This paper has been accepted for publication in Energy Research & Social Science (January 2020)

The limits of energy sufficiency:

A review of the evidence for rebound effects

and negative spillovers from behavioural

change

Steve Sorrell1 *

Birgitta Gatersleben2

Angela Druckman2

1 Sussex Energy Group, Science Policy Research Unit, University of Sussex, UK

2 School of Psychology, University of Surrey, UK

3 Centre for Environment and Sustainability, University of Surrey, UK

* Corresponding author

1

Abstract

‘Energy sufficiency’ involves reducing consumption of energy services in order to minimise the

associated environmental impacts. This may either be through individual actions, such as reducing

car travel, or through reducing working time, income and aggregate consumption (‘downshifting’).

However, the environmental benefits of both strategies may be less than anticipated. First, people

may save money that they can spend on other goods and services that also require energy to

provide (rebounds). Second, people may feel they have ‘done her bit’ for the environment and can

spend time and money on more energy-intensive goods and activities (spillovers). Third, people may

save time that they can spend on other activities that also require energy to participate in (time-use

rebounds). This paper reviews the current state of knowledge on rebounds and spillovers from

sufficiency actions, and on time-use rebounds from downshifting. It concludes that: first, rebound

effects can erode a significant proportion of the anticipated energy and emission savings from

sufficiency actions; second, that such actions appear to have a very limited influence on aggregate

energy use and emissions; and third, that downshifting should reduce energy use and emissions, but

by proportionately less than the reduction in working hours and income.

KeywordsRebound effects, negative spillovers, moral licensing, time-use rebound effects, downshifting, energy

sufficiency

1 Introduction

‘Energy sufficiency’ refers to the idea that people should limit their consumption of energy services

in order to minimise the associated environmental impacts and/or to improve their quality of life.

The concept was first introduced by Daly [1] and Sachs [2] and is gaining increasing attention as a

promising approach to mitigating climate change. It is closely linked to broader debates about

equity, sustainability and ‘de-growth’ and to traditional thought about the benefits of frugality [3,4].

Both individuals and organisations may reduce their consumption of energy services for a variety of

reasons and those actions may be either encouraged or obstructed by public policy, and either

enabled or constrained by the systems that provide those services. For example, people may reduce

2

car travel for financial, health or environmental reasons and they may be encouraged to do so by

rising fuel prices or discouraged by poor quality public transport. However, most of the literature on

energy sufficiency focuses upon intentional, voluntary actions by individuals whose primary aim is to

reduce their ‘carbon footprint’ [5-7]. From this perspective, energy sufficiency actions may be

interpreted as a particular type of ‘pro-environmental behaviour’ (PEB), focused upon minimising

the environmental impacts of energy use [8].

It is commonly assumed that energy sufficiency actions will translate straightforwardly into lower

energy use and greenhouse gas (GHG) emissions [17], but this is not necessarily the case. For

example, consider someone who chooses to travel by public transport rather than by car owing to

their concern about climate change. There are at least three reasons why the energy and

environmental benefits of this action may be less than anticipated:

1. Rebounds: they may save money that they can subsequently spend on other goods and

services that also require energy to provide [9].

2. Spillovers: they may feel they have ‘done their bit’ for the environment and can

subsequently spend time and money on more energy-intensive goods and activities [10].

3. Time-use rebounds: they may save time that they can subsequently spend on other activities

that also require energy to participate in [11].

Sufficiency actions may therefore ‘free-up’ financial, moral and temporal resources that can

subsequently be spent on other goods, services and activities that also involve energy use and

emissions - either directly or indirectly along the relevant supply chain. The concept of ‘moral

resources’ assumes that people track and balance the environmental ‘credits’ and ‘debits’ from

different activities and that credits from ‘good’ actions can compensate for subsequent ‘bad’ actions.

This may not necessarily be the case, but other considerations and motivations can lead to similar

outcomes [10,12].

In practice, rebounds, spillovers and time-use rebounds are practically interdependent (e.g.

environmentally damaging activities require time and money to enjoy) and psychologically

interlinked (e.g. the same motivations may contribute to each). Both individually and collectively,

they may either offset or reinforce the original energy savings. For example, some sufficiency

actions may cost rather than save money (a negative rebound), others may encourage rather than

discourage subsequent sufficiency actions (a positive spillover), and others may take more rather

than less time (a negative time-use rebound. These different possibilities are summarised in Table 1.

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Table 1 Classifying rebounds and spillovers from sufficiency actions – using the example of commuting by bus rather than car

Rebounds

(financial resources)

Spillovers

(moral resources)

Time-use rebounds

(temporal resources)

Offsets the initial

energy savings

Positive rebound

(e.g. if public transport is

less expensive than car

travel, more money is

available to spend on a

long-distance vacation)

Negative spillover

(e.g. if public transport is

less carbon intensive than

car travel, this may ‘licence’

a decision to take a long-

distance vacation)

Positive time-use

rebound

(e.g. if public transport

takes less time than car

travel, more time is

available to spend watching

television)

Reinforces the initial

energy savings

Negative rebound

(e.g. if public transport is

more expensive than car

travel, less money is

available to spend on an

long-distance vacation)

Positive spillover

(e.g. if public transport is

less carbon intensive than

car travel, this may

reinforce a personal

commitment to avoid long-

distance vacations)

Negative time-use

rebound

(e.g. if public transport

takes more time than car

travel, less time is available

to spend watching

television)

Sufficiency actions may also trigger changes in the behaviour of other individuals and organisations.

For example, if a large number of people give up car travel this will reduce the demand for gasoline,

thereby reducing gasoline prices and the cost of car travel and encouraging other people to drive

more. Alternatively, by setting a positive example such actions may encourage friends and families to

drive less and thereby contribute to a broader change in social norms. The first of these is a

macroecomic rebound effect, while the second may be labelled an interpersonal spillover. Again,

these mechanisms may either offset or reinforce the original energy and emission savings.

Time-use rebounds have received much less attention than rebounds and spillovers, but there is a

growing literature on the relationship between working hours, energy use and carbon emissions

[13,14]. This is relevant to a more comprehensive form of energy sufficiency, involving voluntary

reductions in working hours and income – commonly known as downshifting. Since downshifting

households reduce aggregate consumption they should also reduce their energy use and emissions.

However, the size of those reductions will depend upon how households spend their additional free

time - for example, on childcare or on long-distance vacations. Changes in the pattern of time use

(i.e. time-use rebounds) may potentially offset some of the energy and emission savings from lower

consumption.

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This paper summarises the current state of knowledge on rebounds and spillovers from sufficiency

actions, and on time-use rebounds from downshifting. It discusses the factors influencing the sign

and magnitude of each effect and reviews the empirical evidence on their importance in different

contexts. The paper does not discuss energy efficiency improvements, although these also trigger

rebounds and spillovers (Box 1 and Table 2). The focus is upon mechanisms that offset the energy

and emission savings from sufficiency actions (bottom row in Table 1), although it is recognised that

parallel mechanisms may sometimes reinforce those savings (top row in Table 1) and that (for

spillovers in particular) the balance between the two is unclear.

The paper is structured as follows. Section 2 looks more closely at energy sufficiency actions,

including the difficulties of defining such actions and the psychological motivations behind them.

Section 3 reviews the economic evidence on the rebound effects from sufficiency actions, focusing

upon estimates of the size of those rebounds. Section 4 reviews the psychological evidence on the

spillovers from sufficiency actions, focusing upon the causes of those spillovers and the correlation

between individual sufficiency actions and overall environmental impacts. Section 5 reviews the

evidence for the environmental benefits of downshifting, focusing upon time-use rebounds and

estimates of the elasticity of environmental impacts with respect to working hours. In each case, we

tabulate and summarise the the main empirical studies in the area and discuss their implications.

The paper concludes by highlighting the implications of this evidence and suggesting how these

different streams of research could be linked.

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Box 1 Rebounds and spillovers from improved energy efficiency

Rebounds and spillovers are associated with energy efficiency improvements as well as sufficiency actions.

However, there are some important differences between the two. First, energy efficiency improvements

reduce the effective price of the relevant energy service, whereas sufficiency actions do not. As a result, energy

efficiency improvements lead to both income and substitution effects, whereas sufficiency actions only lead to

income effects [13].1 Second, the motivation for energy efficiency improvements (e.g. saving money) may

differ from the motivation for sufficiency actions (e.g. reducing emissions), so there may be corresponding

differences in the nature, sign and magnitude of any resulting spillovers [14]. Third, energy efficiency

improvements will only lead to time-use rebounds if there are associated improvements in time efficiency [15].

