

than the number of variables. Failing to allow for cointegration can allow the precision of relationships to be overestimated and

may also lead to incorrect signs (Lim and Shumway, 1997).

48 Jorgenson’s work suggests that the contribution of technical change has frequently been to reduce energy efficiency and

thereby increase overall energy consumption, even while other factors (such as structural change) are acting to decrease it.

Hence, not only is the direction of technical change opposite to what is conventionally assumed, but also opposite to what is

required for an empirical estimate of the rebound effect. But technical change has clearly improved the thermodynamic

conversion efficiency of individual devices, such as motors and boilers. What Jorgenson’s work suggests, therefore, is this that

has not necessarily translated into improvements in more aggregate measures of energy intensity at the level of industrial

sectors. Similarly, the more robust results of Sue Wing and Eckhaus (2006) suggest that this has not necessarily translated into

improvements in more aggregate measures of energy intensity for particular types of capital (e.g. machinery). Hence, the

relevance of these results may hinge upon the appropriate choice of independent variable for the rebound effect.
energy productivity have been associated

with proportionally greater improvements

in total factor productivity. While Schurr’s

work provides evidence for this at the

level of the national economy, numerous

examples from the energy efficiency

literature provide comparable evidence at

the level of individual sectors and

technologies (Box 5.3). Such examples

are frequently used by authors such as

Lovins (1997) to support the business

case for energy efficiency, but they also

point to situations where rebound effects

may be expected to be large (Saunders,

2000b). If energy efficient technologies

boost total factor productivity and thereby

save more than energy costs alone, the

argument that rebound effects must be

small because the share of energy in total

costs is small is undermined. Much the

same applies to the contribution of energy

efficiency improvements to economic

growth. But this leaves open the question

of whether energy efficiency

improvements are necessarily associated

with proportionally greater improvements

in total factor productivity, or whether (as

seems more likely) this is contingent upon

particular technologies and

circumstances. If the latter is the case,

policy measures could potentially be

targeted on ‘dedicated’ energy efficiency

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70

• Lovins and Lovins (1997) used case studies to argue that better visual, acoustic and

thermal comfort in well-designed, energy efficient buildings can improve labour

productivity by as much as 16%. Since labour costs in commercial buildings are

typically 25 times greater than energy costs, the resulting cost savings can

potentially dwarf those from reduced energy consumption.

• Pye and McKane (1998) showed how the installation of energy efficient motors

reduced wear and tear, extended the lifetime of system components and achieved

savings in capital and labour costs that exceeded the reduction in energy costs.

• Sorrell et al. (2004) found a host of examples of the ‘hidden benefits’ of energy

efficiency improvements within 48 case studies of organisational energy

management. For example changes to defrosting regimes at a brewery led to energy

savings, water savings, reduced maintenance and reduced deterioration of building

fabric.

• Worrell et al. (2003) analysed the cost savings from 52 energy efficiency projects,

including motor replacements, fans/duct/pipe insulation, improved controls and heat

recovery in a range of industrial sectors. The average payback period from energy

savings alone was 4.2 years, but this fell to 1.9 years when the non-energy benefits

were taken into account.

• Using plant-level data, Boyd and Pang (2000) estimated fuel and electricity intensity

in the glass industry as a function of energy prices, cumulative output, a time trend,

capacity utilisation and overall productivity. Their results show that the most

productive plants are also most energy efficient and that a 1% improvement in

overall productivity results in a more than 1% improvement in energy efficiency. 

Box 5.3 Examples from the energy efficiency literature of the link between

improved energy efficiency and improved total factor productivity
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71

improvements that have smaller impacts

on total factor productivity and hence

smaller rebound effects.

5.3 Energy productivity and

production theory 

Harry Saunders is the second major

advocate of the ‘Khazzoom-Brookes

postulate’ and has brought a new level of

sophistication to the rebound debate by

basing his arguments upon neoclassical

production and growth theory. This work

is abstract and theoretical and is

necessarily based on highly restrictive

assumptions that can be a focus of

criticism. But Saunders does not claim

that his results prove the K-B postulate,

but merely provide suggestive evidence in

its favour, given certain assumptions

about how the economy operates. Most

importantly, he shows how backfire is the

predicted outcome of standard economic

models. 

Saunders (1992) uses the neoclassical

growth model to argue that backfire is a

likely outcome of ‘pure’ energy efficiency

improvements - that is, a form of

technical change that improves energy

productivity while not affecting the

productivity of other inputs. He also

argues that improvements in capital,

labour or materials productivity will

increase overall energy consumption.

Since technical change typically improves

the productivity of several inputs

simultaneously, Saunders argues that

most forms of technical change will

increase overall energy consumption.

Saunders’ use of the neoclassical growth

model was subsequently challenged by

Howarth (1997), who argued that the

failure to distinguish between energy and

energy services led to the probability of

backfire being overestimated. However,

Saunders (2000a) subsequently

demonstrated that backfire is still

predicted by the neoclassical model when

an alternative choice is made for the

production function49 used to provide

energy services. In a more recent

contribution, Saunders (2007) focuses on

the potential of different types of

production function to generate backfire.

Unlike Saunders (1992), this work is also

applicable to individual firms and sectors

and opens up the possibility of using

empirically estimated production functions

to estimate the rebound effect from

particular technologies in particular

sectors (Saunders, 2005). Box 5.4

provides a very simple illustration of

Saunders’ approach and contrasts this

with an alternative approach by Laitner

(2000). 

Saunders (2007) shows how the predicted

magnitude of rebound effects depends

almost entirely on the choice of the

relevant production function – whether at

the firm, sector or economy-wide level.

Several commonly used production

functions are found to be effectively

useless in investigating the rebound

49 Production functions are normally represented by functional forms which are intended to approximate the relationship

between inputs and outputs. Their purpose is to define a set of parameters which, given the observed data, reasonably

approximate real-world production behaviour. This is useful in understanding the rate at which agents within an economy can

feasibly (or have historically) moved between alternative input combinations. There are a range of different functional forms

available and the appropriate choice involves trade-offs between flexibility and analytical tractability. 
effect, since the relevant results are the

same for whatever values are chosen for

key parameters. One popular production

function (the constant elasticity of

substitution, or CES), is found to be able

to simulate rebound effects of different

magnitudes, but only if a particular

assumption is made about how different

inputs are combined.50 Since this form is

widely employed within energy-economic

models, Saunders’ results raise serious

concerns about the ability of such models

to accurately simulate rebound effects. An

alternative and more flexible functional

form (the ‘translog’) that is widely used in

empirical studies is also found to lead to

backfire once standard restrictions are

imposed on the parameter values to

ensure that the behaviour of the function

is consistent with economic theory

(Saunders, 2007)51.

There is a substantial empirical literature

estimating the parameters of different

types of production function at different

levels of aggregation and obtaining a good

fit with observed data. Hence, if such

functions are considered to provide a

reasonable representation of real-world

economic behaviour, Saunders’ work

suggests that ‘pure’ energy-efficiency

improvements are likely to lead to

backfire. Alternatively, if rebound effects

are considered to vary widely in

magnitude between different sectors,

Saunders’ work suggests that standard

and widely used economic methodologies

cannot be used to simulate them. 

The above conclusions apply to pure

energy efficiency improvements. But

Saunders (2005) also uses numerical

simulations to demonstrate the potential

for much larger rebound effects when

improvements in energy efficiency are

combined with improvements in the

productivity of other inputs. Again, if the

validity of the theoretical assumptions is

accepted, these results suggest that

backfire may be a more common outcome

than is conventionally assumed. 

Saunders approach is entirely theoretical

and therefore severely limited by the

assumptions implicit in the relevant

models. For instance, technology always

comes free, there are only constant

returns to scale in production, markets

are fully competitive, there is always full

employment, qualitative differences in

capital and energy are ignored and so on.

Indeed, a considerable literature

challenges the idea that an ‘aggregate’

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72

50 The CES function used by Saunders combines inputs into pairs, or ‘nests’. For example, a nested production function with

capital (K), labour (L) and energy (E) inputs, could take one of three forms, namely: K(LE); (KL)E; (KE)L. Saunders (1992) shows

that the (KL)E form permits a range of values for the rebound effect (depending upon the elasticity of substitution between

energy and capital/labour), while the other forms always lead to backfire. However, despite being in widespread use, this type

of function imposes very restrictive conditions on real-world behaviour that are not supported by empirical evidence (Frondel and

Schmidt, 2004).

51 Restrictions normally have to be imposed upon the parameter values in a translog cost function to ensure that its behaviour

is consistent with basic economic theory. In particular, the cost function must be concave - implying that the marginal product

of each input declines with increasing use of that input. In many applications, such as CGE modelling, these conditions need to

be satisfied for all input combinations, but empirically estimated cost functions sometimes violate these conditions (Diewert and

Wales, 1987). However, Ryan and Wales (2000) show that if concavity is imposed locally at a suitably chosen reference point,

the restriction may be satisfied at most of the data points in the sample. Under these circumstances, the translog may be able

to represent different types of rebound effect for particular data sets.
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73

production function for the economy as a

whole is meaningful concept (Fisher,

1993; Temple, 2006) - although this may

not necessarily invalidate the use of such

functions for representing the behaviour

of individual sectors. A particular

weakness is the assumption that technical

change is costless and autonomous,

without explicit representation of the

processes that affect its rate and

direction. This characteristic limits the

capacity of such models to address many

policy-relevant questions. More recent

developments in so-called ‘endogenous

growth theory’ have overcome this

weakness to a large extent, but to date no

authors have used such models to explore

the rebound effect. However, since what is

at issue is the consequences of energy

efficiency improvements, the source of

those improvements is arguably a

secondary concern.

Overall, Saunders work suggests that

significant rebound effects can exist in

theory, backfire is quite likely and this

result is robust to different model

assumptions. Since these results derive

from a contested theoretical framework,

they are suggestive rather than definitive.

But they deserve to be taken seriously. 

5.4 Substitution between

energy and capital

A key conclusion from Saunders’ work is

as follows:

“It appears that the ease with which fuel

can substitute for other factors of

production (such as capital and labour)

has a strong influence on how much

rebound will be experienced. Apparently,

the greater this ease of substitution, the

greater will be the rebound” (Saunders,

2000, p. 443).

The parameter that measures this ‘ease’

of substitution is the so-called elasticity of

substitution between energy and other

inputs (σ). High values of the elasticity of

substitution between energy and other

inputs mean that a particular sector or

economy is more ‘flexible’ and may

therefore adapt relatively easily to

changes in energy prices. In contrast, low

values of the elasticity of substitution

between energy and other inputs suggest

that increases in energy prices may have

a disproportionate impact on productivity

and growth. The elasticity of substitution

is therefore a key parameter within

energy-economic models, leading

Saunders to suggest a possible trade off in

climate policy:

“...If one believes σ is low, one worries less

about rebound and should incline towards

programmes aimed at creating new fuel

efficient technologies. With low σ carbon

taxes are less effective in achieving a

given reduction in fuel use and would

prove more costly to the economy. In

contrast, if one believes σ is high, one

worries more about rebound and should

incline towards programmes aimed at

reducing fuel use via taxes. With high σ,

carbon taxes have more of an effect at

lower cost to the economy.” (Saunders,

2000b)

These observations suggest that a closer

examination of the nature, determinants

and typical values of elasticities of

substitution between energy and other

inputs could provide some insights into
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74

Much of Saunders work involves the use of calculus to investigate the behaviour of

neoclassical production functions. A very simple, but still widely used production function

is the ‘Cobb- Douglas’, which may be represented as follows:

Where: K=capital, L=labour, E=energy and α+β=1. The multiplier τ(τ≥1) increases the

productivity of energy inputs, so that the product τE represents ‘effective’ energy inputs.

This may be interpreted as a form of technical change that does not affect the

productivity of other inputs and is assumed to be costless. Saunders (2007) investigates

the effect of improvements in energy productivity (τ) on energy consumption and

economic output in the short-term, with real energy prices fixed. He finds that, with a

Cobb-Douglas function, both energy consumption and output increase by the same

amount, leaving the aggregate energy/output ratio unchanged. The improvement in

the productivity of energy inputs (τ) has therefore led to backfire, since economy-wide

energy consumption has increased. Using standard assumptions about the share of

energy in total costs, Saunders (2000a) estimates that a 20% increase in the

productivity of energy inputs should only increase GDP by some 2.3%. 

