The Direct and Indirect Rebound Effects for Residential Heating in Switzerland

University of Neuchatel
Institute of Economic Research
IRENE, Working paper 16-11
The Direct and Indirect Rebound Effects for
Residential Heating in Switzerland
Cécile Hediger* Mehdi Farsi** Sylvain Weber***
* University of Neuchâtel, Institute of Economic Research, Rue Abram-Louis
Breguet 2, 2000 Neuchtel, Switzerland. Contact: cecile.hediger@unine.ch.
* University of Neuchâtel, Institute of Economic Research, Rue Abram-Louis
Breguet 2, 2000 Neuchtel, Switzerland. Contact: mehdi.farsi@unine.ch
* University of Neuchâtel, Institute of Economic Research, Rue Abram-Louis
Breguet 2, 2000 Neuchtel, Switzerland. Contact: sylvain.weber@unine.ch.

The Direct and Indirect Rebound Effects for
Residential Heating in Switzerland ∗
Working paper
Cécile Hediger, Mehdi Farsi, Sylvain Weber †
This version: October 7, 2016
Abstract
Improvements in energy efficiency are seen as a key tool to decrease
energy consumption, yet less is known about the reaction of household
to such improvements. They could adapt (increase) their demand because
the relative price of the energy service has diminished thanks
to the efficiency gains. Focusing on the residential heating sector, we
study how the households react to a gain in efficiency of their heating
system. An increased demand for the heating service is the direct rebound
effect, while the indirect rebound effect is the income effect on
the consumption of all other goods. Both effects take back some of the
energy savings initially expected. To estimate these rebound effects,
we use a specific survey containing innovative choice experiments. We
find that 10 to 15% of the expected energy savings are not realised in
the space heating sector due to the direct rebound effect. Including
the indirect rebound effects, it is one third of the potential savings
which are lost. We also highlight the heterogeneity among people:
some do not rebound at all, while others display a large rebound. We
analyse this heterogeneity using socio-economic characteristics, finding
that people with a low income, less educated, less environmentally
friendly, and less satisfied of their heating comfort have the largest
direct rebound effect.
JEL Classification: D12, Q41, Q47, R22.
Keywords: Rebound Effects, Energy Efficiency, Residential Heating
∗ This research was financially supported by the Swiss National Science Foundation
(SNSF), grant No 100018-144310.
† University of Neuchâtel. Corresponding author: cecile.hediger@unine.ch.
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1 Introduction
The easiest way to save energy is not to waste it. Improving energy efficiency
is thus considered a key tool to reduce energy consumption while
keeping the same level of comfort. Energy efficiency is indeed seen as the
“Fifth Fuel” or “Invisible Fuel” (The Economist, 2015) of the energy transition.
In Switzerland, like in many other industrialized countries, room for
improvements in energy efficiency is still considerable, especially in the area
of buildings and heating systems. According to the Swiss Federal Office of
Energy, 30% of Switzerland’s primary energy consumption is attributable to
heating, air-conditioning and hot water in the buildings, with much of the
energy being wasted. Promoting energy efficiency is the first pillar of the
energy strategy 2050 aiming to phase out nuclear power, coming before the
promotion of renewable. In terms of efficiency, four sectors are targeted, with
buildings in the first position, and then mobility, industry, and appliances.
Yet, setting ambitious efficiency standards might not be sufficient to achieve
the new targets of energy consumption, because people will react and adjust
to such policies. These reactions must be acknowledged in order to design
effective policies. Following efficiency improvements, consumers could react
by consuming more goods and services, with the risk of offsetting completely
the initial gains in energy consumption. For example, suppose the heating
system of your house has just been renovated and is now more efficient. It
means that it is now cheaper to heat your house. Because of that, you could
decide to modify your heating habits, for instance set the temperature by 1
or 2 degrees higher, or start heating earlier in the season, air more often,
etc. This mechanism is known as the “rebound effect”, or more precisely the
direct rebound effect. In the literature, the direct rebound effect is commonly
defined as “an increased consumption of energy services [e.g., heating] following
an improvement in the technical efficiency of delivering those services”
(Sorrell and Dimitropoulos, 2008).