Table 2 identifies two perspectives (economic and psychological) on the consequences of efficiency

improvements and sufficiency actions, and indicates the key mechanism involved and the quality of evidence

on the determinants and size of the resulting effects. Empirical studies from an economic perspective typically

involve statistical analysis of historical data and implicitly assume that individual preferences for different

goods, services and activities remain unchanged [16], while empirical studies from a psychological perspective

typically use experiments and surveys and investigate how those preferences change [10]. As a result,

economic studies ignore psychological responses and psychological studies ignore economic responses.

Although complementary, there are only weak links between these two areas of literature [17,18].

Table 2 Comparing rebounds and spillovers from energy efficiency and sufficiency actions

Energy efficiency improvement

(technical change)

Energy sufficiency action

(behavioural change)

Economic perspective

(fixed preferences)

Efficiency rebounds

Key mechanism: income and substitution effects

Evidence on determinants: Good

Evidence on impacts: Medium

Sufficiency rebounds

Key mechanism: income effects

Evidence on determinants: Good

Evidence on impacts: Medium

Psychological perspective

(changed preferences)

Efficiency spill-overs

Key mechanism: moral licensing

Evidence on determinants: Poor

Evidence on impacts: Poor

Sufficiency spill-overs

Key mechanism: moral licensing

Evidence on determinants: Good

Evidence on impacts: Poor

1 The income effect expresses the impact of increased purchasing power on consumption, while the substitution effect describes how consumption is affected by changing relative prices.

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2 Energy sufficiency actions

The concept of energy sufficiency has been explored from a variety of perspectives including

sustainable consumption [19], energy justice [20], energy economics [21] and philosophy [22], but in

every case the practical implications overlap with strategies that have been promoted for decades

under headings such as energy conservation [23]. The concept is primarily relevant to affluent

households/countries and has attracted particular interest in the EU where it is being promoted by

NGOs such as the European Council for Energy Efficient Economy (ECEEE) [24].2

Despite this increasing prominence, there is no consensus on the meaning of energy sufficiency.

Some authors interpret energy sufficiency as a goal or an outcome defined by a level of energy

service consumption that is consistent with equity, well-being and environmental limits3, while

others interpret it as a set of actions or a strategy defined by intentional reductions in energy service

consumption.4 The first interpretation raises conceptual, ethical and practical questions about how

to operationalise environmental limits, while the second overlaps with more familiar concepts such

as ‘behavioural change’, ‘curtailment’ and ‘energy conservation’. The two interpretations are linked,

since achieving the goal of energy sufficiency requires actions to achieve that goal. But if the goal is

defined in terms of environmental limits, this may be approached through both reducing

consumption of energy services (energy sufficiency) and using less energy to provide those services

(energy efficiency) - with the benefits of the former depending upon the level of the latter.

We adopt the second interpretation in what follows and define energy sufficiency actions as

reductions in the consumption of energy services, with the aim of reducing the energy use and

environmental impacts associated with those services. Energy services are normally interpreted as

services that require direct energy consumption for their provision, such as heating, lighting and air-

conditioning. Hence, a narrow interpretation of energy sufficiency would confine attention to actions

affecting those services - such as reducing car travel or lowering internal temperatures. However,

some of the literature on energy sufficiency includes actions that target ‘embodied’ or ‘indirect’

energy use and emissions along the relevant supply chain – such as shifting to a plant-based diet to

reduce methane emissions from cattle [25]. Once these actions are included, the boundary between

energy sufficiency and broader ‘pro-environmental behaviour’ becomes blurred. The sum of direct

and indirect emissions is commonly referred to as the carbon footprint’ or ‘GHG footprint’, although

2 See: https://www.energysufficiency.org/3 “… energy sufficiency is a state in which people’s basic needs for energy services are met equitably and ecological limits are respected” [6].4 “…energy sufficiency refers to changes in individual behaviours that lead to lower demand for energy services” [17].

7

an equivalent term is rarely used for energy consumption. When assessing the environmental

benefits of changes in consumption, both direct and indirect emissions must be taken into account.

Many energy services can be specified in measurable terms (e.g. internal temperature, passenger

kilometres) but this ignores their multiple attributes and dimensions. For example, thermal comfort

depends upon internal air temperature, but also upon air velocity, humidity, activity levels, clothing,

external temperature and social conditioning. The same energy service may be delivered by different

energy carriers (e.g. electricity or gas for heating) and by different energy systems (e.g. cars or buses

for mobility), and therefore with varying levels of utility, energy efficiency and GHG emissions.

Hence, ‘reducing’ energy service consumption can mean different things and can have different

outcomes depending upon how those services are understood, measured, provided and valued.

Areas that offer particular potential for energy sufficiency actions are transport, heating, electricity

use and food, since these account for around three quarters of the GHG footprint of a typical

European household [26-29]. We highlight some commonly cited examples.

In the UK, some 22% of car trips are of less than two miles and these account for approximately 5%

of car emissions [30]. Hence, a widely advocated sufficiency action is to walk or cycle for short

journeys. An equally effective measure is to reduce flying, which accounted for approximately 5% of

the GHG footprint of an average UK household in 2018 and a much larger fraction for high-income

groups [31].5 But while offsetting aviation emissions is becoming increasingly popular, very few

people are prepared to restrict their flying [33-35].

Measures to reduce space heating demand are also advocated, as this accounts for around 15% of

household carbon footprints [36]. Options include reducing thermostat settings and turning off

radiators in unoccupied rooms. Between 1970 and 2011, the average (whole-house) internal

temperature of centrally-heated UK homes rose from 13.7°C to 17.7°C [37], while households in

many other European countries enjoy higher average temperatures. Hence, although millions of

households endure fuel poverty, others enjoy relative thermal comfort and should have scope for

this type of action.

A related but less frequently advocated measure is to limit the floor area of dwellings, as this is

strongly correlated with energy use [38-40]. This may be popular with older people and those

seeking to reduce their housing costs, but income growth and changing demographics (e.g. more

5 For illustration, a return flight from London to New York generates approximately the same GHG emissions as heating an average EU home for a year [32].

8

single-person households) create powerful pressures in the opposite direction (e.g. average floor

space per capita in the EU grew from 15.2m2 in 1990 to 18.6 m2 in 2010 [41]).

Commonly advocated sufficiency actions for electricity consumption include using smaller and fewer

appliances, switching off standby appliances, switching off lighting in unoccupied rooms, washing

clothes at lower temperatures and air drying rather than tumble drying - although each of these

have a relatively modest impact on energy use and emissions [42]. Measures that conserve both

energy and water can also reduce water bills, mitigate water shortages and reduce upstream energy

consumption.

With food consumption, the most popular sufficiency actions are to reduce food waste, buy locally

grown food, avoid air-freighted products and shift away from meat and dairy. An average UK

household throws away one third of their food purchases [43], and shifting to a plant-based diet is

the third most effective GHG mitigation option after car-free-living and giving up flying [42].

In classifying such actions, it is useful to make the following distinctions:

Restraint versus substitution: Sufficiency actions may involve restraint such as renouncing

specific car journeys altogether, or substitution such as using public transport or video-

conferencing instead. Whether substitution is classified as energy sufficiency or energy

efficiency depends upon how the relevant energy service is defined (e.g car travel or mobility).

Reducing versus increasing utility: Alcott [44] defines energy sufficiency actions as those

involving a loss of welfare or utility, thereby distinguishing them from energy efficiency

improvements where utility is unchanged. Whether specific actions involve a loss of utility will

depend upon individual preferences, trade-offs between different values and goals, the degree

of support or opposition from various social groups, and the extent to which various technical,

infrastructural and economic variables facilitate or obstruct the relevant action. Since sufficiency

is commonly advocated for its non-material benefits and its contribution to improved quality-of-

life, the claim that it reduces utility appears problematic.

Voluntary versus encouraged: Sufficiency actions are an individual choice, but are influenced by

the technical, economic and social context and may be incentivised or required by public policy.

For example, walking and cycling can be encouraged by high-density land-use developments,

dedicated cycle lanes and adequate cycle parking. Prescriptive policies that impose limits on

consumption are rarely used since these conflict with individual liberty - although there are

exceptions such as the regulation in North Rhine-Westphalia that limits the maximum floor

space (m2/person) for recipients of housing allowances [45]. More common are less prescriptive

9

policies that encourage voluntary reductions in consumption, such as information programs and

social comparisons. Policies that impose tougher energy efficiency standards on larger

buildings/appliances/vehicles may incentivise the purchase of smaller

buildings/appliances/vehicles [46], while carbon pricing provides incentives for energy efficiency,

energy sufficiency and fuel substitution throughout the economy.

Individual versus social: Sufficiency actions motivated solely by collective benefits may not

attract widespread adherence since those benefits are vulnerable to free-riding. However,

people are more likely to adopt sufficiency actions if they feel social pressure to do so, if they act

in collaboration with others (e.g. neighbourhood groups) or if they identify with a broader social

trend or movement [47]. For example, changes in social norms in areas such as recycling can

encourage individual action which in turn can reinforce those changes in social norms [48].