This approach can be compared with Laitner’s (2000) ‘back of the envelope’ estimate of

the effect of energy efficiency improvements on the US economy. This simply assumes

that energy efficiency policies would reduce the economy-wide energy/GDP ratio by

30%. As a result, the only rebound effects that Laitner considers are the effect of energy

efficiency improvements on GDP growth. Since the share of energy in total costs is

small, the additional economic growth stimulated by ‘pure’ energy efficiency

improvements should be insufficient to offset the energy savings achieved by the

reduction in the energy/GDP ratio. Laitner shows that this conclusion is unchanged when

possible changes in energy prices are also taken into account.

Both authors agree, therefore, that ‘pure’ energy efficiency improvements should have

a relatively small impact on GDP. But while Laitner uses this result to argue that backfire

is unlikely, Saunders position is that backfire is likely to be the norm.

These conflicting conclusions result in part from differences in approach, and in part

from different definitions of the independent variable. Saunders uses an improvement in

energy productivity as the independent variable and derives a result in which the

economy-wide energy/GDP ratio remains unchanged (i.e. energy consumption and

economic output increase by the same amount). Laitner, in contrast, uses the economywide energy/GDP ratio as the independent variable and simply assumes that energy

efficiency policies will reduce this by 30% - thereby ignoring any ‘lower-level’ rebound

effects from these policies. A criticism of Saunders approach could be that the real-world

economy is unlikely to behave in the manner suggested by a Cobb-Douglas production

function. But Saunders (2007) shows that several alternative and more flexible

assumptions about the form of the production function also lead to backfire. In contrast,

a criticism of Laitner’s approach could be that the assumption that energy efficiency

policies will reduce the energy/GDP ratio by this amount is flawed.

Hence, while both approaches are internally consistent, their results are driven by

conflicting theoretical assumptions that require empirical validation.

Box 5.4 How theoretical assumptions can determine results
the likely magnitude of rebound effects in

different sectors and for the economy as a

whole. Of particular interest is the

elasticity of substitution between energy

and capital, since many types of energy

efficiency improvement may be

understood as the substitution of capital

for energy. Technical Report 3 therefore

provides an in-depth examination of

empirical estimates of the elasticity of

substitution between energy and capital,

together with an extensive review of the

associated theoretical issues. The

empirical literature on this subject turns

out to be confusing and contradictory,

with more than three decades of empirical

research failing to reach a consensus on

whether energy and capital may be

considered as ‘substitutes’ or

‘complements’ (Box 5.5 and 5.6).

Moreover, the relationship between the

elasticity of substitution and the rebound

effect turns out to be far from

straightforward.

Our investigation of this topic reveals that

Saunders’ statements regarding the

relationship between elasticities of

substitution and the rebound effect are

potentially misleading. The relationship

depends very much upon the distinction

between energy and energy services, the

particular choice of production function,

the appropriate definition of the elasticity

of substitution and the validity of the

assumption of ‘separability’ between

energy services and other inputs (Box

5.5). When these are taken into account,

it is found that large rebound effects may

occur even when the elasticity of

substitution between energy and capital is

low, which appears to contradict the

observation quoted at the beginning of

this section. The possible trade-off in

climate policy that is suggested by

Saunders may therefore not exist. In

addition, since most empirical studies

measure something quite different from

the parameters assumed within energyeconomic models, the empirical basis for

those models is further called into

question (Box 4.4).

To the extent that a general conclusion

can be drawn from the empirical

literature, it is that energy and capital

typically appear to be either complements

or weak substitutes (Box 5.6). This could

have some important implications. First, a

reduction in the price of capital relative to

energy (e.g. through investment

subsidies) may in some circumstances

increase energy consumption rather than

reduce it (Berndt and Wood, 1979).

Second, the economic impact of an

increase in energy prices could be

significant:

“A reduction in the use of energy by itself

will have a relatively small economic

impact, determined to first order by

energy’s small value share. But if the

reduced use of energy also produces a

reduction in the use of capital, the larger

value share of capital applies and the

economic impact is magnified. This

indirect effect through capital can be the

largest component of the economic

impact of reduced energy use... but this

effect is often ignored in economic impact

analyses of energy policy” (Hogan, 1979)

Hence, this review suggests the possibility

of a strong link between energy

consumption and economic output as well

as potentially high costs associated with

reducing energy consumption. However,

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76

There are at least five definitions of the elasticity of substitution in common use and several

others that appear less frequently. The lack of consistency in the use of these definitions and

the lack of clarity in the relationship between them, combine to make the empirical literature

both confusing and contradictory (Stern, 2004a). 

For all definitions, substitution between two inputs is ‘easier’ when the magnitude of the

elasticity of substitution between them is greater, while the sign of the elasticity of substitution

is commonly used to classify inputs as ‘substitutes’ or ‘complements’. But the appropriate

classification depends upon the particular definition being used (i.e. inputs may be substitutes

under one measure and complements under another). 

The majority of existing empirical studies use the sign of the ‘Allen-Urzwa’ elasticity of

substitution (AES) to make this classification. With this definition, two inputs are described as

substitutes (complements) when the usage of one increases (decreases) when the price of the

other increases (decreases), holding output constant. However this measure has a number of

acknowledged drawbacks and its quantitative value lacks meaning (Frondel, 2004). In many

cases, the Cross Price elasticity (CPE) or the Morishima elasticity of substitution (MES) would

be more appropriate measures, but these have yet to gain widespread use.

A large number of empirical studies estimate elasticities of substitution between different

inputs within different sectors and countries and over different time periods. These rely upon

a variety of assumptions, including in particular the specific form of the production or cost

function employed. In general, the actual scope for substitution may be expected to vary

widely between different sectors, different levels of aggregation and different periods of time,

while the estimated scope for substitution may depend very much upon the particular

methodology and assumptions used.

Standard methodological approaches frequently assume that the ease of substitution between

two inputs is unaffected by the level or price of other inputs (‘separability’). This assumption

is not always tested, and even if it is found to hold, the associated estimates of the elasticity

of substitution between two inputs in the same group could still be biased (Frondel and

Schmidt, 2004). Assumptions about the nature and bias of technical change may also have a

substantial impact on the empirical results, but distinguishing between technical change and

price-induced substitution is empirically challenging. 

The level of aggregation of the study is also important, since a sector may still exhibit input

substitution in the aggregate due to changes in product mix, even if the mix of inputs required

to produce a particular product is relatively fixed. Many studies overlook such changes and

implicitly assume that the product mix is fixed (Miller, 1986). Since the scope for changing

product mix is greater at higher levels of aggregation, the estimated scope for input

substitution may also be greater when higher levels of aggregation are used (Solow, 1987).

However, individual inputs cannot always be considered as independent, notably because

energy is required for the provision of labour and capital. Substitution of capital for energy and

one sector, for example, may lead to an increase in energy consumption in the sector providing

the relevant capital. This suggests that the estimated scope for input substitution may be

smaller when higher levels of aggregation are used. These two factors may therefore partly

cancel each other out.

Box 5.5 Defining and measuring elasticities of substitution
Technical Report 3 reviews more than 200 empirical estimates of the elasticity of

substitution between energy and capital. The results were analysed to see how the

estimates varied with factors such as the sectors covered, the functional form employed,

the use of time-series versus cross-sectional data and the assumptions regarding

technical change. The most striking result from the analysis is the lack of consensus that

has been achieved to date, despite three decades of empirical work. While this may be

expected if the degree of substitutability depends upon the sector, level of aggregation

and time period analysed, it is notable that several studies reach different conclusions

for the same sector and time period, or for the same sector in different countries.

If a general conclusion can be drawn, it is that energy and capital typically appear to be

either complements (i.e. AES<0) or weak substitutes (i.e. 0<AES<0.5). However, little

confidence can be placed in this conclusion, given the diversity of the results and their

apparent dependence upon the particular specification and assumptions used. While

there appears to be some agreement on the possible causes of the different results,

there is no real consensus on either the relative importance of different causes or the

likely direction of influence of each individual cause (i.e. whether a particular

specification/assumption is likely to make the estimate of the substitution elasticity

bigger or smaller).

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77

Overall summary of results

Box 5.6 Empirical estimates of the elasticity of substitution

between energy and capital

World UK US

Complements

Substitute <1

Substitute >1

given the extent to which the estimated

scope for substitution varies between

different sectors and levels of aggregation

(not to mention between different fuels

and types of capital), broad conclusions of

this type could be misleading. Moreover,

given the difficulties with the empirical

literature, very little confidence can be

placed in this result.

5.5 Energy productivity and

ecological economics

In his 1984 paper, Brookes quotes Sam

Schurr’s observation that: “....it is energy

that drives modern economic systems

rather than such systems creating a

demand for energy.” (Brookes, 1984).

This highlights an important theme in

Brookes’ work: namely that energy plays
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a more important role in economic growth

than is conventionally assumed by

neoclassical economists. But precisely the

same claim is made by ecological

economists such as Cleveland, et al.

(1984), who attribute a large component

of the increased productivity over the past

century to the increasing availability of

high-quality energy sources. This leads

them to express scepticism over the scope

for decoupling economic growth from

increased energy consumption.

Ecological economists have not directly

investigated the rebound effect, but their

work arguably provides suggestive

support for the K-B postulate in much the

same way as Schurr’s research on the

historical determinants of US productivity

growth. Four examples of this work are

briefly described below.

First, analysis by Kaufmann (1992; 2004)

and others suggests that historical

reductions in energy/GDP ratios owe

much more to structural change and

improvements in energy quality than to

technological improvements in energy

efficiency (Box 5.7). By neglecting

changes in energy quality, conventional

analysts may have come to incorrect

conclusions regarding the rate and

direction of technical change and its

contribution to reduced energy

consumption. Kaufmann (1992) suggests

that, not only does the energy/GDP ratio

reflect the influence of factors other than

energy-saving technical change, but these

other factors may be sufficient to explain

the observed trends. Hence, the observed

improvements in the thermodynamic

efficiency of individual devices at the

micro level do not appear to have

significantly contributed to the observed

reduction in energy intensity at the

macro-level. As with the work of

Jorgenson and others on energy-using

technical change, this suggests that the

conventional assumptions of energyeconomic models may be flawed.

Second, both neoclassical and ecological

economists have used modern

econometric techniques to test the

direction of causality between energy

consumption and GDP (Stern, 1993;

Chontanawat, et al., 2006; Lee, 2006;

Yoo, 2006; Zachariadis, 2006). If GDP

growth is the cause of increased energy

consumption then a change in the growth

rate should be followed by a change in

energy consumption and vice versa. It is

argued that if causality runs from GDP to

energy consumption then energy

consumption may be reduced without

adverse effects on economic growth, while

if causality runs the other way round a

reduction in energy use may negatively

affect economic growth. While the results

of such studies are frequently

contradictory, most of them neglect

changes in energy quality. When energy

quality is taken into account, the causality

appears to run from energy consumption

to GDP - as ecological economists suggest

(Stern, 1993; 2000).

Third, historical experience provides very

little support for the claim that increases

in income will lead to declining energy

consumption (Stern, 2004b; Richmond

and Kaufmann, 2006b; 2006a). While the

income elasticity of aggregate energy

consumption may be both declining and

less than one in OECD countries, there is

no evidence that it is negative (or is soon

to become negative). Again, neglect of

changes in fuel mix and energy prices
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79

Kaufmann (1992) sought to quantify the factors that contributed to changes in the ratio of primary

energy consumption (in kWh thermal) to real GDP in France, Germany, Japan and the UK during the

period 1950-1990. The explanatory variables were the percentage share of different energy carriers

in primary energy consumption; the fraction of GDP spent directly on energy by households; the

proportion of the product mix that originated in energy intensive manufacturing sectors; and primary

energy prices.

Despite the simplicity of this formulation, it was found to account for most of the variation in energy

intensity for the four countries studied throughout the post-war period. Kaufmann argued that

improvements in energy quality led to lower energy intensities by allowing more useful work to be

obtained from each heat unit of energy input. The shift from coal to oil contributed greatly to

declining energy/GDP ratios prior to 1973, while the rising contribution of primary electricity (hydro

and nuclear) provided a significant contribution after 1973.