An indirect rebound effect might also arise because products and/or services
will be purchased with the savings made on heating. The use of the term
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indirect rebound is inconsistent in the literature, but it most commonly describes
the income effect on the consumption of all other goods (Chan and
Gillingham, 2015). As any product and service requires energy to be created
and used, overall energy consumption will decrease by less than the amount
predicted after the renovation of an heating system, even in the absence
of any direct rebound effect. In the worst case, if the products or services
purchased with the savings made on heating are very energy intensive (for instance
travelling by plane), overall energy consumption could even increase.
The latter situation is known as backfire.
In this paper, we investigate the direct and indirect rebound effects for residential
heating in Switzerland. As efficiency is seen as a key tool to decrease
energy consumption and in turn CO 2 emissions, a reliable estimation of
these rebound effects is highly valuable for policy makers in charge of the
energy transition. Besides, we also analyse individual heterogeneity and determine
who rebounds the most. In the literature, hypotheses suggest that
people less satisfied with their heating comfort will rebound more. Yet, such
hypotheses are never tested due to the lack of data, except for the impact
of income (Chitnis et al., 2014) and ownership (Madlener and Hauertmann,
2011).
Our data come from a survey that we created for this purpose. The 3,500
respondents answered a choice experiment in which improvements in the efficiency
of their heating system were simulated. To our knowledge, this kind of
experiment has never been conducted to estimate both direct and indirect rebound
effects. Furthermore, measuring rebound effects for residential heating
has received few attention, with only a handful studies in Europe (Haas and
Biermayr, 2000; Madlener and Hauertmann, 2011; Druckman et al., 2011;
Chitnis et al., 2013). However, results show that the direct rebound effect
in this domain is not negligible, with a “best guess” (Sorrell et al., 2009)
between 10 and 30% .
Our results indicate an average direct rebound effect between 10 and 15%.
Adding the indirect rebound effect, we find an average total household rebound
effect of 36%. It means that policy makers should not overestimate
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the power of efficiency to diminish energy consumption in the residential
heating sector, because one third of the benefits of efficiency is lost through
direct and indirect rebound effects. We also investigate who rebounds the
most, and obtain among other that less educated people and people with a
lower income have a larger direct rebound effect.
The remainder of this article is structured as follows. In section 2, we provide
an overview of how the rebound effect has been defined and measured in the
literature, section 3 presents the data we use, and section 4 describes the
models we estimate in section 5. Conclusions and policy implications are
discussed in section 6.
2 Rebound Effects in Residential Heating
Sorrell and Dimitropoulos (2008) provide an extensive discussion of the definition
of the rebound effects (RE). The direct RE is basically defined as an
increase in the consumption of an energy service following a decrease in the
effective price of that service due to an improvement in the energy efficiency
of delivering that service. Energy efficiency may be defined as ε = S/E, where
E represents energy input and S service demand. In our study, S represents
the services provided by heating. It is not only an internal temperature, but
heating comfort in general. The rationale is that heating demand may increase
because heating is turned on earlier in the season, or because of more
frequent airing, and not only because internal temperature is increased.
Using elasticities, the direct RE is defined as the elasticity of the service
demand (S) with respect to efficiency (ε):
η ε (S) = ∂S
∂ε · ε
S
(1)
Because of the lack of data, this definition is seldom used in empirical studies.
Authors usually rely on alternative definitions such as the elasticity of the
service demand with respect to energy price. In the case of heating, an
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estimation of the price elasticity for heating fuel is often used in the literature
to approximate the direct RE (Madlener and Hauertmann, 2011; Haas and
Biermayr, 2000). Yet, strong assumptions are needed to do so: people have
to react symmetrically to a change in price and to a change in efficiency,
an hypothesis rejected by Greene (2012) in the context of private mobility.
Chan and Gillingham (2015) criticize strongly this approach, demonstrating
that it leads to biased estimations of rebound effects.
In the present paper, we do not need to make such restrictive assumptions.