Energy versus environmental impacts: The climate benefits of sufficiency actions depend upon

the (direct and indirect) emission intensity of the relevant energy services. Some actions may

lead to large energy savings but small emission savings (and vice versa) and the latter will decline

as energy systems decarbonise and energy efficiency improves. If sufficiency actions reduce the

consumption of energy services with a low carbon intensity and if rebounds and spillovers

increases the consumption of energy services with a high carbon intensity, the net result may be

to increase aggregate emissions (‘backfire’).

Having defined energy sufficiency actions, we now consider how rebounds and spillovers may affect

the environmental benefits of such actions.

3 Rebound effects from energy sufficiency actions

3.1 Determinants of rebound effects

The term ‘rebound effects’ derives from energy economics where it refers to a range of mechanisms

that reduce the anticipated energy savings from improved energy efficiency [16]. Analogous

mechanisms apply to energy sufficiency actions, but these are less explored. Unlike improved

energy efficiency, sufficiency actions do not reduce the effective price of the relevant energy service,

and most sufficiency actions do not require investment in durable goods or entail additional

operating costs. But many sufficiency actions will save on energy costs - and in some cases will save

on other types of cost, such as car maintenance, food purchases and water bills. Such cost savings

are equivalent to an increase in real income and will be re-spent on other goods and services that

also require energy to provide.

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Assuming consumer preferences remain unchanged, the pattern of re-spending will be similar to

historic patterns and can be estimated from econometric evidence on the the marginal propensity to

consume different goods and services - as indicated by expenditure elasticities. Normal goods will be

associated with a positive rebound effect and inferior goods with a negative rebound effect [49].6

The overall impact will depend upon the distribution of re-spending between different goods and

services, together with the energy/emission intensity of those goods and services relative to that of

the energy service. The re-spending may be associated with both direct energy use/emissions (e.g.

spending cost savings on more lighting) and indirect energy use/emissions (e.g. spending cost

savings on more ready-meals). The use of expenditure elasticities should provide an unbiased

estimate of the sufficiency rebound effect since, in the absence of any change in the price of energy

services, there is no substitution effect [50].

The size of the rebound effect will depend upon the pattern of re-spending. Goods with low

energy/emissions intensity may contribute a large rebound effect if they constitute a large share of

total re-spending, and vice-versa. To illustrate, Figure 1 compares the GHG-intensity of expenditure

(tCO2e/£), the share of total expenditure (%) and the share of total GHG emissions (%) of 17

categories of goods and services for an average UK household [49]. This shows that spending £1 on

electricity and gas is associated with up to ten times more GHG emissions than spending £1 on other

commodities. But the emission intensity of energy commodities is offset by their small share of total

expenditure, with the result that energy consumption only accounts for ~38% of an average UK

household’s GHG footprint, split between 27% domestic energy (i.e. electricity, gas and other fuels)

and 12% vehicle fuels (Figure 1).

Figure 1 aggregates household expenditure into only 17 categories which obscures the variation in

energy and emission intensity between different products within each category. For example, some

food products (e.g. beef) are far more emission-intensive than others (e.g. potatoes) [51] and

products that have similar emission intensity by weight (tCO2e/kg) may differ in emission intensity by

value (tCO2e/£) [52]. In addition, some products are relatively emission-intensive (e.g. beef owing to

methane emissions from livestock) but less energy-intensive, and vice versa. As a result of these

variations, the size of the rebound effect will depend upon the distribution of re-spending both

within and between the expenditure categories in Figure 1 as well as upon the metric used (energy,

carbon or GHGs).

6 Consumption of normal goods increases with income (positive income elasticity) while consumption of inferior goods decreases with income (negative income elasticity). An example of the latter could be ‘value’ food products that are replaced with high quality products as income increases.

11

The rebound effect will be larger for sufficiency actions that reduce other costs. For example, if a

household gives up car travel altogether, they will save on maintenance, insurance, and vehicle tax

as well as on road fuels. These additional cost savings will also be available for re-spending, thereby

amplifying the rebound effect. In addition, since expenditure on car insurance and maintenance is

less energy and emission-intensive than expenditure on road fuels, the rebound effect associated

with the former will be proportionately larger than that associated with the latter [49].

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Figure 1 Estimates of the GHG-intensity of expenditure, share of total expenditure and share

of total GHG emissions by category for an average UK household

0.000

0.001

0.002

0.003

0.004

0.005

0.006

0.007

0.008

GH

G in

tens

ity (t

CO

2e/£

)

0%

2%

4%

6%

8%

10%

12%

14%

16%

Expe

nditu

re s

hare

(%)

0%

2%

4%

6%

8%

10%

12%

14%

Shar

e of

GH

G e

mis

sion

s (%

)

Source: Chitnis et al [49]

13

Note: Estimates include both direct and indirect emissions and allow for the variation of product taxation between

categories. The latter contributes to the comparatively low emission intensity of vehicle fuels compared to electricity and

gas.

Re-spending the cost savings from sufficiency actions may also lead to various macroeconomic

rebound effects as a result of induced changes in the price of goods, services and factor inputs

throughout the economy. In particular, the choice of some people to reduce their energy (service)

consumption may trigger a reduction in energy prices that will in turn encourage other people to

increase their energy (service) consumption [44]. Similar impacts may occur in markets for goods

that are complements to the relevant energy service/carrier (e.g. cars are a complement to

gasoline), but there may be offsetting impacts in markets for goods that are substitutes to the

energy service/carrier.

Depending upon both the number of people adopting sufficiency actions and the structure of the

relevant markets, these macroeconomic effects may occur at the regional, national or global level.

The fall in energy prices should benefit low-income groups, but there may be offsetting price

increases for substitute goods and any reductions in energy prices will also benefit the wealthy.

Moreover, if the affected energy carrier is used for multiple energy services, it is possible that the

same people who chose to reduce consumption of one energy service (e.g. electric heating) will

increase their consumption of another energy service (e.g. lighting) to take advantage of the lower

energy prices. The significance of this outcome will depend upon the energy price elasticity of the

relevant energy service and whether people are prepared to restrict their consumption of multiple

energy services.

3.2 Environmental impacts of rebound effects

Assuming preferences remain unchanged, the rebound effects from sufficiency actions may be

quantified by combining estimates of the expenditure elasticities of different goods and services

(derived from econometric analysis of household expenditure data) with estimates of the

energy/emission intensity of those goods and services (derived from environmentally-extended

input-output models) [53].7 Quantification of macroeconomic rebound effects requires the use of

computable general equilibrium (CGE) models [54]. Both approaches have been used to estimate the

rebound effects from energy efficiency improvements, but there are fewer applications to energy

sufficiency actions. Table 3 summarises the most prominent studies in this area, all of which use

7 Multiregional input-output models allow the energy/emission intensity of imported goods to be estimated separately from that of domestically produced goods. A simpler approach is to assume that the energy/emission intensity of imports is the same as that of domestically produced goods.

14

input-output models and hence neglect macroeconomic adjustments. Box 2 summarises the key

findings of these studies.

Table 3 categorises the modelled sufficiency actions by the area of consumption they affect and

expresses the estimated rebound effects as a percentage of the anticipated energy or emissions

savings from the sufficiency action. Most of the studies measure rebound effects in terms of GHG

emissions, but they differ in terms of the gases covered and conversion factors assumed. The

diverging results for energy and GHG rebounds in Lenzen and Dey [55] illustrate the importance of

the choice of metric. The studies also differ in the methodology used, the types of sufficiency action

investigated, the level of commodity disaggregation employed and the region studied - all of which

influence the results. For example, the estimates of Chitnis et al. [49] are influenced by the high road

fuel taxation in the UK, while those of Bjelle et al [56] are influenced by the low carbon intensity of

Swedish electricity generation.

Overall, the studies suggest that rebound effects tend to be modest (e.g. 5 to 15%) for sufficiency

actions affecting heating and electricity, larger (e.g. 15 to 50%) for actions affecting transport fuels

and very large (e.g. 50 to >100%) for actions affecting food consumption. Hence, they suggest that

rebound effects can erode a significant proportion of the anticipated energy and emission savings

from sufficiency actions. However, the estimates are sensitive to the metric used (i.e. energy, carbon

or GHGs), to the emission intensity of electricity generation, to the level of commodity taxation and

to the pattern of re-spending. The last three variables vary widely from one household/country to

another, but the evidence suggests that rebound effects tend to be larger for low-income

households and for households in emerging economies [57].