Since the energy intensity of household energy purchases is an order of magnitude greater than the

energy intensity of other goods and services, falls in the former as a fraction of total expenditure

should translate into falls in the energy/GDP ratio – and vice versa. The fraction of GDP spent directly

on energy by households increased prior to 1973 and decreased thereafter and these trends were

also found to be highly significant in explaining trends in the aggregate ratio. 

In addition, changes in energy prices encouraged substitution between inputs, including the

substitution of capital for energy, while shifts towards less energy intensive manufacturing sectors

and towards the service sector reduced energy/GDP ratios. These mechanisms were found to be less

important than those above, but when all four factors were taken into account, they were found to

provide a more or less sufficient explanation for the observed trends in energy intensity.

By implication, Kaufmann’s results suggest little role for energy-saving technical change - defined as

advances in technology that allow the same type and quantity of output to be produced with less

energy inputs. Kaufmann tested this implication in three different ways,52 but in each case failed to

find statistically significant evidence for energy saving technical change. Kaufmann comments that

“....Technical changes has reduced the amount of energy (as measured in heat units) used to produce

a unit of output. But characterising that technical change as ‘energy-saving’ is misleading. Over the

last 40 years, technical change has reduced the amount of energy use to produce a unit of output

by developing new techniques for using oil, natural gas and primary electricity in place of coal.”

Kaufmann also interprets the results as illustrating the limited scope, at the level of the macroeconomy, for substituting capital and labour for energy. Estimated annually, the own price elasticity

of energy demand varies between -0.05 and -0.39, which is generally smaller than the elasticities

estimated at the level of individual sectors. This arguably suggests that the indirect energy

consumption associated with labour and capital inputs constitute a significant portion of the energy

saved directly through energy efficiency improvements in each of those sectors.

The results also indicate that reducing the fraction of GDP spent directly on energy by households,

may be the most effective way of reducing the energy/GDP ratio. This in turn suggests that rebound

effects from energy efficiency improvements may be lower in the household sector than in producing

sectors.

Box 5.7 Energy/GDP ratios and changes in energy quality

52 Namely: a) seeking evidence for serial correlation and heteroscedasticity in the error term, which could be evidence of missing variable

bias; b) including a time trend to represent energy-saving technical change; and c) using dummy variables to test for changes in the intercept

or slope of individual regression coefficients during different time periods - such as may follow an increase in energy prices if this induces

energy saving technical change.
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80

may have led earlier studies to draw

misleading conclusions regarding the role

and potential of energy saving technical

change (Kaufmann, 2004). 

Finally, ecological economists have

developed a number of alternatives to the

conventional models of economic growth

(Kummel, et al., 1985; Beaudreau, 1998;

Kummel, et al., 2000; Ayres and Warr,

2005). A key feature of these models is a

departure from the traditional assumption

that the productivity of each input is

proportional to the share of that input in

the value of output. Instead, the

productivity of each input is estimated

directly from a production function. These

models are found to reproduce historical

trends in economic growth extremely well,

without attributing any role to technical

change. This is in contrast to conventional

theories of economic growth, which

attribute much of the increase in output to

technical change.53 The marginal

productivity of energy inputs is found to

be around ten times larger than its cost

share, implying that improvements in the

productivity of energy inputs could have a

dramatic effect on economic growth and

therefore on economy-wide energy

consumption – in other words, the

rebound effect could be very large.

Of particular interest is the work by Ayres

and Warr (2005), who combine historical

data on the exergy content of fuel inputs

and thermodynamic (second-law)

conversion efficiencies (Table 5.2) to

develop a unique time series of the exergy

output of conversion devices (termed

useful work) in the US economy over the

past century. They show that useful work

inputs to the US economy have grown by

a factor of 18 over the past 100 years,

implying that the useful work obtained

from fuel resources has grown much

faster than the consumption of fuels

themselves, owing to substantial

improvements in thermodynamic

conversion efficiencies. This approach

makes a great deal of sense, since it

useful work that is economically

productive, while the exergy that is lost in

conversion processes is effectively wasted

(Ayres and Warr, 2006). 

By including useful work in their

production function, rather than primary

energy, Ayres and Warr obtain an

extremely good fit to US GDP trends over

the past century, thereby eliminating the

need for a multiplier for technical change.

The implication is that improvements in

thermodynamic conversion efficiency

provide a quantifiable surrogate for all

forms of technical change that contribute

to economic growth. Far from being a

minor contributor to economic growth,

improvements in thermodynamic

efficiency become the dominant driver –

obviating the need for alternative

measures of technological change.

While firmly outside mainstream

economics, the ecological perspective is

well articulated and persuasive. The

53 Traditional neoclassical growth models estimate ‘technical change’ as the residual growth in output that is not explained by

the growth of inputs. Early growth models attributed as much as 70% of the growth in output to technical change, but later

studies have shown how the proportion of growth that is attributed to technical change depends upon how the inputs are

measured (Jorgenson and Griliches, 1967). In neoclassical growth models, technical change is typically represented by a simple

time trend. Modern theories of economic growth seek to make the source and direction of technical change endogenous.
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81

general implication is that energy is more

productive than is suggested by its small

share of total costs. This is precisely the

argument that Schurr made and which

appears to underlie Brookes’ arguments in

favour of backfire. However, the empirical

evidence in support of this perspective

remains patchy and in some cases flawed.

For example, the results of econometric

investigations of causality relationships

between energy and GDP remain

ambiguous and the policy implications

that are drawn are frequently

oversimplified (Zachariadis, 2006). Also,

the statistical form of causality that is

being measured here (so-called ‘Granger

causality’) is not the same as causality as

conventionally understood54 and

conventional notions of causality may be

problematic for systems as complex as

modern economies. In a similar manner,

the different variants of ‘ecological growth

models’ rely upon an unusual and oddly

behaved production function55, provide

results that are difficult to reconcile with

each other56 and appear vulnerable to

bias from a number of sources57 that

could potentially invalidate the results. As

a result, claims that the marginal

productivity of energy is an order of

magnitude larger than its cost share, or

that improvement in thermodynamic

conversion efficiency can act as a suitable

proxy for technical change, must be

treated with considerable caution. 

Unfortunately, the different assumptions

of conventional and ecological

perspectives seem to have prevented an

objective comparison of their methods

and conclusions. Convincing evidence of

the disproportionate contribution of

energy to economic growth therefore

remains elusive. Moreover, even if this

were to be accepted, the link from this

evidence to the K-B postulate remains

ambiguous and indirect.

Year Electricity Transportation High Medium Low

generation temperature temperature temperature 

and process heat process heat space heat

distribution (steel) (steam)

1900 3.8 3.0 7 5 0.25

1970 32.5 8.0 20 14 2

1990 33.3 13.9 25 20 3

54 For example, a met office prediction of rain can be shown to ‘Granger cause’ rain!

55 The so-called LINEX production function implies increasing marginal returns and variable marginal productivities. 

56 Ayres and Warr (2005) claim that the inclusion of useful work rather than primary energy in the production function allows

them to dispense with a separate time trend to represent technical change. But they made no comment as to why Kummel

(1985; 2000) is able to reproduce economic growth without a time trend, while measuring energy inputs on the basis of thermal

content.

Table 5.2 Trends in second-law conversion efficiencies of primary conversion

processes in the US (average % efficiency in specified year)

Source: Ayres et al. (2003)
The neoclassical assumption appears to

be that capital, labour and energy inputs

have independent and additive effects on

economic output, with any residual

increase being attributed to exogenous

technical change. Endogenous growth

theory has modified these assumptions,

but still attributes a relatively minor role

to energy. In contrast, the ecological

assumption appears to be that capital,

labour and energy are interdependent

inputs that have synergistic and

multiplicative effects on economic output,

and that the increased availability of lowcost, high-quality energy sources provides

a necessary condition for technical

change. A bridge between the two could

potentially be provided by Toman and

Jemelkova’s (2003) observation that

increased inputs of useful work (or energy

services) may enhance the productivity of

capital and labour:

“......when the supply of energy services is

increased, there is not just more energy

to be used by each skilled worker or

machine; the productivity with which

every unit of energy is used also rises. If

all inputs to final production are increased

in some proportion, final output would

grow in greater proportion because of the

effect on non-energy inputs.” (Toman and

Jemelkova, 2003)

Focusing in particular on households in

developing countries, Toman and

Jemolkova (2003) propose a number of

ways in which the increased availability of

useful work could improve capital and

labour productivity and hence

disproportionately affect economic output.

For example, cheaper and better lighting

could allow greater flexibility in time

allocation throughout the day and evening

and enhance the productivity of education

efforts. The increased availability of

electricity could promote access to safe

drinking water (e.g. in deeper wells),

allow the refrigeration of food and

medicine and thereby improve both the

health of workers and their economic

productivity. Similarly, the increased

availability of low-cost transport fuels

could interact with investment in transport

infrastructure to increase the geographic

size, scale and efficiency of markets.

Schurr’s (1983; 1984; 1985) account of

the impact of electricity (and especially

electric motors) on the organisation and

productivity of US manufacturing provides

an analogous example for producers in

developed countries.

It is an empirical question as to whether

such benefits apply in practice and to

what extent. Ecological economists

appear to claim that such a situation is the

norm, with the result that the increased

availability of high-quality energy has

been a primary driver of economic

activity. But if the increased availability of

high-quality energy inputs has a

disproportionate impact on productivity

and economic growth, then improvements

in thermodynamic efficiency may do the

same, because both increase the useful

work available from conversion devices. If

it is useful work rather than raw energy

(or exergy) inputs that drives economic

activity, then improvements in second-law

conversion efficiency could potentially

mitigate the economic impact of future

shortages of high-quality forms of energy

– notably oil. 

However, this argument uses a

thermodynamic measure of energy quality

and therefore neglects the other factors

that determine the economic productivity

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of energy carriers. Also, improvements in

conversion efficiencies are necessarily

associated with embodied energy and are

ultimately constrained by thermodynamic

limitations; hence, their ability to

compensate for future supply shortages

may be limited. Moreover, if these

improvements have a disproportionate

effect on economic output, they may also

be associated with large secondary

effects.

5.6 Implications 

One interpretation of the K-B postulate is

that all economically justified energy

efficiency improvements will increase

energy consumption above where it would

be without those improvements. This is a

counterintuitive claim for many people

and therefore requires strong supporting

evidence if it is to gain widespread

acceptance. The main conclusion from the

review is that such evidence does not

exist. The theoretical and empirical

evidence cited in favour of the postulate is

suggestive rather than definitive, only

indirectly relevant to the rebound effect

and flawed in a number of respects.

Nevertheless, the arguments and

evidence deserve more serious attention

than they have received to date. Much of

the evidence points to economy-wide

rebound effects being larger than is

conventionally assumed and to energy

playing a more important role in economic

growth than is conventionally assumed. 

The possibility of large economy-wide

rebound effects has been dismissed by a

number of leading energy analysts

(Howarth, 1997; Lovins, 1998; Laitner,

2000; Schipper and Grubb, 2000). But it

becomes more plausible if it is accepted

that energy efficiency improvements are

frequently associated with improvements

in the productivity of other inputs. If this

is the case, then rebound effects need not

necessarily be small just because the

share of energy in total costs is small.

Future research should therefore

investigate the extent to which

improvements in energy efficiency

(however defined and measured) are

associated with broader improvements in

economic productivity, and the

circumstances under which economy-wide

rebound effects are more or less likely to

be large. For example, we may speculate

that rebound effects should be larger for

energy efficiency improvements

associated with:

• energy intensive production sectors

compared to non-energy intensive

sectors;

• energy supply industries compared to

energy users; 

• core process technologies compared

to non-core technologies;

• technologies in the early stages of

diffusion compared to those in the

later stages; and

• technologies that improve capital and

labour productivity, compared to

those that do not. 

Rebound effects may be particularly large

for the energy efficiency improvements

associated with ‘general-purpose

technologies’, such as steam engines,

railroads, automobiles and computers.