We rely on a second definition of the RE, which is given by the proportion
of the energy savings which are not realised (as Chitnis et al., 2013, do), in
other words:
Direct RE = 1 −
Actual energy savings
Potential energy savings
(2)
Note that definitions (1) and (2) are equivalent since Actual energy savings =
1 − η ε (S).
There is no consensus in the literature about the magnitude of the direct and
indirect rebound effects. For residential space heating, Sorrell et al. (2009)
review the literature and collect estimates of the direct RE ranging between
10 and 58% in the short run and between 1.4 and 60% in the long run. They
suggest a mean value of 20% for the direct rebound effect for household
heating. Nadel (2012), also in a review of the literature, advocates a likely
range would be between 1 and 12%. He questions studies claiming higher
direct RE, because they are mostly based on price elasticity. In a recent
article (Nadel, 2016), he summarizes the findings of studies looking at both
direct and indirect RE. For residential space heating, he finds that direct RE
is generally about 10%, and indirect RE in the range of 10-20%, leading to
a total rebound of 20 to 30%.
We are not aware of any study that focuses on rebound effects in space
heating in Switzerland. In their study about the effects of global warming on
energy use in Switzerland, Gonseth et al. (2015) find a direct RE of 35% in
the long-run (up to 2060) using a CGE model. Madlener and Hauertmann
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(2011) and Haas and Biermayr (2000) investigate a similar topic for two
neighbouring countries, namely Germany and Austria. For Germany, a direct
RE between 12 to 49% is found, with tenants having a higher rebound than
owners, and between 20 to 32% for Austria. They both use price-elasticity.
Some studies rely on engineering calculations, comparing predicted energy
use with observed energy use. For instance Aydin et al. (2014) study 500,000
households in the Netherlands, comparing the energy label of dwellings with
their energy consumption. They find a direct RE of 28% for owners and
42% for tenants. This technique relying on engineering predictions has also
been criticised, mostly because these predictions over-estimate the potential
energy savings of the efficiency improvements. Fowlie et al. (2015) study
30,000 households participating in an energy efficiency program in the USA.
They find that the projected savings by engineers are roughly 2.5 times the
actual savings. A part of this discrepancy may be due to direct RE. As they
define it (an increase in the indoor temperature), they find no evidence for
it, attributing all the discrepancy to over-estimated engineer calculations.
Although we argue in this article that direct RE is not only due to higher
temperatures, a large part of the discrepancy remains explained by overestimations
of the engineers’ projections.
The way we measure the direct RE, by using scenarios in a survey and
asking respondents about their reaction, is innovative in the field. It is the
first time that this kind of experiment is used to assess the rebound effect
for residential heating. Only two authors previously used a survey in the
rebound effect literature; Schleich et al. (2014) in Germany for lightning,
and Yu et al. (2013) in Japan for vehicles.
Most of the literature focuses solely on the direct RE, yet the indirect is
also important for energy policies. The indirect rebound effect is linked
to income and substitution effects, although the latter is often put aside
in the literature because it is considered as small (Thomas and Azevedo,
2013a). We also consider only the income effect, i.e., how the income freed
up by the efficiency improvement is spent. By spending it, overall energy
consumption will decrease by less than the amount predicted by the efficiency
6

improvement, as any product and service requires energy to be created and
used. To estimate this variation in energy consumption we take into account
not only the direct energy but also the embodied (grey) energy.
Most of the studies on the indirect RE are based upon income elasticities for
diverse categories of goods and services (Chitnis et al., 2013, for instance) or
input-output tables (Thomas and Azevedo, 2013a; Lenzen and Dey, 2002).
Data on the energy intensity of the categories of goods and services are
added to estimate the variation in energy consumption. Our estimation of
the indirect RE is based on self-reported respending patterns and data on the
embodied energy in kWh for categories of goods and services. We estimate
at both the direct and the indirect RE, which very few studies have achieved
before: Druckman et al. (2011) and Chitnis et al. (2013) conduct such analyses
in terms of GHG emissions, finding respectively a total rebound effect
between 12 and 34% and between 5 and 15%.