The studies also suggest that taxing energy commodities leads to larger rebound effects, since it

amplifies the cost savings from sufficiency actions [49,57]. But higher taxation also provides an

incentive to reduce direct energy consumption. The net effect of taxation on energy use and

emissions will depend upon the own-price elasticity of the relevant energy commodities, the

energy/emission intensity of expenditure on energy commodities relative to that of other goods and

services, the use of taxation revenues, and upon various macroeconomic adjustments that can only

be captured through macroeconomic modelling. A key limitation of the existing literature is the

absence of such modelling studies for sufficiency actions - despite a growing number of applications

to energy efficiency improvements.

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Table 3: Empirical estimates of the rebound effects from sufficiency actions

Study Region No. of

expenditure

categories

Areas targeted

by sufficiency

actions

Measure of

environmental

impact

Estimated rebound

effect (%)

Alfreddso

n [58]

Sweden 300 Food, travel,

housing

Energy use

(Carbon

emissions)

Food: 300% (200%)

Travel: 30% (10%)

Housing: 14% (20%)

Total: 33% (20%)

Lenzen

and Dey

[55]

Australia 150 Food Energy use

GHG emissions

Energy: 112-123%

GHGs: 45-50%

Grabs [59] Sweden 117 Food Energy use

GHG emissions

Energy: 95-104%

GHGs: 49-56%

Murray

[60]

Australia 36 Transport,

electricity

GHG emissions Transport: 15-17%

Electricity: 4.5-6.5%

Druckman

et al [61]

UK 17 Heating,

transport food

GHG emissions Heating: 7%

Transport: 25%

Food: 51%

Chitnis et

al [49]

UK 20 Heating,

transport, food

GHG emissions Heating: 12-17%

Transport: 25-40%

Food: 66-106%

Bjelle et al

[56]

Norway 200 Transport,

utilities, food,

waste, other

GHG emissions Transport: 57-83%

Shelter: 0%

Clothing: 61-89%

Food: 11-16%

Paper: 129-190%

Plastic: 65-95%

Note: In each case, the measure of environmental impacts includes both direct and indirect impacts.

16

Box 2 Summary of studies estimating the rebound effects from sufficiency actions

Alfredsson [58] investigates sufficiency actions in food, travel and housing (heating and electricity) by Swedish households. She estimates that a shift towards ‘green’ diets would reduce food-related energy consumption by 5% and food expenditure by 15%, but re-spending the cost savings would lead to a rebound effect for carbon emissions of ~200%. This high rebound results from food products having a lower GHG-intensity of expenditure than domestic energy and transport fuels, and from a proportion of re-spending being directed towards the latter. Alfredsson estimates rebound effects of 10% and 20% for her green travel and green housing scenarios respectively, and a rebound effect of 20% for all three scenarios combine. The latter involves a comprehensive set of sufficiency actions, but only reduces carbon emissions by 13%. Such modest reductions may rapidly be outweighed by increased emissions from growing consumption.

Carlsson-Kanyama et al [62] use a similar approach to Alfredsson, but find that a shift to ‘green’ food consumption reduces overall energy consumption. This result follows in part from their assumption that ‘green’ diets are more expensive (owing to the higher cost of locally produced organic food), thereby leading to a negative rebound effect. The importance of variations in the price and quantity of individual products is also emphasised by Girod and de Haan [63], who find that Swiss households with low GHG emissions spend more on high-quality goods and services. But while shifting towards higher price goods will reduce the rebound effect, the aggregate impact will also depend upon the relative emission intensity of high and low price goods. For example, Thiesen et al. [64] compare two Danish cheese products - one with ‘traditional’ packaging and the second with ‘convenience’ packaging. Since the latter is more expensive, purchasers of the traditional product save money that can be spent upon other goods and services. Using life-cycle analysis, Thiesen et al. [64] estimate that the traditional cheese is three times more emission-intensive, but this increases to seven times when the re-spending is allowed for. In practice, ‘green’ products (e.g. traditional, organic, locally produced) may often be more energy/emission intensive than standard products.

Lenzen and Dey [55] also explore the impacts of a low-cost ‘green diet’, involving a 30% reduction in food expenditure. This achieves significant reductions in food-related energy consumption and GHG emissions, but once re-spending is allowed for, total energy consumption increases by 4-7%. They estimate a rebound effect of 112-123% for energy consumption, but only 45-50% for GHGs owing to the large reductions in methane emissions from livestock.

The Lenzen and Dey study has the drawback that the ‘green’ diet has a lower calorific content than a traditional diet. Grabs [59] overcomes this problem by modelling a switch to a vegetarian diet while holding calorific content constant. This reduces food expenditure by 10%, food-related energy consumption by 16% and food-related GHG emissions by 20%, but the re-spending leads to rebound effect of 95-104% for energy and 49-56% for GHGs. Grabs also finds that rebound effects are larger for low income groups. The reduction in food expenditure assumed by Grabs is consistent with evidence on the actual expenditures of vegetarian households in the US [65].

Murray [60] models a mix of energy efficiency improvements and sufficiency actions by Australian households. For households with median income, reducing vehicle use leads to a rebound effect for GHG emissions of 15-17%, while reducing electricity use leads to a rebound effect of 4.5-6.5%. One reason rebound effects are larger for reducing vehicle use is that additional savings are made on car maintenance and other costs (although gasoline taxation is low in Australia), while one reason they are small for electricity use is that Australian electricity is carbon intensive (>60% coal-fired). Murray also finds that rebound effects are larger for low income groups.

Druckman et al [61] model three sufficiency actions by UK households, namely: reducing internal temperatures by 1°C; eliminating food waste; and replacing car travel with walking or cycling for trips less than two miles.

17

They estimate that re-spending the associated cost savings leads to rebound effects of 7%, 51% and 25% respectively, or 34% for the three actions combined. In the case of the latter, spending all the cost savings on the least GHG intensive category leads to a rebound of 12%. While individual products within each category may have a lower GHG-intensity, re-spending all the cost savings on these products seems unrealistic.

Chitnis et al [49] model the same actions as Druckman et al [61], and estimate that rebound effects are modest (12-17%) for reducing internal temperatures, larger (25-40%) for reducing vehicle use and very large (66-106%) for reducing food waste. These difference are explained by the GHG-intensity of expenditure (in £/tCO2e) on each of these categories (which is high for heating fuels, lower for vehicle fuels and lower still for food products) relative to the average GHG-intensity of re-spending. Differences in the GHG-intensity of expenditure on each category are in turn influenced by the level of taxation on each category - which is low for heating fuels but very high for vehicle fuels. Chitnis et al also find that rebound effects are larger for low-income groups (Figure 2) because they spend a greater proportion of their cost savings on necessities such s food and heating that tend to be relatively GHG-intensive.

Finally, Bjelle et al [56] model a comprehensive set of 34 actions by Norwegian households, including common energy sufficiency actions such as reducing car travel and less common actions (with smaller energy savings) such as increasing the lifetime of household goods and reducing plastic use. They estimate that, in the absence of rebound effects, these would reduce aggregate GHG emissions by 58%. Bjelle et al model average, marginal and ‘green’ re-spending patterns, where the latter avoids re-spending on the most emission-intensive products. This leads to estimated rebound effects of 59%, 46% and 40% respectively for all the actions combined. Some of the actions have very large rebound effects, such as 108% for reducing clothes purchases and 353% for walking instead of using the train. Since Norwegian electricity generation is low-carbon (mostly hydro) the sufficiency actions affecting electricity use lead to an increase in aggregate emissions.

Figure 2 Estimates of the rebound effect from reducing vehicle use for different income groups in the UK

048

121620242832364044485256606468

Q1 Q2 Q3 Q4 Q5 All

Reb

ound

(%)

Direct (gas, electricity, other fuels, vehicle fuels) Embodied TotalQuintile

Source: Chitnis et al [49]

18

4 Spillovers from energy sufficiency actions

4.1 Determinants of spillovers

The term spillovers derives from environmental psychology, where it is normally used in relation to

the broader category of ‘pro-environmental behaviours’ (PEBs). PEBs include energy sufficiency

actions, but also behaviours that have a limited or ambiguous impact on energy use such as buying

organic food. Spillovers occur when the adoption of a PEB in one domain (e.g. travel) makes the

subsequent adoption of a PEB in another domain (e.g. food) either more or less likely.8 These

contrasting outcomes are termed positive spillovers and negative spillovers respectively [10]. While

positive spillovers reinforce the energy and emission savings from the PEB, negative spillovers offset

those savings (Table 1).

Environmental psychologists have proposed a number of models to explain spillovers [10] which

typically emphasise the motivations people have for undertaking the initial behaviour together with

the social feedback they receive about that behaviour (e.g., being praised for being environmentally

friendly). Positive spillover appears more likely when people have strong environmental values,

when the behaviour is motivated by an environmental goal, and when social feedback reinforces

pro-environmental behaviour. Conversely, negative spillover appears more likely when people have

weak environmental values, when the behaviour is primarily motivated by financial or other goals

and when social feedback fails to reinforce pro-affirmative behaviour. Evidence suggests that the

financial or other costs (e.g. time, inconvenience) of the original behaviour play an important role -

with costly PEB being associated with a strengthening of moral self-identity and thereby positive

spillover, and low-cost behaviour being associated with negative spillover [66].