General-purpose technologies (GPTs)
have a wide scope for improvement and

elaboration, are applicable across a broad

range of uses, have potential for use in a

wide variety of products and processes

and have strong complementarities with

existing or potential new technologies

(Lipsey, et al., 2005). Steam engines

provide a paradigmatic illustration of a

GPT in the 19th-century, while electric

motors provides a comparable illustration

for the early 20th century. The former was

used by Jevons to support the case for

backfire, while the latter was used by

Brookes.

The key to unpacking the K-B postulate

may therefore be to distinguish the

energy efficiency improvements

associated with GPTs from other forms of

energy efficiency improvement. The K-B

postulate seems more likely to hold for

the former, particularly when these are

used by producers and when the energy

efficiency improvements occur at an early

stage of development and diffusion of the

technology. The opportunities offered by

these technologies have such long term

and significant effects on innovation,

productivity and economic growth that

economy-wide energy consumption is

increased. In contrast, the K-B postulate

seems less likely to hold for dedicated

energy efficiency technologies such as

improved thermal insulation, particularly

when these are used by consumers or

when they play a subsidiary role in

economic production. These technologies

have smaller effects on productivity and

economic growth, with the result that

economy-wide energy consumption may

be reduced. 

The implication is that energy policy

should focus on encouraging dedicated

energy efficient technologies, rather than

improving the energy efficiency of GPTs.

However, these categories are poorly

defined and the boundaries between them

are blurred. Moreover, even if GPTs can

meaningfully be distinguished from other

forms of technology, continued economic

growth is likely to depend upon the

diffusion of new types of GPT that may

increase aggregate energy consumption.

Hence, while it may be unlikely that all

energy efficiency improvements will lead

to backfire, we still have much to learn

about the factors that make backfire more

or less likely.

5.7 Summary

• The case for the K-B postulate is not

based upon empirical estimates of

rebound effects, but instead relies

upon stylised theoretical arguments

and empirical evidence from a range

of sources that is both suggestive and

indirect. Disputes over the postulate

rest in large part on competing

theoretical assumptions.

• Historical experience demonstrates

that substantial improvements in

various measures of energy efficiency

have occurred alongside continuing

increases in economic output, total

factor productivity and overall energy

consumption. However, the causal

links between these trends remains

unclear. 

• Brookes has developed a range of

arguments in support of the K-B

postulate and cited a number of

suggestive sources of empirical

evidence. However, his theoretical

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arguments have a number of

weaknesses and more recent

empirical evidence does not always

support his case. 

• A key theme in Brookes’ work is that

improvements in energy productivity

are generally associated with

proportionally greater improvements

in total factor productivity. Evidence

for this can be found at the level of

national economies, as well as

individual sectors and technologies. If

energy efficient technologies boost

the productivity of other inputs and

thereby save costs on more than

energy alone, the argument that

rebound effects must be small

because the share of energy in total

costs is small is undermined. But this

leaves open the question of whether

this is necessarily the case, or

whether it is contingent upon

particular circumstances.

• Saunders has shown how backfire is

the predicted outcome of neoclassical

production functions that are used

widely in theoretical and empirical

research. If such functions are

considered to provide a reasonable

representation of real-world

behaviour, Saunders’ work suggests

that ‘pure’ energy-efficiency

improvements are likely to lead to

backfire. Alternatively, if rebound

effects vary widely in magnitude

between different sectors, such

functions cannot be used to represent

them. In either case, the implications

are far-reaching. 

• Rebound effects depend in part upon

the scope for substitution between

energy and other inputs, but the

nature of this relationship is more

complex than commonly assumed.

The extensive empirical literature in

this area is both confused and

inconclusive and provides an

insufficient basis for the assumed

parameter values within energyeconomic models. To the extent that

general conclusions can be drawn, it is

that capital and energy appear to be

complements or only weak

substitutes. This suggests the

possibility of a strong link between

energy consumption and economic

output and high costs associated with

reducing energy consumption.

• Underlying Brookes’ work is a claim

about the contribution of energy to

economic growth. Most economists

assume that the increased availability

of energy inputs has only made a

small contribution to economic

growth, owing to the small share of

energy in total costs. In contrast,

ecological economists argue that the

increased availability and quality of

energy inputs has been the primary

driver of economic growth. But energy

is only economically productive

because it provides useful work.

Hence, if increases in energy inputs

contribute disproportionately to

economic growth, then improvements

in thermodynamic efficiency should do

the same, since both provide more

useful work. The dispute over the K-B

postulate can therefore be linked to

the much broader question of the
contribution of energy to economic

growth.

• Ecological economists cite a range of

evidence in support of their claims,

but this remains patchy and in some

cases flawed. Of particular interest

are the alternatives to standard

models of economic growth which can

be used in support of claims that

energy efficiency improvements are

associated with substantial

improvements in total factor

productivity and that rebound effects

are large. However, since these

models have a number of theoretical

and empirical weaknesses, convincing

evidence of the disproportionate

contribution of energy remain elusive.

• The debate over the K-B postulate

would benefit from further distinctions

between different types of energy

efficiency improvement. In particular,

the K-B postulate seems more likely

to hold for energy efficiency

improvements associated with the

early stage of diffusion of ‘generalpurpose technologies’, such as electric

motors in the early 20th century. It

may be less likely to hold for the later

stages of diffusion of these

technologies, or for ‘dedicated’ energy

efficiency technologies such as

improved thermal insulation.

However, these categories are poorly

defined and the boundaries between

them are blurred. 

• Overall, while it is unlikely that all

energy efficiency improvements will

lead to backfire, we still have much to

learn about the factors that make

backfire more or less likely to occur.

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6.1 Conclusions

The main conclusions from this

assessment are as follows: 

1. Rebound effects are significant,

but they need not make energy

efficiency policies ineffective in

reducing energy demand 

• The available evidence for all types of

rebound effects is limited and

inconclusive. While the evidence is

better for direct effects than for

indirect effects, it remains mainly

focused on a small number of

consumer energy services within

OECD countries. Both direct and

indirect rebound effects appear to

vary widely between different

technologies, sectors and income

groups and in most cases cannot be

quantified with much confidence. 

• The evidence does not suggest that

improvements in energy efficiency

routinely lead to economy-wide

increases in energy consumption, as

some commentators have suggested.

At the same time the evidence does

not suggest that economy-wide

rebound effects are small (e.g.

<10%) as many analysts and

policymakers assume. Rebound

effects therefore need to be taken

seriously in policy appraisal.

2. For most consumer energy

services in OECD countries, direct

rebound effects are unlikely to

exceed 30% 

• Evidence for the direct rebound effect

for personal automotive transport,

household heating and household

cooling within OECD countries is

relatively robust. Evidence for direct

rebound effects for other consumer

energy services is much weaker, as is

that for energy efficiency

improvements by producers. 

• For household heating, household

cooling and personal automotive

transport in OECD countries, the

direct rebound effect is likely to be

less than 30% and may be closer to

10% for transport. Moreover, direct

rebound effects for these energy

services are expected to decline in the

future as demand saturates and

income increases. This means that

improvements in energy efficiency

should achieve 70% or more of the

expected reduction in energy

consumption for those services -

although the existence of indirect

effects means that the economy-wide

reduction in energy consumption will

be less. Direct rebound effects should

be smaller for other consumer energy

services where energy forms a

relatively small proportion of total

costs and therefore has little influence

on operating decisions. 

• These conclusions are subject to a

number of important qualifications,

including the dependence of direct

rebound effects on household income,

the neglect of ‘marginal consumers’

and the relatively limited time periods

over which these effects have been

studied 

• There are very few studies of direct

rebound effects from energy efficiency

improvements in developing

countries. However, both theoretical

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87

6. Conclusions, research needs and policy implications
considerations and the limited

evidence that is available suggest that

direct rebound effects in these

contexts could be larger than in OECD

countries and could in some cases

exceed unity. This is especially the

case for the 1.6 billion households

who currently lack access to electricity

and the 2.5 billion who rely upon

biomass for cooking. 

3. There are relatively few

quantitative estimates of indirect

and economy-wide rebound

effects, but several studies

suggest that economy-wide

effects may exceed 50% 

• Quantitative estimates of indirect and

economy-wide rebound effects are

rare. While a number of

methodological approaches can be

used to estimate these effects, the

limited number of studies available

provides an insufficient basis to draw

any general conclusions. The most

important insight from these studies

is that the magnitude of the effect

depends very much upon the sector

with the energy efficiency

improvement takes place and is

sensitive to a number of variables.

• A handful of CGE modeling studies

estimate economy-wide rebound

effects to be 37% or more, with half

of the studies predicting backfire.

These effects derive from ‘pure’

energy efficiency improvements by

producers (not consumers) and

therefore do not rely upon

simultaneous improvements in the

productivity of other inputs. However,

the small number of studies available,

the diversity of approaches used and

the variety of methodological

weaknesses associated with the CGE

approach all suggest the need for

caution when interpreting these

results.

• In principle, more robust estimates of

the economy-wide rebound effect may

be obtained from macro-econometric

models of national economies. Barker

and Foxon (2006) use this approach

to estimate an economy-wide rebound

effect of 26% from current UK energy

efficiency policies. However, there are

a number of reasons why this study

could have underestimated economywide effects.

4. The evidence and arguments

used in support of the KhazzoomBrookes postulate are insufficient

to demonstrate its validity, but

nevertheless pose an important

challenge to conventional

wisdom

• The theoretical arguments for the K-B

postulate rely upon a conceptual

framework that is stylised and

restrictive, while the empirical

evidence cited in its favour is indirect

and suggestive. Since a number of

flaws have been found with both, the

K-B ‘hypothesis’ cannot be considered

to have been adequately verified.

Nevertheless, the arguments and

evidence used to defend the K-B

postulate deserve more serious

attention than they have received to

date. 

• It is conventionally assumed that

there is considerable scope for

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substituting capital and other inputs

for energy consumption while

maintaining the same level of

economic output. It is also

conventionally assumed that technical

change has historically improved the

energy efficiency of individual sectors

and thereby contributed to the

observed decoupling of energy

consumption from economic growth.

However, the evidence reviewed in

this report suggests that there is more

limited scope for substituting other

inputs for energy and that much

technical change has acted to increase

energy intensity. Also, once different

fuels are weighted by their relative

‘quality’ or economic productivity,

there is less evidence that the growth

in economic output has been

decoupled from the growth in energy

consumption. Overall, this evidence

points to economy-wide rebound

effects relatively large and to energy

playing a more important role in

economic growth than is

conventionally assumed.

• The possibility of large economy-wide

rebound effects becomes more

plausible if it is accepted that energy

efficiency improvements are

frequently associated with

proportionately greater improvements

in total factor productivity. If this is

the case, then rebound effects need

not necessarily be small just because

the share of energy in total costs is

small. But energy efficiency

improvements may not necessarily be

associated with such improvements.

Instead, the link between the two

seems more likely to be contingent

upon particular technologies and

circumstances.

• The debate over the K-B postulate

would benefit from more careful

distinctions between different types of

energy efficiency improvement. For

example, the K-B postulate seems

more likely to hold for energy

efficiency improvements associated

with ‘general-purpose technologies’

(GPTs), particularly when these are

used by producers and when the

improvements occur at an early stage

of development and diffusion. Steam

engines provide a paradigmatic

illustration of a GPT in the 19thcentury, while electric motors provide

a comparable illustration for the early

20th century. The opportunities

offered by these technologies have

such long term and significant effects

on innovation, productivity and

economic growth that economy-wide

energy consumption is increased. In

contrast, the K-B postulate seems less

likely to hold for dedicated energy

efficiency technologies such as

thermal insulation, particularly when

these are used by consumers. These

technologies have smaller effects on

productivity and economic growth,

with the result that economy-wide

energy consumption may be reduced.

6.2 Research needs

Given the potential importance of rebound

effects, the evidence base is remarkably

weak. While this is partly a consequence

of the inherent difficulty of measuring or

estimating such effects, there is
considerable scope for improving

knowledge in a number of areas. The

following highlights some priorities for

future research.

1. Research on direct rebound

effects needs to improve in

rigour and expand in scope

• Estimates of the direct rebound effect

are contingent upon good data sets

and would benefit from more robust

methodologies. This is particularly

case for quasi-experimental studies of

household heating, where current

evaluation practice is poor.