3 Data
Our data come from an original online survey carried out in September 2015
with 3,555 respondents representative of the Swiss population (excluding the
Italian-speaking region). Quotas matching data from the Swiss Federal Office
of Statistics were imposed for age, gender and region 1 . For income levels we
checked afterwards whether our sample was consistent with national data,
and it differs only slightly (see figure A.4 in the appendix). The lowest and
the highest categories are under-represented in our sample. Yet we do not
consider this as a problem in the estimation of the direct RE, since we show
later that both categories have an opposite effect on the magnitude of the
direct RE. Thus it does not lead to a systematic upward or downward bias.
The key questions of the survey regarding the direct RE were asked using
scenarios, which simulated an improvement in the efficiency of the heating
1 Four regions represent the country: Romandie, Alps and Pre-Alps, Plateau West and
Plateau East.
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system. Each respondent was faced with three different scenarios: 1) a small
improvement in efficiency (10, 15 or 20% randomly chosen); 2) a large improvement
(40, 50 or 60%); 3) a medium improvement (25 or 30%) arising
from a CO 2 neutral technology. Under each scenario, we asked respondents
whether they would modify their heating habits, and if yes by how much 2 .
We used both qualitative and quantitative questions to ensure respondents
understood the potential actions offered to them following an efficiency improvement
and to check they answered consistently.
We should also acknowledge that our survey rules out superconservation (i.e.,
negative rebound) and backfire (i.e., rebound larger than 100%) by design.
Indeed, in the quantitative question, respondents were constrained to choose
a number between 0 and the efficiency improvement in %. For instance, if
the scenario was based on an efficiency improvement of 20%, the scale of the
slider along which respondents could make a choice went from 0 to 20 (like
in figure A.2). We did so deliberately, in order to avoid potential confusion
in the respondents, who would probably have wondered how such cases are
possible. In light of the estimates of the direct rebound effect available in
the literature, most of which being around 20% and none being outside the
range from 0 to 100%, we do not consider these constraints as a flaw of our
design.
To estimate the direct RE and to analyse who rebounds the most, we use
a set of controls, namely education, income, owner or tenant status, satisfaction
regarding heating comfort, environmental attitudes, and indoor
2 First, in a qualitative question, we provided possible reactions, such as setting the
thermostat higher, airing more often, or heating earlier/later in the season. This qualitative
question was intended to help the respondents think about the potential actions
that have an impact on their fuel consumption related to heating and to frame them for
the next quantitative question. If at least one of the possible reactions was chosen, we
requested the respondent to state how much of the savings would be used to increase heating
services. Based on the definition of efficiency ε = S/E, we observe that when efficiency
improves by ∆ε%, services will increase by ∆ε% if energy consumption is kept constant,
or energy consumption will decrease by ∆ε% if services are kept constant, and any other
combination with ∆S% − ∆E% = ∆ε% is possible. This is the range of possibilities we
offer the respondents in the qualitative question. The questions relative to the direct RE
are available in the appendix figures A.1 and A.2.
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temperature. Table 1 shows descriptive statistics for these variables.
Table 1: Summary Statistics
Variable Mean Std. dev. Min. Max. N
Age 47.78 16.27 19 89 3,555
Education 7.37 1.92 2 9 3,554
Income level 3.97 1.41 1 6 2,924
Female 0.51 0.50 0 1 3,555
Tenants 0.58 0.49 0 1 3,555
Environmental attitudes 22.88 6.41 0 36 3,543
Heating satisfaction 10.40 3.22 0 15 3,477
Indoor temperature 21.20 1.60 9 30 3,444
Education goes from 1 (compulsory school not finished) to 9 (University and
higher vocational training). Figure A.3 shows the frequencies. We group
some levels together for the analysis, following groups from the Federal Office
of Statistics (FOS). The four groups are described in table A.1, as well as the
percentage from the FOS. Compared to the whole population, people with a
low education level (only compulsory school finished) are under-represented
in our sample, and people with a high degree of education over-represented.
This may slightly under-estimate the direct rebound effects, as we show in
the analysis that people less educated have a higher direct RE.