PEBs frequently cost time, money or inconvenience, but may nevertheless be perceived as the ‘right’

thing to do. Such behaviours are therefore partly motivated by guilt [67] and it is guilt that underlies

the concept of moral licencing. The idea here is that doing something ‘good’ makes people feel less

guilty about subsequently doing something ‘bad’ [68]. Hence, adopting a particular moral behaviour

makes people more likely to adopt subsequent ‘immoral’ behaviours.

Moral licencing has been demonstrated in consumer, social, health and other domains (e.g. eating a

healthy dinner gives a licence to eat cake for dessert) [69], but there are fewer studies of energy-

related behaviour. The most robust studies use laboratory or field (quasi-) experiments and include a

8 Spillovers may also occur between the same behaviour in different locations - such as recycling at home encouraging recycling at work.

19

control group. For example: Tiefenbeck et al [70] find that households that receive weekly feedback

on water consumption reduce their water use but increase their energy use relative to a control

group (although Truelove et al [10] question the statistical significance of this result). McCoy and

Lyons [71] find that households exposed to information and time-of-use pricing reduce their energy

use but subsequently adopt fewer energy efficiency measures than a control group. Meijers et al [72]

find that people who donate to a charity are subsequently less willing to engage in environmental

behaviour or to support environmental policy initiatives. Harding and Rapson [73] find that

households who sign up to a program to offset their carbon emissions subsequently increase their

electricity consumption. Similarly, Jacobsen et al [74] find that households who buy into a green

power program at the minimum level increase their electricity consumption (although households

who buy in at a higher level do not, perhaps because they have a higher level of environmental

commitment).

Other studies use focus groups or surveys to explore the reasons for adopting or not adopting PEBs

in different domains – including moral licensing. For example, Miller et al [75] find that focus group

participants do not feel a need to be environmentally friendly on vacation if they engage in PEBs at

home [75]. Hope et al [76] find that focus group participants highlight their PEBs in some areas to

reduce their feelings of guilt for environmentally damaging behaviours in other areas, and to defend

their ‘green’ credentials in social situations. Capstick et al [77] survey households in seven countries

and find that moral licensing is widely endorsed and that it predicts inconsistent behaviour in

different domains - although this effect is moderated by environmental identity.

A third group of studies uses surveys to investigate correlations between different types of

environmental behaviour. For example, Noblet and McCoy [78] find that survey participants who

report engaging in sufficiency actions are less likely to support sustainable energy policy (although

again, this finding did not hold for those with the strongest environmental values). Klöckner et al [79]

find that Norwegian electric car owners drive more than conventional car owners and report less

obligation to reduce car use. Alcock et al [80] find that environmental attitudes predict PEBs within

the home but not discretionary flying behaviour, while Barr et al [81] find that survey respondents

who report the most PEBs at home also take more flights.

Overall, there is strong evidence that negative spillovers are prevalent within and across a variety of

domains, including many that are relevant to energy sufficiency. However, the size of these effects

may be relatively modest [66,69,82,83] and they are both balanced by instances of positive spillover

and moderated by environmental identity. Negative spillover appears more likely to occur when

20

people have weak environmental values, when the initial action involves little financial or other cost

and when people feel less need for consistency in their behaviours [67,84-86]. The importance of

costly actions is particularly relevant for climate policy, as many attempts to encourage sufficiency

actions focus upon the cost savings from those actions. But not only does re-spending the cost

savings create a rebound effect, emphasising those cost savings could encourage negative spillover

and thereby amplify that rebound effect.

4.2 Environmental impacts of spillovers

The environmental impacts of spillovers will depend upon whether the induced behaviour is more or

less energy/emission-intensive than the original behaviour. For example, if engaging in recycling

encourages less car use (positive spillover) the GHG benefits of recycling will be greatly enhanced,

but if it encourages more car use (negative spillover) the result will be significantly higher GHG

emissions.

Environmental psychology focuses upon identifying the determinants of spillovers rather than

quantifying their impacts, so the environmental impact of spillovers remains uncertain [87]. Many

studies rely upon self-reported behaviour, which is sensitive to response bias and may not reliably

reflect actual behaviour [84,88]. Behaviour measures are rarely weighted by their relative

environmental impact and there is a tendency to study low-impact behaviours in GHG terms (e.g.

recycling) and to overlook high-impact behaviours (e.g. flying). While there is evidence of

‘clustering’ of pro-environmental behaviours within particular domains (e.g. waste avoidance,

energy use, transport, political activism), there is less evidence of consistency across those domains

and little consensus on the reasons for this lack of consistency [80,89-91].

One indirect way of assessing the potential importance of spillovers is to identify the correlation

between pro-environmental behaviours and the overall environmental impacts of household

consumption, whilst controlling for socio-economic variables that are expected to be correlated with

those impacts. Low correlation may suggest the presence of negative spillovers, although other

factors (including rebound effects) could contribute to this outcome. This type of study is

challenging, as it requires comprehensive and accurate measures of both the environmental

behaviours of individual households and their consumption patterns.

Most studies find that the biggest predictor of overall environmental impacts is household income

[92-96].9 For example, the expenditure elasticity of emissions have been estimated at 0.57 in the US,

0.64 in Japan, 0.78 in Australia, 0.84 in Sweden 0.91 in Spain and 1.0 in Brazil [96,98,99]. As an

9 While larger households have larger impacts, the per capita impacts of household members are lower due to sharing of living space and utilities [97].

21

illustration, Figure 2 shows that wealthy households in the UK have disproportionately high GHG

emissions, as a consequence of regular flying, greater car use, larger dwellings and overall greater

consumption. Some studies have also found geographical location to be a significant predictor of

environmental impacts, with urban households having slightly lower footprints (less car travel and

smaller dwellings) [97,100,101]. However, inconsistent results have been found for age, gender,

education and employment [102-105].

Figure 3 Estimates of GHG emissions for different income groups in the UK

0

5000

10000

15000

20000

25000

30000

Quintile 1

Quintile 2

Quintile 3

Quintile 4

Quintile 5 All

Annu

al e

quiv

alis

ed G

HG e

mis

sion

s (k

gCO

2e)

Investment

Other transport

Vehicle fuels emission

Other Housing

Other fuels

Gas

Electricity

Miscellaneous goods & services

Restaurants & hotels

Education

Recreation & culture

Communication

Health

Furnishings

Clothing & footwear

Alcoholic beverages, tobacco, narcotics

Food & non-alcoholic beverages

Source: Chitnis et al Figure 4 [7]

Table 4 summarises the results of 11 studies that use surveys of individual households and combine

measures of socio-demographics, environmental values and/or PEBs, and overall environmental

impacts. The studies all test the impact of values/behaviours on environmental impacts whilst

controlling for other variables, but vary widely in how those variables are measured. We summarise

the key findings of these studies in Box 3.

Eight of the studies test whether environmental values predict environmental impacts and five find a

significant relationship. However, three of these studies find only a weak relationship (e.g.

Gatersleben et al [87] estimate a coefficient of -0.07 for environmental beliefs (5-point scale),

compared to +0.44 for income (7-point scale)) while one [106] finds a negative relationship (i.e.

households with stronger environmental values have greater environmental impacts). Similarly,

nine of the studies test whether PEBs predict environmental impacts, but only three find a significant

relationship – and in each case the relationship is weak. For example, Balmford et al [107] find that

22

conservationists have only a slightly lower environmental footprints than economists and medics,

despite having much stronger environmental values.

Comparison of these studies is hindered by their use of different measures of environmental

concern, PEBs and/or environmental impacts. Even studies that use the same metric for

environmental impacts (e.g. carbon footprint) estimate those impacts in different ways, and none

provides a complete measure of impacts since they overlook key categories of consumption - such as

durable goods. Nevertheless, the results strongly suggest that environmental values and self-

reported PEBs have a very limited influence on aggregate energy use and emissions. This could be

for a number of reasons, including:

H1. Self-report bias: The survey respondents exaggerate their adoption of PEBs

H2. Poor targeting: The respondents prioritise PEBs with limited impact on energy use and emissions

and neglect those with greater impact

H3. Rebound effects: The respondents re-spend the cost savings from their PEBs on other goods and

services, thereby offsetting some or all of the environmental benefits of those behaviours

H4. Negative spillovers: The respondents consider that their PEBs provide them with a ‘moral

licence’ to engage in other, more environmental damaging behaviours.