• Econometric studies need to address

the potential sources of bias indicated

in Section 3. There is scope for both

econometric and quasi-experimental

studies of a greater range of

consumer energy services, provided

that individual appliances can be

monitored. However, the policy issue

here is not so much changes in shortterm utilisation patterns, but changes

in the number and capacity of

conversion devices over the longer

term.

• Estimates of the direct rebound effect

for personal automotive transport

would benefit from more appropriate

definitions of useful work. A study

employing tonne kilometres as the

dependent variable appears feasible

and could potentially capture the

effect of increasing car sizes. Analysis

is also needed of other modes of

transport, including freight.

• There is scope for more empirical

work on the ‘rebound effect with

respect to time’, especially in the area

of transportation. More work is also

required on the dependence of direct

rebound effects on income. 

• The geographical bias of the evidence

base also needs to be addressed. In

particular, the evidence for rebound

effects in developing countries is very

weak.

2. Quantitative estimates of indirect

and economy-wide rebound

effects are feasible and should be

pursued

• A combination of input-output

analysis and life-cycle analysis can be

used to estimate the embodied energy

associated with various types of

energy efficiency improvement. These

estimates need to be developed more

systematically and the results

incorporated within technology and

policy appraisals.

• Embodied energy analysis can also be

combined with econometric models of

consumer or producer behaviour to

estimate the secondary effects from

energy efficiency improvements by

households. This approach is limited

to rather aggregate categories of

energy service and has a number of

methodological weaknesses.

Nevertheless, there is considerable

scope for further research. 

• Given the widespread use of CGE

models in energy research, the lack of

application to rebound effects is

surprising. There is much scope for

expanding the evidence base in terms

of the countries and sectors studied

and the different types of energy

efficiency improvement that are

modelled. However, the empirical

basis for CGE models needs to be

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improved and there is a need for more

systematic and informed sensitivity

analysis to highlight the importance of

specific assumptions. This would help

establish how robust the results are,

and with what confidence they can be

expressed.

• More robust estimates of economywide rebound effects may potentially

be obtained from macro-econometric

models. There is scope for further

application of this methodology, in

which some of the weaknesses

identified here could be addressed.

3. Our understanding of the

contribution of energy to

economic growth needs to be

greatly improved 

• The linkage between economic

measures of energy efficiency at the

macro-level and physical/

thermodynamic measures of energy

efficiency at the micro level is poorly

understood. While decomposition

analysis is routinely used to identify

the relative contribution of structural

change and changes in various

measures of energy efficiency, the

relative importance of technical

change, substitution between energy

and other inputs, and changes in fuel

mix remains unclear. Since standard

assumptions about ‘autonomous

energy efficiency improvements’

(AEEI) appear inconsistent with the

available evidence, the projections of

many energy-economic models may

be misleading. 

• Since thirty years of research has

failed to reach a consensus on the

issue of substitutability between

energy and capital, the underlying

theoretical assumptions and/or

methodological approach may be

flawed. Future work should ensure

that restrictions such as neutral

technical change are tested for rather

than assumed and only accepted on

empirical grounds. Future studies

should also use a flexible form for the

production function, allow for nonneutral technical change, test the

assumption of ‘separability’ between

different inputs and pay closer

attention to changes in product mix. A

full meta-analysis of existing studies

would also be beneficial, to further

clarify the reasons for the differing

results. 

• Future research should investigate

whether, how and to what extent

different types of energy efficiency

improvement at different levels of

aggregation are associated with

improvements in the productivity of

other inputs and with improvements

in total factor productivity.

• ‘Ecological’ models of economic

growth challenge conventional

assumptions and offer a promising

route for further research. At present,

this approach is largely ignored by

mainstream economists, who

generally pay insufficient attention to

the contribution of energy to

economic growth. While Saunders has

used neoclassical growth theory to

explore the rebound effect, the issue

has not been addressed by

contemporary research on

endogenous growth theory and

induced technical change. While
communication is inhibited in part by

competing ‘world-views’, there should

be scope for mutual learning and

improved testing. 

6.3 Policy implications

The general conclusion of this assessment

is that rebound effects need be taken

more seriously by analysts and

policymakers than has hitherto been the

case. While the effectiveness of energy

efficiency policies has not been

investigated, some general policy

implications can nevertheless be drawn:

1. The potential contribution of

energy efficiency policies needs

to be reappraised. 

• Energy efficiency may be encouraged

through policies that raise energy

prices, such as carbon taxes, or

through non-price policies such as

building regulations. Both should

continue to play an important role in

energy and climate policy. However,

many official and independent

appraisals of such policies have

overstated the potential contribution

of non-price policies to reducing

energy consumption and carbon

emissions. 

• It would be wrong to assume that, in

the absence of evidence, rebound

effects are so small that they can be

disregarded. Under some

circumstances (e.g. energy efficient

technologies that significantly

improve the productivity of energy

intensive industries) economy-wide

rebound effects may exceed 50% and

could potentially increase energy

consumption in the long-term. In

other circumstances (e.g. energy

efficiency improvements in consumer

electronic goods) economy-wide

rebound effects are likely to be

smaller. But in no circumstances are

they likely to be zero. 

• Taking rebound effects into account

will reduce the apparent effectiveness

of energy efficiency policies. However,

many energy efficiency opportunities

are highly cost-effective and will

remain so even when rebound effects

are allowed for. Provided market and

organisational failures can be

overcome, the encouragement of

these opportunities should increase

real income and contribute to

economic growth. They may not,

however, reduce energy consumption

and carbon emissions by as much as

previously assumed.

2. Rebound effects should be taken

into account when developing

and targeting energy efficiency

policy

• Rebound effects vary widely between

different technologies, sectors and

income groups. While these

differences cannot be quantified with

much confidence, there should be

scope for including estimated effects

within policy appraisals and using

these estimates to target policies

more effectively. Where rebound

effects are expected to be large, there

may be a greater need for policies

that increase energy prices.

The Rebound Effect: an assessment of the evidence for economy-wide energy savings from improved energy efficiency 

92
The Rebound Effect: an assessment of the evidence for economy-wide energy savings from improved energy efficiency 

93

• ‘Win-win’ opportunities that reduce

capital and labour costs as well as

energy costs may be associated with

large rebound effects. Hence, the

implications of encouraging these

opportunities need to be clearly

understood and quantified. It may

make more sense to focus policy on

‘dedicated’ energy efficient

technologies, leaving the realisation of

wider benefits to the market

3. Rebound effects may be

mitigated through carbon/energy

pricing – whether implemented

through taxation or an emissions

trading scheme 

• Carbon/energy pricing can reduce

direct and indirect rebound effects by

ensuring that the cost of energy

services remains relatively constant

while energy efficiency improves.

Carbon/energy pricing needs to

increase over time at a rate sufficient

to accommodate both income growth

and rebound effects, simply to

prevent carbon emissions from

increasing. It needs to increase more

rapidly if emissions are to be reduced. 

• Carbon/energy pricing may be

insufficient on its own, since it will not

overcome the numerous barriers to

the innovation and diffusion of low

carbon technologies and could have

adverse impacts on income

distribution and competitiveness.

Similarly, policies to address market

barriers may be insufficient, since

rebound effects could offset much of

the energy savings. A policy mix is

required. 
The Rebound Effect: an assessment of the evidence for economy-wide energy savings from improved energy efficiency 

94
Adams, F. G. and P. Miovic, (1968), 'On relative fuel efficiency and the output elasticity of energy

consumption in Western Europe', The Journal of Industrial Economics, 17(1), 41-56.

Alcorn, J. A. and G. Baird, (1996), Embodied energy analysis of New Zealand building materialsmethods and results, Proceedings of the Embodied Energy: The Current State of Play Seminar,

Deakin University, Geelong, Australia, 28-29 November

Alcott, B., (2005), 'Jevons' paradox', Ecological Economics, 54(1), 9-21.

Alfredsson, E. C., (2004), ''Green' consumption - no solution for climate change', Energy, 29, 513-

24.

Allan, G., N. Hanley, P. G. McGregor, J. Kim Swales, and K. Turner, (2006), 'The macroeconomic

rebound effect and the UK economy', Final report to the Department Of Environment Food and Rural

Affairs, Department Economics, University of Strathclyde, Strathclyde.

Ang, B. W., (1999), 'Decomposition methodology in energy demand and environmental analysis', in

Handbook of Environmental and Resource Economics, J.C.J.M van der Bergh ed, Edward Elgar,

Cheltenham, UK.

Ang, B. W., (2006), 'Monitoring changes in economy-wide energy efficiency: From energy-GDP ratio

to composite efficiency index', Energy Policy, 34(5), 574-82.

Atack, J., (1979), 'Fact in fiction? The relative cost of steam and water power: a simulation

approach', Explorations in Economic History, 16, 409-37.

Ayres, R. U., (2002), 'Resources, scarcity, technology and growth', INSEAD Working Paper No.

2002/118/EPS/CMER, Centre for the Management of Environmental Resources, INSEAD,

Fontainebleau.

Ayres, R. U., L. W. Ayres, and B. Warr, (2003), 'Exergy, power and work in the US economy, 1900-

1998', Energy, 28(3), 219-73.

Ayres, R. U. and B. Warr, (2002), 'Two paradigms of production and growth', CMER Working Paper,

INSEAD, Fontainbleau, France.

Ayres, R. U. and B. Warr, (2005), 'Accounting for growth: the role of physical work', Structural

Change and Economic Dynamics, 16(2), 181-209.

Ayres, R. U. and B. Warr, (2006), 'REXS: a forecasting model for assessing the impact of natural

resource consumption and technological change from economic growth', Structural Change in

Economic Dynamics, 17(3), 329-78.

Baker, P., Blundell, R., and J. Mickelwright, (1989), 'Modelling household energy expenditures using

micro-data', Economic Journal, 99(720-738).

Baker, P. and R. Blundell, (1991), 'The microeconometric approach to modelling energy demand:

some results for UK households', Oxford Review of Economic Policy, 7(2), 54-76.

Barker, T., (2005), 'The transition to sustainability: a comparison of general equilibrium and spacetime economics approaches', Working Paper Number 62, Tyndall Centre for Climate Change

Research.

Barker, T., P. Ekins, and T. Foxon, (2007), 'Macroeconomic effects of efficiency policies for energyintensive industries: the case of the UK Climate Change Agreements, 2000-2010', Energy

Economics, 29(5), 760-78.

The Rebound Effect: an assessment of the evidence for economy-wide energy savings from improved energy efficiency 

95

References
Barker, T., P. Ekins, and N. Johnstone, (1995), Global warming and energy demand, Routledge,

London.

Barker, T. and T. Foxon, (2006), 'The macroeconomic rebound effect and the UK economy', Report

to the Department of the Environment, food and Rural Affairs, 4CMR, Cambridge.

Barro, R. J. and X. Sala-I-Martin, (1995), Economic Growth, McGraw-Hill, New York.

Beaudreau, B. C., (1998), Energy and Organisation: group and distribution re-examined,

Greenwood Press, Westport, CT.

Becker, G. S., (1965), 'A theory of the allocation of time', The Economic Journal, LXXV(299), 493-

517.

Bergh, J. C. J. M. v. d. and K. J. Button, (1999), 'Meta-analysis, economic valuation and

environmental economics', in Handbook of Environmental Resources, J.C.J.M. van den Bergh ed,

Edward Elgar, Cheltenham.

Berndt, E. R., (1978), 'Aggregate energy, efficiency and productivity measurement', Annual Review

of Energy, 3, 225-73.

Berndt, E. R., (1990), 'Energy use, technical progress and productivity growth: a survey of economic

issues', The Journal of Productivity Analysis, 2, 67-83.

Berndt, E. R. and D. O. Wood, (1979), 'Engineering and econometric interpretations of energycapital complementarity', American Economic Review, June, 259-68.

Berry, L., (1983), 'Residential conservation programme impacts: methods of reducing self-selection

bias', Evaluation Review, 7(6), 753-75.

Besen, S. M. and L. L. Johnson, (1982), 'Comment on "Economic implications of mandated efficiency

standards for household appliances"', Energy Journal, 3(1), 110-16.