Heating satisfaction is constructed as an index using the answers to five questions
about the satisfaction on internal temperature in winter, uniformity
of temperature, quality of the ventilation, ease to modify the temperature
and the number of days with the heating on. Respondents could be totally
unsatisfied, rather unsatisfied, rather satisfied and totally satisfied of each
item. To create the index, we attributed points, respectively from 0 (totally
unsatisfied) to 3 (totally satisfied). Consequently the respondent totally unsatisfied
for the 5 items gets 0 point, whereas the respondent totally satisfied
for the 5 items gets 15 points. We proceeded in a similar way to build an
index of environmental attitudes. Such an index has been used previously in
the literature, by Best and Mayerl (2013). Nine items were presented to the
respondents who have to disagree-agree on a five-point scale. If the respon-
9

dent completely disagrees with one item, he gets 0 point, if he completely
agrees 4 points. Hence the minimal score is 0 (for those who care very little
about environment) and the maximum is 36. The (stated) indoor temperature
goes from 9 to 30 degrees, but 96% of the answers lie between 18 and 25
degrees. To diminish the impact of the outliers and because the answers are
not continuous , we create three categories:

83 studies containing 616 stated-revealed preferences comparisons for quasipublic
goods find that stated preferences estimates are smaller, but only
slightly. However the downward bias is not systematic, and they conclude
that there is no general need to correct stated preferences data. More recently,
Grijalva et al. (2002) studied visitors’ behaviour in a climbing area.
They find that intended behaviour matches aggregate actual data. Loomis
and Richardson (2006) examine the number of visits to a national park. They
conclude that the estimates from the stated and revealed preference methods
are not statistically different from one another.
To our knowledge, hypothetical choices have never been used to estimate direct
rebound effect in the heating sector. In view of the literature comparing
stated and revealed preferences, we acknowledge that there may be a bias,
but limited. To obtain results as robust as possible, we used two complementary
strategies. First, we collected information on the rebound effect in
two different flavors, once through a qualitative question and once through
a quantitative one. Based on these questions, we can distinguish consistent
and inconsistent respondents, with the possibility to keep only the consistent
ones for the analysis. Second, every respondent was faced with three different
scenarios, under which he had to provide answers to the same questions.
These repeated questions allow us to build a panel. When controlling for
individual heterogeneity in this way, our estimates will be based on changes
within individuals from one scenario to the other.
Moreover there are many advantages in using stated preferences, Whitehead
et al. (2008) summarize them. Stated preferences data are useful for the
analysis of new policies, for which revealed preferences data do not exist.
It is particularly relevant in the case of new policies leading to a change in
behaviour, in this case hypothetical choices may be the only way to gather
information. Additionally, it increases the sample size in a cross-section,
as usually the respondents answer several times the hypothetical questions.
Their choices can be treated as panel data, providing more information about
the preferences of each respondent.
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4 Empirical strategy
We estimate the direct rebound effect using formula (2). In the choice
experiment of the survey, potential savings are given. Respondents can subsequently
choose the variation of service level, i.e., the part of the potential
energy savings which is taken back because of people’s reaction. Answers
collected through this choice experiment thus provide all necessary data to
apply formula (2) directly: potential energy savings are given by the efficiency
improvement, while actual energy savings are deduced from respondents’ answers.
This strategy provides a different rebound effect for every individual
and each scenario We use it to test for the impact of individual characteristics
on its magnitude. The estimated equation is:
Direct RE i,t = α + β 1 Educ i,t + β 2 Income i,t + β 3 F emale i,t
+ β 4 Owner i,t + β 5 Environ i,t + β 6 Satisf i,t (3)
+ β 7 T emperature i,t + β 8 Scenario i,t + u i
Where i denotes the individual, t (= 1, 2, 3) denotes the scenario, and u is a
random intercept .
Satisf is the score on the index of heating satisfaction and Environ the score
on the environmental attitudes index. Income is divided in three classes
(CHF): 9000. The variable Owner indicates whether
the individual owns or rents is residence and whether the heating bill is
calculated on the individual consumption or shared among all the inhabitants
(calculated as a proportion of the m 2 of the home). Four categories exist for
this variable: owners with individual costs, owners with shared costs, tenants
with individual costs, and tenants with shared costs. The rationale for that
distinction is that tenants with individual costs should behave as owners.