These hypotheses are not mutually exclusive, and their validity and relative importance remains to

be established. There is good evidence for self-report bias and poor targeting [84,88,106], with the

latter being explained by limited information on the relative impact of different PEBs and the greater

cost and difficulty of high-impact PEBs (e.g reducing car use). Nevertheless, it seems likely that both

rebound effects and negative spillovers play an important role in explaining these findings.

This evidence therefore raises important questions about the effectiveness of sufficiency actions as

currently practised. While some households may have significantly reduced their overall

environmental impacts, they appear to be the exception rather than the rule. Most households

prioritise actions with limited environmental benefits, and a combination of rebound effects and

negative spillovers partly or wholly offset those benefits. Moreover, since aggregate energy use and

emissions is strongly correlated with income, the modest impact of PEBs can be easily outweighed

by small increases in income. This suggests that a more effective approach to energy sufficiency

could be to voluntarily reduce income and aggregate consumption - commonly known as

downshifting [108-110].

23

Table 4 Empirical evidence for the influence of environmental values and/or pro-

environmental behaviours on overall environmental impacts

Study Region Sample

size

Measure of

environmental

impacts

Environmental

values/concern

predict

environmental

impacts?

Pro-

environmental

behaviours

predict

environmental

impacts?

Gatersleben

et al [87]

Netherlands a) 2167

b) 1250

Direct and

indirect energy

use

Yes

(weak

relationship)

Yes

(weak

relationship)

Poortinga et

al [111]

Netherlands 455 Direct and

indirect energy

use

No No

Vringer et al

[112]

Netherlands 2304 Direct and

indirect energy

use

No No

Kennedy et

al. [113]

Alberta,

Canada

1203 Direct carbon

emissions

Yes

(weak

relationship)

No

Csutora [114] Hungary 1012 Direct and

indirect carbon

emissions

Not tested No

Tabi [115] Hungary 1012 Direct carbon

emissions

Not tested No

Nassen et al

[116]

Sweden 1003 Direct and

indirect GHG

emissions

Not tested Yes

(weak

relationship)

Bleys et al

[117]

Flanders 1286 Ecolife

environmental

footprint

calculator

Yes

(weak

relationship)

Not tested

Balmford et Global 734 Direct and No Yes

24

al [107] indirect carbon

emissions

(weak

relationship)

Moser and

Kleinhückelk

otten [106]

Germany 1012 Energy use and

GHG emissions

Yes

(negative

relationship)

Not tested

Enzler and

Diekmann

[99]

Switzerland 2789 Direct and

indirect GHG

emissions

Yes No

Note: ‘Predict’ means that the coefficient on environmental values/PEBs is significant at the 5% level in a regression of the

measure of environmental impacts on a range of variables. ‘Weak relationship’ indicates that environmental values or PEBs

explain only a small fraction (typically <10%) of the variance in the measure of environmental impacts. The diversity of

measures for environment values, PEBs and environmental impacts make it difficult to summarise these measures

quantitatively.

25

Box 3 Summary of studies estimating the influence of environmental values or pro-environmental behaviours on overall environmental impacts

Gatersleben et al [87] conduct two surveys of Dutch households and construct partial estimates of direct and indirect energy use from reported travel patterns, dwelling characteristics and the ownership and use of household goods. They find environmental awareness and PEBs to be significant predictors of energy use in the first survey, and environmental beliefs to be a significant predictor in the second, but in both cases these have only marginal impacts relative to income and other variables. The authors argue that more attention should be paid to environmentally significant behaviours.

Poortinga et al [111] also survey Dutch households and construct a similar measure of direct and indirect energy use, as well as measuring attitudes towards a range of PEBs. They find that concern about the environment and climate change predict attitudes towards PEBs, but does not predict energy consumption itself. Again the latter is primarily explained by income.

In a third Dutch study, Vringer et al [112] construct a more accurate measure of direct and indirect energy use by combining survey responses on six categories of consumption (natural gas, electricity, transport, holidays, dwelling and food) with information from the Dutch household expenditure survey. They find that neither values or concern about climate change are significant predictors of energy consumption, and the motivation to save energy is only a very weak predictor (with a 4% difference in energy consumption between the average and most motivated households). Again, income is overwhelmingly the most important determinant of overall energy use.

Huddart Kennedy et al. [113] measure environmental concern and relatively low-impact PEBs within a sample of Canadian households, and estimate direct carbon emissions from survey responses on energy use, travel behaviour and waste disposal. They find only a weak negative relationship between environmental concern and carbon emissions and no significant relationship between PEBs and carbon emissions. This leads them to criticise environmental psychologists for ‘not measuring what really counts’.

Csutora [114] estimates both direct and indirect carbon emissions for a representative sample of Hungarian households, using a mix of reported consumption patterns in some areas (e.g. distance travelled) and reported expenditures in others. She classifies respondents as ‘green’, ‘average’ or ‘brown’ according to their take-up of eight types of PEB and finds no significant difference between the carbon footprint of green and brown consumers. While green consumers have lower electricity consumption after controlling for income, they use more electricity overall since both PEBs and electricity consumption are correlated with income.

Using the same dataset, Tabi [115] employs latent cluster analysis to distribute the sample into four groups (‘beginners’, ‘browns’, ‘energy savers’ and ‘supergreens’) according to their self-reported PEBs. He finds no significant difference in electricity or heating emissions between these groups, although supergreens report slightly lower transport emissions. Supergreen and brown households have comparable emissions, in part because the greater average wealth of the former outweighs the influence of their PEBs.

Nassen et al [116] assemble fairly accurate estimates of the GHG footprint of a sample of Swedish households, and find that environmental concern is a significant but weak predictor impact of those footprints. Moving from low to high environmental concern (-1 to +1 standard deviations) is associated with a 5% difference in impacts, compared to a 44% difference when moving from low to high income.

Bleys et al [117] measure environmental concern, self-assessed environmental sustainability (SAES) and environmental footprints for a sample of Belgium households. They find that environmental concern predicts footprints, but income and other variables play a much more important role. Environmental concern also predicts SAES, but actual environmental footprint does not – indicating a mismatch between perceived and

26

actual impacts. The results suggest that respondents overestimate the environmental benefits of some behaviours (e.g. food choices, paper use) and pay insufficient attention to the impact of others (e.g. heating, car use, holidays).

Taking a different approach, Balmford et al [107] survey professional conservationists, economists and medics and estimate their direct and indirect carbon emissions from responses to specific questions (e.g. commuting, pet ownership, flights). They find that environmental impacts are largely explained by sociodemographic variables, that environmental knowledge does not predict emissions, that PEBs in one domain are only weakly correlated with PEBs in other domains, and that conservationists have only a slightly lower footprint than the other groups, despite having much stronger environmental values.

Moser and Kleinhückelkotten [106] estimate energy use and GHG emissions for a sample of German households from responses to questions on car travel, ownership of energy efficient appliances, living space and other variables. Again, they find income to be the biggest predictor of GHG emissions. Environmental identity predicts low-impact PEBs and meat consumption, but not car use or flying. Also, households with a stronger environmental identity have larger emissions, after controlling for other variables.

Finally, Enzler and Diekmann [99] use data from the 2007 Swiss Environmental Survey and estimate overall GHG emissions from responses to questions on mobility, housing, food and non-durable consumption. They find income to be the strongest predictor of emissions, but environmental concern is also significant - with a one-unit (20%) increase in concern being associated with a 5-10% reduction in emissions. However, they find little correlation between reported PEBs and overall impact - suggesting that traditional measures of PEBs are a poor indicator of those impacts.

5 Time-use rebounds and downshifting

5.1 Determinants of the environmental benefits of downshifting

Most people taking sufficiency actions will continue to work and to earn as much as before – and

simply spend their time and money in a different way. But an alternative approach is to voluntarily

reduce working hours, income and aggregate consumption - and hence also energy service

consumption. While the primary motivation for downshifting is to reduce time pressures and

improve quality of life, it may also reduce the environmental impacts of consumption [110,118,119].

For example, Druckman et al [120] estimate that if everyone in the UK were to downshift to the

Minimum Income Standard [121], then average household GHG emissions would fall by 37%.10

The term downshifting’ was introduced by Amy Saltzman in 1991 [108] and is advocated as means to

escape the ‘work and spend’ cycle of contemporary life [122]. In the 1980s, the century-long trend

towards lower working hours began to flatten or reverse in many OECD countries, as hourly wages

failed to keep up with productivity improvements [123]. Longer work hours - exacerbated by long

commutes - leaves people with less time to spend with children or to engage in leisure and

community activities, and is widely seen as contributing to rising stress, poor health and reduced

10 The Minimum Income Standard includes “…what you need in order to have the opportunities and choices necessary to participate in society” [120]

27

quality-of-life [124,125]. However, the evidence on the relationship between working hours and

quality-of-life is ambiguous, with a range of other factors (e.g. gender, profession, income, job

security, shift patterns, flexibility, information technologies) playing a role [126]. More generally,

there is little evidence that environmental motivations play an important role in decisions to

downshift [110].