Bhattacharyya, S. C., (1996), 'Applied general equilibrium models for energy studies: a survey',

Energy Economics, 18, 145-64.

Binswanger, M., (2001), 'Technological progress and sustainable development: what about the

rebound effect?' Ecological Economics, 36(1), 119-32.

Birol, F. and J. H. Keppler, (2000), 'Prices, technology development and the rebound effect', Energy

Policy, 28(6-7), 457-69.

Boardman, B. and G. Milne, (2000), 'Making cold homes warmer: the effect of energy efficiency

improvements in low-income homes', Energy Policy,, 218(6-7), 411-24.

Boyd, G. A. and J. X. Pang, (2000), 'Estimating the linkage between energy efficiency and

productivity', Energy Policy, 28, 289-96.

Brännlund, R., T. Ghalwash, and J. Nordstrom, (2007), 'Increased energy efficiency and the rebound

effect: effects on consumption and emissions', Energy Economics, 29(1), 1-17.

Brookes, L., (2004), 'Energy efficiency fallacies—a postscript', Energy Policy, 32(8), 945-47.

Brookes, L. G., (1972), 'More on the output elasticity of energy consumption', The Journal of

Industrial Economics, 21(1), 83-92.

Brookes, L. G., (1978), 'Energy policy, the energy price fallacy and the role of nuclear energy in the

UK', Energy Policy, 6(2), 94-106.

The Rebound Effect: an assessment of the evidence for economy-wide energy savings from improved energy efficiency 

96
The Rebound Effect: an assessment of the evidence for economy-wide energy savings from improved energy efficiency 

97

Brookes, L. G., (1984), 'Long-term equilibrium effects of constraints in energy supply', in The

Economics of Nuclear Energy, Leonard Brookes and H. Motamen eds, Chapman and Hall, London.

Brookes, L. G., (1990), 'The greenhouse effect: the fallacies in the energy efficiency solution.'

Energy Policy, 18(2), 199-201.

Brookes, L. G., (1992), 'Energy efficiency and economic fallacies - a reply', Energy Policy, 20(5),

390-92.

Brookes, L. G., (1993), 'Energy efficiency fallacies - the debate concluded', Energy Policy, 21(4),

346-47.

Brookes, L. G., (2000), 'Energy efficiency fallacies revisited', Energy Policy, 28(6-7), 355-66.

Bullard, C. W., P. S. Penner, and D. A. Pilati, (1978), 'Net energy analysis: handbook for combining

process and input–output analysis', Resources and Energy 1 (1978) (3), pp. 267–313, 1(3),

267–313.

Cambridge Econometrics, (2005), 'Modelling the initial effects of the Climate Change Levy', HM

Customs and Excise, London.

Casals, X. G., (2006), 'Analysis of building energy regulation and certification in Europe: their role,

limitations and differences', Energy and Buildings, 38, 381-92.

Chalkley, A. M., E. Billett, and D. Harrison, (2001), 'An investigation of the possible extent of the

respending rebound effect in the sphere of consumer products', The Journal of Sustainable Product

Design, 1(163-170).

Chapman, P., (1974), 'Energy costs: a review of methods', Energy Policy, 2(2), 91-103.

Chontanawat, J., L. Hunt, C.,, and R. Pierse, (2006), 'Causality between energy consumption and

GDP: evidence from 30 OECD and 78 non-OECD countries', Working Paper, Department of

Economics, University of Surrey, Guildford.

Cleveland, C. J., (1992), 'Energy quality and energy surplus in the extraction of fossil fuels in the

U.S.' Ecological Economics, 6(2), 139-62.

Cleveland, C. J., R. Costanza, C. A. S. Hall, and R. K. Kaufmann, (1984), 'Energy and the US

economy: a biophysical perspective', Science, 225, 890-97.

Cleveland, C. J., R. K. Kaufmann, and D. I. Stern, (2000), 'Aggregation and the role of energy in the

economy', Ecological Economics, 32, 301-17.

Conrad, K., (1999), 'Computable general equilibrium models for environmental economics and policy

analysis', in Handbook of Environmental and Resource Economics, J.C.J.M van dn Bergh ed, Edward

Elgar, Cheltenham, UK.

Costanza, R., (1980), 'Embodied energy and economic valuation', Science, 210, 1219-24.

Crafys, N., (2003), 'Steam as a general-purpose technology: a growth accounting perspective',

Working Paper nO. 75/03, Department of Economic History, London School of Economics.

Dahl, C., (1993), 'A survey of energy demand elasticities in support of the development of the

NEMS', Prepared for US Department of Energy, Contract No. De-AP01-93EI23499, Department of

Mineral Economics, Colorado School of Mines, Colorado.

Dahl, C., (1994), ' Demand for transportation fuels: a survey of demand elasticities and their

components', Journal of Energy Literature, 1(2), 3.
Dahl, C. and T. Sterner, (1991), 'Analyzing Gasoline Demand Elasticities - a Survey', Energy

Economics, 13(3), 203-10.

Dargay, J. M., (1992), Are price & income elasticities of demand constant? The UK experience,

Oxford Institute for Energy Studies, Oxford, U.K.

Dargay, J. M. and D. Gately, (1994), 'Oil demand in the industrialised countries', Energy Journal,

15(Special Issue), 39-67.

Dargay, J. M. and D. Gately, (1995), 'The imperfect price irreversibility of non-transportation of all

demand in the OECD', Energy Economics, 17(1), 59-71.

Davis, L. W., (2007), 'Durable goods and residential demand for energy and water: evidence from

a field trial', Working Paper, Department of Economics, University of Michigan.

Deaton, A. and J. Mulbauer, (1980), 'An almost ideal demand system', The American Economic

Review, 70, 312-26.

DEFRA, (2007), 'Consultation document: energy, cost and carbon savings for the draft EEC 2008 -

11 illustrative mix', Department of Environment, Food and Rural Affairs, London.

Denison, E. F., (1985), Trends in American Economic Growth 1929-1982, The Bookings Institute,

Washington, DC.

Department of Transport, (2006), 'National Travel Survey', Department of Transport, London.

Diewert, W. E. and T. J. Wales, (1987), 'Flexible functional forms and global curvature conditions',

Econometrica, 55(1), 43-68.

Douthitt, R. A., (1986), 'The demand for residential space and water heating fuel by energy

conserving households', The Journal of Consumer Affairs, 20(2), 231-48.

Dubin, J. A., A. K. Miedema, and R. V. Chandran, (1986), 'Price Effects of Energy-Efficient

Technologies - a Study of Residential Demand for Heating and Cooling', Rand Journal of Economics,

17(3), 310-25.

Einhorn, M., (1982), 'Economic implications of mandated efficiency standards for household

appliances: an extension.' Energy Journal, 3(1), 103-09.

EMF 4 Working Group, (1981), 'Aggregate elasticity of energy demand', Energy Journal, 2(2), 37-

75.

Engle, R. F. and C. W. J. Granger, (1987), 'Cointegration and error correction: representation,

estimation and testing', Econometricaa, 55(2), 251-76.

Espey, J. A. and M. Espey, (2004), 'Turning on the lights: a meta-analysis of residential electricity

demand elasticities', Journal of Agricultural and Applied Economics, 36(1), 65-81.

Espey, M., (1998), 'Gasoline demand revisited: an international meta-analysis of elasticities', Energy

Economics, 20, 273-95.

Fanger, O., (1970), Thermal Comfort: Analysis and Applications in Environmental Engineering,

Danish Technical Press, Copenhagen.

Feist, W., (1996), Life-cycle energy balances compared: low energy house, passive house, selfsufficient house, Proceedings of the International Symposium of CIB W67, Vienna, Austria, 

Ferrer-i-Carbonell, A., A. C. Muskens, and M. J. van Leeuwen, (2002), 'Behavioural responses to

energy-related taxes: a survey', International Journal of Global Energy Issues, 18(2/3/4), 202-17.

The Rebound Effect: an assessment of the evidence for economy-wide energy savings from improved energy efficiency 

98
The Rebound Effect: an assessment of the evidence for economy-wide energy savings from improved energy efficiency 

99

Fisher, F. M., (1993), Aggregation: aggregate production functions and related topics, MIT Press,

Cambridge, MA.

Fouquet, R. and P. Pearson, (2006), 'Seven centuries of energy services: the price and use of light

in the United Kingdom (1300-1700)', The Energy Journal, 27(1), 139-77.

Frey, C. J. and D. G. Labay, (1988), 'Examination of energy take-back', Energy Systems and Policy,

12, 205-17.

Friedman, D., (1987), 'Cold houses in warm climates and vice versa: a paradox of rational heating',

Journal of Political Economy, 95, 1089-97.

Frondel, M., (2004), 'Empirical assessment of energy price policies: the case for cross price

elasticities', Energy Policy, 32, 989-1000.

Frondel, M. and C. M. Schmidt, (2004), 'Facing the truth about separability: nothing works without

energy', Ecological Economics, 51, 217-23.

Frondel, M. and C. M. Schmidt, (2005), 'Evaluating Environmental Programs: The Perspective of

Modern Evaluation Research', Ecological Economics, 55(4), 515-26.

Gately, D., (1992), 'Imperfect price reversed ability of US gasoline demand: asymmetric responses

to price increases and declines', Energy Journal, 13(4), 179-207.

Gately, D., (1993), 'The imperfect price reversibility of world oil demand', Energy Journal, 14(4),

163-82.

Geller, H., P. Harrington, A. H. Rosenfeld, S. Tanishima, and F. Unander, (2006), 'Policies for

increasing energy efficiency: thirty years of experience in OECD countries', Energy Policy, 34, 556-

73.

Goldberg, P. K., (1996), 'The effect of the corporate average fuel effciency standards.' Working Paper

No: 5673, National Bureau of Economic Research, Cambridge,MA.

Graham, D. J. and S. Glaister, (2002), 'The demand for automobile fuel: a survey of elasticities',

Journal of Transpor Economics and Policy, 36(1), 1-26.

Greene, D. L., (1992), 'Vehicle use and fuel economy: how big is the "rebound" effect?' Energy

Journal, 13(1), 117-43.

Greene, D. L., J. R. Kahn, and R. Gibson, (1999a), 'An econometric analysis of the elasticity of

vehicle travel with respect to fuel cost per mile using the RTEC survey data', Oak Ridge National

Laboratory, Oak Ridge, Tennessee.

Greene, D. L., J. R. Kahn, and R. C. Gibson, (1999b), 'Fuel economy rebound effect for US household

vehicles', Energy Journal, 20(3), 1-31.

Greening, L. A. and D. L. Greene, (1998), 'Energy use, technical efficiency, and the rebound effect:

a review of the literature', Report to the U.S. Department of Energy, Hagler Bailly and Co., Denver.

Greening, L. A., D. L. Greene, and C. Difiglio, (2000), 'Energy efficiency and consumption - the

rebound effect - a survey', Energy Policy, 28(6-7), 389-401.

Grepperud, S. and I. Rasmussen, (2004), 'A general equilibrium assessment of rebound effects',

Energy Economics, 26(2), 261-82.
Grubb, M., J. Kohler, and D. Anderson, (2002), 'Induced technical change in energy and

environmental modelling: analytic approaches and policy implications', Annual Review of Energy and

the Environment, 27, 271-308.

Grubb, M. J., (1990), 'Energy efficiency and economic fallacies', Energy Policy, 18(8), 783-85.

Grubb, M. J., (1992), 'Reply to Brookes', Energy Policy, 783-85.

Grubb, M. J., (1995), 'Asymmetrical price elasticities of energy demand', in Global warming and

energy demand, T. Barker, P. Ekins and N. Johnstone eds, Routledge, London and New York.

Guertin, C., S. Kumbhakar, and A. Duraiappah, (2003), 'Determining demand for energy services:

investigating income-driven behaviours', International Institute for Sustainable Development.

Gullickson, W. and M. J. Harper, (1987), 'Multi factor productivity in US manufacturing 1949-1983',

Monthly Labour Review, (October 18-28).

Haas, R. and P. Biermayr, (2000), 'The rebound effect for space heating - Empirical evidence from

Austria', Energy Policy, 28(6-7), 403-10.

Haas, R. and L. Schipper, (1998), 'Residential energy demand in OECD-countries and the role of

irreversible efficiency improvements', Energy Economics, 20(4), 421-42.

Hanley, M., J. M. Dargay, and P. B. Goodwin, (2002), 'Review of income and price elasticities in the

demand for road traffic', Final report to the DTLR under Contract number PPAD 9/65/93, ESRC

Transport Studies Unit, University College London, London.

Hanley, N., P. G. McGregor, J. K. Swales, and K. Turner, (2005), 'Do increases in resource

productivity improve environmental quality? Theory and evidence on rebound and backfire effects

from an energy-economy-environment regional computable general equilibrium model of Scotland',

Department of Economics, University of Strathclyde, Strathclyde.

Harrison, D., G. Leonard, B. Reddy, D. Radov, P. Klevnäs, J. Patchett, and P. Reschke, (2005),

'Reviews of studies evaluating the impacts of motor vehicle greenhouse gas emissions regulations

in California', National Economic Research Associates, Boston.

Hartman, R. S., (1988), 'Self-selection bias in the evaluation of voluntary energy conservation

programs', The Review of Economics and Statistics, 70(3), 448-58.

Haughton, J. and S. Sarkar, (1996), 'Gasoline tax as a corrective tax: Estimates for the United

States, 1970-1991', Energy Journal, 17(2), 103-26.

Hausman, J. A., (1979), 'Individual Discount Rates and the Purchase and Utilization of Energy-Using

Durables', Bell Journal of Economics, 10(1), 33-54.

Henly, J., H. Ruderman, and M. D. Levine, (1988), 'Energy savings resulting from the adoption of

more efficient appliances: a follow-up', Energy Journal, 9(2), 163-70.

Herendeen, R., (1978), 'Total energy cost of household consumption in Norway, 1973', Energy, 3(5),

615-30.

Herendeen, R. and J. Tanak, (1976), 'The energy cost of living', Energy, 1(2), 165-78.

Herring, H., (2006), 'Energy efficiency--a critical view', Energy, 31(1), 10-20.

Herring, H. and M. Elliot, (1990), 'Letter to the editor', Energy Policy, 18(8), 786.

Hills, R. L., (1989), Power from Steam, Cambridge University Press, Cambridge.

The Rebound Effect: an assessment of the evidence for economy-wide energy savings from improved energy efficiency 

100
The Rebound Effect: an assessment of the evidence for economy-wide energy savings from improved energy efficiency 

101

Hogan, W. W., (1979), 'Capital-energy complementarity in aggregate energy-economic analysis',

Resources and Energy, 2, 201-20.

Hogan, W. W. and D. W. Jorgenson, (1991), 'Productivity trends and the cost of reducing CO2

emissions', The Energy Journal, 12(1), 67-85.

HoL, (2006), 'Second Report: Energy Efficiency, Session 2005-2006', House of Lords Select

Committee on Science and Technology, London.

Hong, N. V., (1983), 'Two measures of aggregate energy production elasticities', The Energy Journal,

4(2), 172-77.

Hong, S. H., T. Oreszeczyn, and I. Ridley, (2006), 'The impact of energy efficient refurbishment on

the space heating fuel consumption in English dwellings', Energy and Buildings, 38(10), 1171-81.

Howarth, R. B., (1997), 'Energy efficiency and economic growth', Contemporary Economic Policy,

15(4), 1.

Hsueh, L.-M. and J. L. Gerner, (1993), 'Effect of Thermal Improvements in Housing on Residential

Energy Demand', Journal of Consumer Affairs, 27(1), 87-105.

IPCC, (2007), 'Climate Change 2007: Mitigation of Climate Change', Working Group III contribution

to the IPCC Fourth Assessment Report, Intergovernmental Panel on Climate Change.

Jalas, M., (2002), 'A time use perspective on the materials intensity of consumption', Ecological

Economics, 41(1), 109-23.

Jevons, W. S., (1865), 'The Coal Question: Can Britain Survive?' in The Coal Question: An Inquiry

Concerning the Progress of the Nation, and the Probable Exhaustion of Our Coal-mines, A.W. Flux

ed, Augustus M. Kelley, New York.

Johansson, O. and L. Schipper, (1997), 'Measuring long-run automobile fuel demand: separate

estimations of vehicle stock, mean fuel intensity, and mean annual driving distance', Journal of

Transport Economics and Policy, 31(3), 277-92.

Jones, C. T., (1993), 'Another look at U.S. passenger vehicle use and the "rebound" effect from

improved fuel efficiency', The Energy Journal, 14(4), 99.

Jones, H. G., (1975), An introduction to modern theories of economic growth, Van Nostrand

Reinhold, London.

Jorgenson, D. and B. Fraumeni, (1981), 'Relative prices and Technical Change', in Modeling and

Measuring Natural Resource Substitution., E.R. Berndt and B.C. Field eds, The MIT Press, Boston.

Jorgenson, D. W., (1984), 'The role of energy in productivity growth.' The Energy Journal, 5(3).

Jorgenson, D. W., (1996), Growth Vol. 2 Energy, the Environment, and Economic Growth, MIT Press,

Boston.

Jorgenson, D. W. and Z. Griliches, (1967), 'The explanation of productivity change', Review of

Economic Studies, 34(3), 249-82.

Junankar, S., O. Lofsnaes, and P. Summerton, (2007), 'MDM-E3: a short technical description',

Working Paper, Cambridge Econometrics.

Kanyama, A. C., R. Engstrom, and R. Kok, (2006), 'Indirect and direct energy requirements of city

households in Sweden: options for reduction, lessons from modelling', Journal of Industrial Ecology,

9(1-2), 221-35.
Kaufmann, R. K., (1992), 'A biophysical analysis of the energy/real GDP ratio: implications for

substitution and technical change', Ecological Economics, 6(1), 35-56.

Kaufmann, R. K., (1994), 'The relation between marginal product and price in US energy markets :

Implications for climate change policy', Energy Economics, 16(2), 145-58.

Kaufmann, R. K., (2004), 'The mechanisms of autonomous energy efficiency increases: a

cointegration analysis of the US energy/GDP ratio', The Energy Journal, 25(1), 63-86.

Kaufmann, R. K. and I. G. Azary-Lee, (1990), A biophysical analysis of substitution: does

substitution save energy in the US forest products industry?, Ecological economics: its implications

for forest management and research, Proceedings of a Workshop held in St Paul, Minnesota, April

2-6

Kempton, W. and L. Montgomery, (1982), 'Folk quantification of energy', Energy, 7(10), 817-27.

Khazzoom, J. D., (1980), 'Economic implications of mandated efficiency in standards for household

appliances', Energy Journal, 1(4), 21-40.

Klein, Y. L., (1987), 'Residential energy conservation choices of poor households during a period of

rising energy prices', Resources and Energy, 9(4), 363-78.

Klein, Y. L., (1988), 'An econometric model of the joint production and consumption of residential

space heat', Southern Economic Journal, 55(2), 351-59.

Kok, R., R. M. J. Benders, and H. C. Moll, (2006), 'Measuring the environmental load of household

consumption using some methods based on input–output energy analysis: A comparison of methods

and a discussion of results', Energy Policy, 34(17), 2744-61.

Kraft, J. and A. Kraft, (1978), 'On the relationship between energy and GNP', Journal of Energy and

Development, 3(401-3).

Kummel, R., J. Henn, and D. Lindenberger, (2002), 'Capital, labor, energy and creativity: modeling

innovation diffusion', Structural Change and Economic Dynamics, 13(4), 415-33.

Kummel, R., D. Lindenberger, and W. Eichhorn, (2000), 'The productive power of energy and

economic evolution', Indian Journal of Applied Economics, 8, 231-62.

Kummel, R., W. Strassl, A. Gossner, and W. Eichhorn, (1985), 'Technical progress and energy

dependent production functions', Nationalökonomie - Journal of Economics, 3, 285-311.

Laitner, J. A., (2000), 'Energy efficiency: rebounding to a sound analytical perspective', Energy

Policy, 28(6-7), 471-75.

Leach, G., (1975), 'Net energy analysis — is it any use?' Energy Policy, 3(4), 332-44.

Lee, C. C., (2006), 'The causality relationship between energy consumption and GDP in G-11

countries revisited', Energy Policy, 34(9), 1086-93.

Lenzen, M., (1998), 'Energy and greenhouse gas cost of living for Australia during 1993/94', Energy,

23(66), 497-516.

Lenzen, M. and C. Dey, (2000), 'Truncation error in embodied energy analyses of basic iron and steel

products', Energy, 25(6), 577-85.

Lim, H. and C. R. Shumway, (1997), 'Technical change and model specification: US agricultural

production', American Journal of Agricultural Economics, 79, 543-54.

The Rebound Effect: an assessment of the evidence for economy-wide energy savings from improved energy efficiency 

102
The Rebound Effect: an assessment of the evidence for economy-wide energy savings from improved energy efficiency 

103

Lipsey, R., G., K. I. Carlaw, and C. T. Bekar, (2005), Economic Transformations: General Purpose

Technologies and Long-term Economic Growth, Oxford University Press, Oxford.

Löschel, A., (2002), 'Technological change in economic models of environmental policy: a survey',

Ecological Economics, 43(2-3), 105-26.

Lovins, A. B., (1998), 'Further comments on Red Herrings', Letter to the New Scientist, No. 2152,

18 September

Lovins, A. B., J. Henly, H. Ruderman, and M. D. Levine, (1988), 'Energy saving resulting from the

adoption of more efficient appliances: another view; a follow-up', The Energy Journal, 9(2), 155.

Lovins, A. B. and L. H. Lovins, (1997), 'Climate: making sense and making money', Rocky Mountain

Institute, Old Snowmass, Colorado.

Meyer, B., (1995), 'Natural and quasi experiments in economics', The Journal of Business and

Economic Statistics, 13(2), 151-60.

Miller, E. M., (1986), 'Cross-sectional and time-series biases in factor demand studies: explaining

energy-capital complementarity', Southern Economic Journal, 52(3), 745-62.

Milne, G. and B. Boardman, (2000), 'Making cold homes warmer: the effect of energy efficiency

improvements in low-income homes', Energy Policy, 28, 411-24.

Mizobuchi, K. I., (2007), 'An empirical evidence of the rebound effect considering capital costs',

Working Paper, Graduate School of Economics, Kobe University, Kobe, Japan.

Nadel, S., (1993), 'The take-back effect - - fact or fiction', U933, ACEEE.

Nesbakken, R., (1999), 'Price sensitivity of residential energy consumption in Norway', Energy

Economics, 21, 493-515.

Norsworthy, J. R., M. J. Harper, and K. Kunz, (1979), 'The slowdown in productivity growth: an

analysis of some contributing factors', Brookings Papers in Economic Activity, 2, 387-41.

Parti, M. and C. Parti, (1980), 'The total and appliance-specific conditional demand for electricity in

the household sector', The Bell Journal of Economics, 11, 309-21.

Patterson, M. G., (1996), 'What is energy efficiency: concepts, indicators and methodological

issues', Energy Policy, 24(5), 377-90.

Poyer, D. A. and M. Williams, (1992), 'Residential energy demand: additional empirical evidence by

minority household type', Energy Economics, 15(2), 93-100.

Puller, S. L. and L. A. Greening, (1999), 'Household adjustment to gasoline price change: an analysis

using 9 years of US survey data', Energy Economics, 21(1), 37-52.

Pye, M. and A. McKane, (1998), Enhancing shareholder value: making a more compelling energy

efficiency case to industry by quantifying non-energy benefits, Proceedings 1999 Summer Study on

Energy Efficiency in Industry, Washington, DC, 

Richmond, A. K. and R. K. Kaufmann, (2006a), 'Energy prices and turning points: the relationship

between income and energy use/carbon emissions', The Energy Journal, 27(4), 157-79.

Richmond, A. K. and R. K. Kaufmann, (2006b), 'Is there a turning point in the relationship between

income and energy use and/or carbon emissions?' Ecological Economics, 56(2), 176-89.

Rosen, M. A., (2004), 'Exergy analysis of energy systems', in Encyclopaedia of Energy, C.J.