To downshift, you must begin with a level of income that it is feasible to downshift from without

jeopardising wellbeing. Hence, downshifting is primarily an option for the relatively wealthy. But

with increasing income inequality, rising housing costs, growing debt burdens and falling real wages,

the proportion of people with both the motivation and ability to downshift may be declining [127-

130]. A key problem is that many people are economically or psychologically “locked-in” to current

consumption patterns and find it difficult to change [131]. Relevant constraints include: unavoidable

financial commitments (e.g. housing costs, non-income related tax obligations, child support); land-

use patterns and physical infrastructures that limit choices in key areas (e.g. travel, heating); the

rapid obsolescence of consumer goods, (creating a need for regular expenditure on replacement);

the search for status through the acquisition of symbolic ‘positional goods and services’ (e.g.

designer clothing, expensive cars; and the rapid adaptation of aspirations to higher income levels

[4,132-134]. Downshifting, in other words, can be difficult and may only be an option for a few.

While downshifting households reduce their working hours, income and hence total expenditures (a

scale effect), they may not achieve a proportionate reduction in energy use and emissions owing to

associated changes in the pattern of expenditure (a compositional effect). This is because, with more

time and less money, households may allocate their financial and temporal resources in a different

way. If widely adopted, downshifting may also induce changes in labour, energy and other markets

that have additional impacts on economy-wide energy consumption.

An indication of the energy savings from downshifting can be obtained from estimates of the

expenditure elasticity of total (direct plus indirect) energy consumption ( ). Since lower

income households tend to spend a larger proportion of their budget on energy-intensive

‘necessities’ such as food and heating, this elasticity is typically found to be less than unity [96,98,99].

This implies that the percentage reduction in energy consumption from downshifting will be less

than the percentage reduction in income.

Expenditure elasticities are typically estimated from household expenditure surveys, where the

differences in income between households derive more from differences in hourly wages than from

differences in working time. However, downshifting does not just reduce income – it also frees up

28

time for other activities. Hence, the changes in expenditure patterns (and hence energy use) from

downshifting may differ from those suggested by comparing the expenditure patterns of different

income groups. For example, downshifting households may choose to travel by public transport

rather than by taxi, to cook at home rather than buy ready-meals, or to spend time walking and

gardening rather than commuting. If there is a negative correlation between time-intensive and

energy-intensive activities, the reduction in energy consumption from downshifting could be

proportionately larger than the reduction in income. Conversely, if downshifting households have

more time to pursue energy-intensive activities such as long-distance vacations, the reduction in

energy consumption could be proportionately smaller than the reduction in income. These different

outcomes may be viewed as negative and positive time-use rebounds - although it is difficult to

separate these empirically from the effect of changes in income.

The environmental impact of downshifting is likely to depend upon how the reduction in work time

is achieved (e.g. shorter working days, three-day weekends or longer holidays [135]) and upon the

post-downshift level of income. For example, if someone downshifts by taking longer holidays and

still remains on a relatively high income, they may be able to take more vacations abroad. The

outcome may also depend upon the motivation for downshifting. For example, it is possible that

environmentally concerned households will be more likely to downshift, and those households may

be motivated to take additional sufficiency actions. Hanbury et al [119] explore such motivations

through interviews with 17 downshifting households and find that those who use their newly gained

time for activities such as parenting and further education can reduce their environmental impacts,

while those that use the time for additional leisure activities risk increasing their environmental

impacts. In a longitudinal study of UK households, Melo et al [136] find no evidence that households

with more free time or with less concern about their worklife balance adopt more sufficiency

actions. However, there appears to be no study of the environmental values of households who have

chosen to downshift.

A further complication is that reductions in working time may not necessarily be associated with

reductions in income at either the individual or the aggregate level. To understand this, it is useful to

decompose the per capita GDP of a country (€/person) as the product of the employment rate, the

average annual work-time per worker (hours) and the average labour productivity (€/hour), as

follows [137,138]:

GDPPopulation

= WorkersPopulation

∗Work time

Workers∗ GDP

Work time1

29

If employment and productivity are unaffected by changes in average work-time, the widespread

adoption of downshifting would reduce per capita GDP and hence income and expenditure.

However, reductions in average work-time could be associated with, or enabled by, increases in

labour productivity, which in turn could enable higher wages. Similarly, downshifting through job

sharing could increase the employment rate, thereby offsetting the reduction in average working

hours among the working population. Adjustments such as these would offset the reduction in per

capita GDP and aggregate consumption, and thereby offset some of the environmental benefits of

downshifting.

Downshifting may also have other macroeconomic effects, such as reducing energy prices and

encouraging increased energy use by other consumers [44]. If large-scale downshifting does reduce

economic activity, it could lead to more bankruptcies, higher unemployment, lower tax revenues

and reduced public expenditure. Options are available to mitigate these impacts, but they require

collective rather than individual action.

5.2 Estimates of the environmental impacts of downshifting

The preceding discussion suggests that the environmental impacts of downshifting may best be

explored by estimating the working-time elasticity of those impacts ( ). If this elasticity is

greater than one, the percentage reduction in environmental impacts should be greater than the

percentage reduction in working time - and if this elasticity is negative, downshifting may create

additional environmental impacts.

A small number of studies have estimated this elasticity using either cross-country or household

survey data that includes measures of working time (Table 5). Most of these studies distinguish

between the scale effect and compositional effect of changes in working time, where the latter

controls for changes in income. We summarise the results of these studies in Table 5 and in Box 4.

Seven of these nine studies use aggregate data on GDP, working time and environmental impacts for

a selection of countries, while two (Fremstad et al [139] and Nassen and Larsen [140]) use data for

individual households in a single country. Each study estimates the scale effect and/or the

compositional effect as an elasticity, indicating the percentage change in impacts following a

percentage change in working time. However, the studies use a variety of methodological

approaches and measures of environmental impacts, and their elasticity estimates range from +1.33

to -3.46.

It is clear from Table 5 that the literature has yet to reach a consensus on the relationship between

working time and energy use/emissions. The estimates of scale and composition effects appear

30

sensitive to the region and time period studied, the measure of impacts chosen and the

methodology employed. While earlier studies using cross-sectional data (e.g. [141]) suggest that a

reduction in working hours is associated with a more than proportional reduction in environmental

impacts, this is not supported by the results of later studies using panel data (e.g. [142]). Indeed, the

two studies by Shao and colleagues [143,144] suggest that, once per-capita GDP exceeds a certain

threshold (or working time falls below a certain threshold), reductions in working time are

associated with increased emissions - perhaps because the additional time is used for energy-

intensive leisure activities. However, none of the aggregate studies control for fuel mix or the

stringency of national climate policies and if these are correlated with average working time the

elasticity estimates may be biased. Also, the aggregate studies include countries with diverging

trends in working hours, and it seems likely that the scale effect is asymmetric (i.e. the change in

energy use following a reduction in working hours may not be equal and opposite to that following

an increase in working hours). Potentially more robust estimates could be obtained from data on

individual households, but the only study to adopt this approach to date is Fremstad et al [139] who

estimate that the percentage reduction in emissions for downshifting households in the US is less

than one third of the percentage reduction in working time.

The emission reductions achieved by an individual downshifting household will depend upon its

specific circumstances, including the manner in which work-time reductions are achieved (e.g.

shorter days or longer weekends), the initial and final level of income, the initial number of working

hours and the broader national context. These reductions may differ systematically from those

estimated in Table 5, since the latter derive from aggregate data in which variations in working hours

derive from sources other than downshifting. Further research in this area should therefore use

data on individual households, as well as studying households who have actively chosen to

downshift.

Overall, the evidence suggests that downshifting should be more effective than targeted sufficiency

actions in reducing energy use and emissions – although it also requires a bigger commitment and

entails a bigger change in lifestyles. Given the strong correlation between environmental impacts

and total expenditure, it is unsurprising that reduced expenditure is associated with lower

environmental impacts. However, the reduction in environmental impacts is likely to be

proportionately less than the reduction in working hours and income, and this reduction is not

guaranteed. For wealthy households (who are the ones likely to downshift), there is a risk that

downshifting allows more time to be spent on energy-intensive leisure activities that offset the

31

benefits of reduced expenditure – or in other words, the environmental benefits of downshifting are

vulnerable to time-use rebound effects.