Cleveland ed, Elsevier Academic Press.
Rosenberg, N., (1989), Energy Efficient Technologies: Past, Present and Future Perspectives, How

Far Can the World Get on Energy Efficiency Alone?, 

Roy, J., (2000), 'The rebound effect: some empirical evidence from India', Energy Policy, 28(6-7),

433-38.

Roy, J., J. Sathaye, A. H. Sanstad, P. Mongia, and K. Schumacher, (1999), 'Productivity trends in

India's energy intensive industries', The Energy Journal, 20(3), 33-61.

Royal Commission on Environmental Pollution, (2007), 'The Urban Environment', London.

Sanders, M. and M. Phillipson, (2006), 'Review of differences between measured and theoretical

energy savings for insulation measures', Centre for Research on Indoor Climate and Health, Glasgow

Caledonian University, Glasgow.

Sanstad, A. H., J. Roy, and S. Sathaye, (2006), 'Estimating energy-augmenting technological change

in developing country industries', Energy Economics, 28, 720-29.

Sartori, I. and A. G. Hestnes, (2007), 'Energy use in the life-cycle of conventional and low-energy

buildings: a review article', Energy and Buildings, 39, 249-57.

Saunders, H. D., (1992), 'The Khazzoom-Brookes postulate and neoclassical growth', The Energy

Journal, 13(4), 131.

Saunders, H. D., (2000a), 'Does predicted rebound depend on distinguishing between energy and

energy services?' Energy Policy, 28(6-7), 497-500.

Saunders, H. D., (2000b), 'A view from the macro side: rebound, backfire, and Khazzoom-Brookes',

Energy Policy, 28(6-7), 439-49.

Saunders, H. D., (2005), 'A calculator for energy consumption changes arising from new

technologies', Topics in Economic Analysis and Policy, 5(1), 1-31.

Saunders, H. D., (2007), 'Fuel conserving (and using) production function', Working Paper, Decision

Processes Incorporated, Danville, CA.

Schipper, L., (2000), 'Editorial', Energy Policy, 28, 6-7.

Schipper, L. and M. Grubb, (2000), 'On the rebound? Feedback between energy intensities and

energy uses in IEA countries', Energy Policy, 28(6-7), 367-88.

Schipper, L., M. Josefina, L. P. Figueroa, and M. Espey, (1993), 'Mind the gap The vicious circle of

measuring automobile fuel use', Energy Policy, 21(12), 1173-90.

Schurr, S., (1982), 'Energy Efficiency and Productive Efficiency: Some Thoughts Based on the

American Experience', Energy Journal, 3(3), 3-14.

Schurr, S. H., (1983), 'Energy abundance and economic progress', Annual Review of Energy,

10(3/4), 111-17.

Schurr, S. H., (1984), 'Energy, economic growth and human welfare', in The Economics of Nuclear

Energy., L. Brookes and H. Motamen eds, Chapman and Hall, London.

Schurr, S. H., (1985), 'Energy conservation and productivity growth : Can we have both?' Energy

Policy, 13(2), 126-32.

Schurr, S. H., B. C. Netschert, V. E. Eliasberg, J. Lerner, and H. H. Landsberg, (1960), 'Energy in the

American Economy', Resources for the Future, John Hopkins University Press, Baltimore.

The Rebound Effect: an assessment of the evidence for economy-wide energy savings from improved energy efficiency 

104
The Rebound Effect: an assessment of the evidence for economy-wide energy savings from improved energy efficiency 

105

Schwarz, P. M. and T. N. Taylor, (1995), 'Cold hands, warm hearth? Climate, net takeback, and

household comfort', Energy Journal, 16(1), 41-54.

Small, K. A. and K. Van Dender, (2005), 'A study to evaluate the effect of reduced greenhouse gas

emissions on vehicle miles travelled', Prepared for the State of California Air Resources Board, the

California Environment Protection Agency and the California Energy Commission, Final Report ARB

Contract Number 02-336, Department of Economics, University of California, Irvine.

Söllner, F., (1997), 'A reexamination of the role of thermodynamics for environmental economics',

Ecological Economics, 22(3), 175-201.

Solow, J. L., (1987), 'The capital-energy complementarity debate revisited', The American Economic

Review, 77(4), 605-14.

Sorrell, S., (2007), 'Improving the evidence base for energy policy: The role of systematic reviews',

Energy Policy, 35(3), 1858-71.

Sorrell, S. and J. Dimitropoulos, (2005a), 'An assessment of evidence for a 'rebound effect' from

improvements in energy efficiency: Assessment Protocol', A report for the UK Energy Research

Centre, Sussex Energy Group, SPRU, University of Sussex, Brighton.

Sorrell, S. and J. Dimitropoulos, (2005b), 'An assessment of evidence for rebound effect from

improvements in energy efficiency: Scoping Note', Report for the Technology Policy Assessment

function of the UK Energy Research Centre, SPRU (Science and Technology Policy Research),

University of Sussex, Brighton.

Sorrell, S., E. O. Malley, S. J., and S. Scott, (2004), The Economics of Energy Efficiency: Barriers to

Cost-Effective Investment, Edward Elgar, Cheltenham, UK.

Stern, D. I., (1993), 'Energy and economic growth in the USA : A multivariate approach', Energy

Economics, 15(2), 137-50.

Stern, D. I., (1997), 'Limits to substitution in the irreversibility in production and consumption: a

neoclassical interpretation of ecological economics', Ecological Economics, 21, 197-215.

Stern, D. I., (2000), 'A multivariate cointegration analysis of the role of energy in the US

macroeconomy', Energy Economics, 22, 267-83.

Stern, D. I., (2004a), 'Elasticities of substitution and complementarity', Working Paper in Economics,

No. 0403, Rennsselaer Polytechnic Institute.

Stern, D. I., (2004b), 'The rise and fall of the Environmental Kuznets Curve', World Development,

32(Eight), 1419-39.

Stern, D. I., (2004c), 'The rise and fall of the Environmental Kuznets Curve', World Development,

32(8), 1419-39.

Stern, D. I. and C. J. Cleveland, (2004), 'Energy and economic growth', Rensselaer Working Paper

in Economics, No. 0410, Rensselaer Polytechnic Institute, Troy, NY.

Stern, N., (2007), 'Stern Review: The Economics of Climate Change', HM Treasury, London.

Sue Wing, I. and R. S. Eckaus, (2006), 'The Decline in U.S. Energy Intensity: Its Origins and

Implications for Long-Run CO2 Emission Projections', Working Paper, Kennedy School of

Government, Harvard University, Boston.

Sweeney, J. L., (1984), 'The response of energy demand to higher prices: what have we learned?'

American Economic Review, 74(2), 31-37.
Taylor, L. D., G. R. Blattenberger, and P. K. Verleger, (1977), 'The residential demand for energy',

EA-235, Electric Power Research Institute, Palo Alto, California.

Temple, J., (2006), 'Aggregate production functions and growth economics', International Review of

Applied Economics, 20(3), 301-17.

Toke, D., (1990), 'Increasing energy supply not inevitable', Energy Policy, 18(7), 671-73.

Toman, M. and B. Jemelkova, (2003), 'Energy and Economic Development: An Assessment of the

State of Knowledge', The Energy Journal, 24(4).

Train, K. E., (1994), 'Estimation of net savings from energy-conservation programs', Energy, 19(4),

423-41.

Vringer, K. and K. Blok, (1995), 'The direct and indirect energy requirements of households in the

Netherlands', Energy Policy, 23(10), 893-910.

Wall, G., (2004), 'Exergy', in Encyclopaedia of Energy: Volume 2, C.J. Cleveland ed, Elsevier

Academic Publishers, Amsterdam, New York.

Webb, M. and D. Pearce, (1975), 'The economics of energy analysis', Energy Policy, 3(4), 318-31.

Welsch, H. and C. Ochsen, (2005), 'The determinants of aggregate energy use in West Germany:

factor substitution, technological change, and trade', Energy Economics, 27, 93-111.

West, S. E., (2004), 'Distributional effects of alternative vehicle pollution control policies', Journal of

Public Economics, 88, 735-57.

Wilson, A. and J. Boehland, (2005), 'Small is Beautiful: U.S. House Size, Resource Use, and the

Environment', Journal of Industrial Ecology7, 9(1-2), 277-28.

Winther, B. N. and A. G. Hestnes, (1999), 'Solar versus Green: the analysis of a Norwegian row

house', Solar Energy, 66(6), 387-93.

Wirl, F., (1997), The economics of conservation programs, Kluwer, Dordrecht.

Worrell, J. A., M. Ruth, H. E. Finman, and J. A. Laitner, (2003), 'Productivity benefits of industrial

energy efficiency measures', Energy, 1081-98.

Xiao, N., J. Zarnikau, and P. Damien, (2007), 'Testing functional forms in energy modeling: An

application of the Bayesian approach to U.S. electricity demand', Energy Economics, 29(2), 158-66.

Yoo, S. H., (2006), 'The causal relationship between electricity consumption and economic growth

in the ASEAN countries', Energy Policy, 34(18), 3573-82.

Zachariadis, T., (2006), 'On the exploration of causal relationships between energy and the

economy', Discussion Paper 2006-05, Department of Economics, University of Cyprus.

Zarnikau, J., (1999), 'Will tomorrow's energy efficiency indices prove useful in economic studies?'

The Energy Journal, 20(3), 139-45.

Zarnikau, J., S. Guermouches, and P. Scmidt, (1996), 'Can different energy resources be added or

compared?' Energy, 21(6), 483-91.

Zein-Elabdin, E. O., (1997), 'Improved stoves in Sub-Saharan Africa: the case of the Sudan', Energy

Economics, 19(4), 465-75.

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The Rebound Effect: an assessment of the evidence for economy-wide energy savings from improved energy efficiency 

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Annex 1: Project team, expert group and contributors 

Project Team

The assessment was managed by Steve

Sorrell of the Sussex Energy Group, SPRU,

University of Sussex. The contributors

were as follows:

Sussex Energy Group (SEG),

University of Sussex

Steve Sorrell, 

John Dimitropoulos 

Surrey Energy Economics Centre

(SEEC), University of Surrey

Lester Hunt

David Broadstock

Fraser of Allander Institute and

Department Economics, University of

Strathclyde

Grant Allan

Michelle Gilmartin

Peter McGregor

Kim Swales

Karen Turner

ICEPT, Imperial College

Dennis Anderson

Matt Sommerville

Expert Group

The expert group was chosen for its

combination of expertise on energy

efficiency, energy economics and the

rebound effect. It met twice during the

course of the project, providing input to

the initial framing of the issues, literature

search, synthesis and drafting. The

members were as follows:

Jim Skea (UKERC); 

Horace Herring (Open University); 

Hunter Danskin (DEFRA); 

Tina Dallman (DEFRA); 

Ken Double (Energy Saving Trust); 

Lester Hunt (Surrey Energy Economics

Centre); 

Paolo Agnolucci (Policy Studies Institute).

Peer Reviewers

Manuel Frondel (Zentrum für Europäische

Wirtschaftsforschung, Mannheim) 

Karsten Neuhoff (Electricity Policy

Research Group, Faculty of Economics,

University of Cambridge), 

Jake Chapman
The results of the full assessment are contained in five in-depth Technical Reports, as

follows:

Technical Report 1: Evidence from evaluation studies

(Matt Sommerville, Steve Sorrell)

Technical Report 2: Evidence from econometric studies

(Steve Sorrell, John Dimitropoulos) 

Technical Report 3: Evidence from elasticity of substitution studies

(David Broadstock, Lester Hunt, Steve Sorrell)

Technical Report 4: Evidence from CGE modeling studies

(Grant Allan, Michelle Gilmartin, Peter McGregor, Kim Swales, Karen Turner)

Technical Report 5: Evidence from energy, productivity and economic growth

studies

(Steve Sorrell, John Dimitropoulos) 

In addition, there is a shorter Supplementary Note by Steve Sorrell on the graphical

analysis of rebound effects. All these reports are available to download from the UKERC

website.

Annex 2: Technical and Supplementary Reports

The Rebound Effect: an assessment of the evidence for economy-wide energy savings from improved energy efficiency 

108
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