32

Table 5: Empirical estimates of the environmental impacts of work-time reductions

Study Measure of

environmental

impacts

Data Estimate of scale

effect

Estimate of

compositional

effect

Rosnick and

Weisbrot [141]

Primary energy

consumption

48 countries

(24 developed)

2003

1.33 Not estimated

Hayden and

Shandra [137]

Ecological

footprint

45 countries

(19 developed)

2000

1.20 0.59

Knight et al [145] Carbon

emissions and

carbon

footprints

29 OECD

countries

1970-2007

Emissions: 0.5

Footprint: 1.30

Not significant

Fitzgerald et al

[142]

Primary energy

consumption

52 countries

(29 developed)

1990-2008

0.32

(-0.26 in 1992 to

+0.49 in 2008)

Not significant

Fitzgerald et al.

[146]

Carbon

emissions

50 US states 0.67 0.68

Shao and

Rodriguez-

Labajos [144]

Carbon

emissions

55 countries

(37 developed)

1980-2010

Pre-2000: +0.194

Post-2000: -0.157

Pre-2000: -0.693

Post-2000: -0.149

Shao and Shen

[143]

Energy use and

carbon

emissions

EU 15

1990-2010

Not estimated Energy:

Med GDP= 3.49

High GDP = -0.05

Carbon:

Med GDP= 0.89

High GDP = -3.46

Fremstad et al

[139]

Direct and

indirect

household GHG

emissions

US houshold

expenditure

survey (n=3200)

2012-2014

0.27

Nassen and Direct and Swedish Energy: 0.74 Energy: -0.02

33

Larsen [140] indirect

household

energy use and

GHG emissions

household

expenditure

(n=1492) and

time-use (n=636)

surveys 2006

GHGs: 0.80 GHGs: -0.02

Box 4 Summary of studies estimating the work-time elasticity of environmental impacts

Rosnick and Weisbrot [141] estimate equations for per capita primary energy consumption in 48 countries in 2003 and find a scale effect of 1.33. While this suggests that reductions in working time achieve a more than proportionate decrease in energy consumption, their estimate is not significantly different from unity. Hayden and Shandra [137] conduct a similar study for 45 countries in 2000, using ecological footprint as their dependent variable. They estimate a scale effect of 1.20 and a compositional effect of 0.59 - suggesting that changes in the pattern of consumption by downshifting households are environmentally beneficial. Since both studies use cross-sectional data, their results may not provide a good guide to work-time reductions in later years.

Knight et al [145] estimate equations for both national carbon emissions and carbon footprints, using data from 29 high-income countries over the period 1970-2007. They estimate a scale effect of 0.5 for emissions and 1.3 for footprints, suggesting that a reduction in working hours would have a less than proportionate impact on the former and a more than proportionate impact on the latter. These impacts primarily result from changes in aggregate consumption, since they find the compositional effect to be insignificant.

Fitzgerald et al. [142] estimate equations for primary energy consumption for 52 countries over the period 1990-2008. They estimate a scale effect of 0.32 for the full dataset and 0.40 for developed countries alone, and also find the compositional effect to be insignificant. They estimate that the scale effect has increased from -0.26 in 1990 to +0.49 in 2008 – suggesting that the environmental benefits of downshifting have increased over time as countries have become richer. Since Fitzgerald et al. include country and year fixed effects, their result may be less vulnerable to omitted variable bias than Knight et al.

Fitzgerald et al.[146] use a similar approach to analyse carbon emissions in US states between 2007 and 2013. They estimate a scale effect of 0.67, which is consistent with their earlier finding of larger effects in wealthier countries. They find the compositional effect to be of similar size, but this result is questionable since the coefficient on per capita GDP is insignificant.

Shao and Rodriguez-Labajos [144] use a dynamic panel approach to investigate trends in 55 countries over 30 years. They find no significant relationship between working time and per capita carbon emissions in developing countries, while for developed countries they find a positive scale effect before 2000 (+0.194) and negative scale effect (-0.157) and negative compositional effect (-0.149) after that date. This implies that reductions in working time in the 21st-century were associated with increased emissions - contradicting the results of earlier studies. One possible explanation for this result is that, beyond a certain level of income, people use their additional leisure time for energy-intensive activities.

Building upon this study, Shao and Shen [143] employ a ‘threshold auto regression’ technique to test for turning points in the relationship between working hours and per capita carbon emissions in the EU-15 over the period 1970-2010. Their results support the finding of Shao and Rodriguez-Labajos [144] in that reductions in working hours are associated with increased emissions in wealthier countries. Their results also suggest that there are no environmental benefits from downshifting in countries where working time is already short.

34

In the only study of this type using micro-data, Fremstad et al [139] estimate equations for GHG emissions from a sample of 3200 US households over the period 2012-2014. Employing a variety of specifications, they estimate a work-time elasticity of GHG emissions of 0.27 and a wage elasticity of 0.21 - suggesting that downshifting encourages a less GHG-intensive pattern of consumption. Their results also suggest that these benefits are larger for households with longer working hours.

Finally, Nassen and Larsen [140] combine data from expenditure and time-use surveys of Swedish households. Their results suggest a scale effect of 0.7 for energy use and 0.8 for GHG emissions, and a much smaller compositional effect of -0.02. This suggests that changes in the pattern of expenditure contribute a small increase in emissions that is outweighed by the negative income effect.

6 Summary and conclusions

Energy sufficiency is being increasingly advocated as a strategy to address climate change, but

insufficient attention has been paid to the effectiveness of this strategy. This paper has shown how

rebound effects and negative spillovers can erode the environmental benefits of energy sufficiency

actions, and how changes in the pattern of consumption - including time-use rebounds - can do the

same for downshifting. The environmental benefits from energy sufficiency actions may be less than

many advocates expect, and in some circumstances (e.g. shifting to a vegetarian diet) such actions

may even increase energy use/emissions.

The magnitude of rebound effects and negative spillovers will depend upon the type of sufficiency

action and the motivations, circumstances and choices of individual households. While people can

significantly reduce their environmental footprint through voluntary actions, this is only possible if

they act consistently in multiple domains and prioritise actions with large environmental benefits -

such as reducing car use and aviation. In practice, however there is a tendency to behave

inconsistently between different domains and to prioritise relatively low-impact actions, such as

recycling.

Rebound effects appear to be modest for actions affecting heating and electricity, larger for actions

affecting transport fuels and very large for actions affecting food consumption. However, the size of

these effects varies with the metric used, the emission intensity of electricity generation and the

particular pattern of re-spending. As energy systems decarbonise the climate benefits from such

actions will fall, and if the cost savings are re-spent upon imported goods from countries with a

higher carbon intensity, the size of rebound effects will increase.

Negative spillovers appear more likely to occur when people have weak environmental values, when

the initial action involves little financial or other cost and when people feel less need for consistency

in their behaviour. Policies that provide financial incentives for sufficiency actions are likely to

35

amplify the rebound effect from those actions and to also encourage negative spillovers. While few

studies quantify the environmental impact of negative spillovers, there appears to be little

correlation between self-reported sufficiency actions and overall environmental impacts. Hence,

sufficiency actions as currently practised appear to have only a limited influence on total energy

use/emissions and their effectiveness can be outweighed by small increases in income. While

negative spillovers provides one explanation of this observation, the importance of these relative to

rebound effects and the poor targeting of sufficiency actions remains to be established.

In contrast to individual sufficiency actions, voluntary reductions in working time, income and

aggregate consumption should reduce energy use and emissions. But downshifting may only be an

option for a subset of wealthy households and these are unlikely to achieve a proportionate

reduction in energy use/emissions unless they adopt additional sufficiency actions. The evidence on

the environmental impact of work-time reductions is patchy and inconsistent, but it is possible for

downshifting households to increase their environmental impacts if they use their additional free

time for energy-intensive leisure activities - a time-use rebound effect. There is a need for greater

research on this topic, preferably using household-level data and exploring the experience of

households that have actively chosen to downshift.

The evidence on rebound effects and negative spillovers from sufficiency actions is growing, but the

economic and psychological literatures on these topics remain largely separate, with different aims,

theoretical frameworks and methodological approaches. The economic literature has paid

insufficient attention to the psychological drivers of rebound effects, while the psychological

literature has paid insufficient attention to the environmental impacts of different actions. Hence,

there is much to be gained from bringing these two communities together and from conducting

experimental and survey-based studies that give appropriate weight to both individual motivations

and aggregate impacts. There is also need for more standardised measures of environmental

impacts to increase the comparability of different studies, and for modelling studies that explore the

economy-wide consequences of sufficiency actions.

AcknowledgementsThe authors would also like to acknowledge the support of: the KR Foundation and the European Council for Energy Efficient Economy ; UK Research and Innovation (UKRI) through a grant to the Centre for Research on Energy Demand Solutions (grant number EP/R035288/1); and the UK Economic and Social Research Council through a grant to the Centre for the Understanding of Sustainable Prosperity ( grant number ES/M010163/1). We would also like to thank two anonymous reviewers for their helpful comments.

36

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