This hypothesis has never been tested before. Scenario is the small, middle
(with the CO 2 neutral technology) or large efficiency improvement.
We use a two-part model to estimate equation (3). As described by Belotti
et al. (2015), a two-part model is appropriate when the outcome (y i ) has
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two features: 1) y i ≥ 0, and 2) y i = 0 is observed often enough. In our
dataset, the direct RE is either zero or a positive value, with about half
of the observations being zero. The two-part model accounts for the mass
of zeroes by first fitting a binary choice model, and then, conditional on a
positive outcome, fitting a linear regression model for the positive outcomes.
Equation (3) is the second part of the two-part model. The first part is the
binary choice model, and we use a probit model:
P r(Y i,t = 1|X i,t ) = φ(X ′ i,tβ) (4)
The vector X i ′ includes the same variables as in equation (3). In the two
equations of the model we use panel data regression method. It is more efficient
(lower standard errors) than pooled models, because the pooled model
assumes independence over i and t. In our case, because all the independent
variables are time-invariant, random effects are chosen. The random
and individual intercept u i accounts for autocorrelation due to the omitted
time-invariant variables for a respondent.
We expect a negative effect on the direct RE of education, income, heating
satisfaction and environmental attitude. It is obvious that people already
fully satisfied with their heating comfort will be less prone to a direct RE,
as well as people paying more attention to the environment. The effect
of income has been analysed in two previous studies (Chitnis et al., 2014;
Madlener and Hauertmann, 2011), both finding that richer people rebound
less. An explanation is that they were not restricting their usage of heating
before the efficiency improvement, and hence were already at their maximum
level of comfort. Some evidence of the impact of income on the rebound effect
can also be found in macro-level studies and in various fields. For instance,
Small and Van Dender (2007) find that the rebound effect for motor vehicles
declined over 1966-2001 in the US, which they explain by the rise in
incomes. More in general, rebound effect estimates are larger for developing
countries than for developed ones, which is interpreted as the impact of
income constraints in the former (e.g., Azevedo, 2014). Therefore, an inter-
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esting contribution of our paper is that we are able to determine the effect
of income on the rebound effect at the individual level. For education it is
less obvious why less educated people would rebound more. A possibility is
that they are less aware of the impact of their actions on the climate, therefore
they will not restrict their heating usage for environmental concerns.
Finally we expect that the owners with shared or individual costs and the
tenants with individual costs rebound more than the tenants with shared
costs. The latter have fewer incentives to limit their heating usage and then
could already have reached a maximum heating comfort prior to the efficiency
improvement. Madlener and Hauertmann (2011) find that tenants rebound
more than owners, but they do not examine the features of the heating bill.
For temperature we expect a negative coefficient, as people gets closer to
their satiety point.
With the scenario dummies, we test whether the direct RE is linear with the
efficiency improvement, i.e., whether a larger improvement would induce a
larger direct RE. The green technology in the second scenario is used to see
whether people react more (have a larger direct RE) if on top of the efficiency
improvement they benefit from a green technology. The hypothesis is that
people would react more because they get rid of a sort of guilt of consuming
more of a polluting service. We also tested for the impact of other variables:
age, the region, the housing type, the size of the house or flat, the size of the
household, the number of children, rural or urban household. None of them
was significant.
5 Results
5.1 Estimations and Analysis of the Direct
Rebound Effect
The implementation of formula (2) gives an individual RE for each respondent
in each scenario, i.e., three estimates for every respondent. The overall
14

esults are very similar for the three scenarios, with an average direct RE estimated
between 10 and 15%. When the three scenarios are pooled together,
the average direct RE is 12.2%. Table 2 reports the values obtained and
figure A.5 displays the distribution of the strictly positive values. A direct
RE of 12.2% is rather in the low-end of the estimations obtained so far in
the literature, but it is consistent with the reviews by Sorrell et al. (2009)
(10-30%) and Nadel (2016) (1-12%).
Table 2:
Estimation of the Direct Rebound Effect
Mean Std. dev. Min. Max. N
Direct RE (Small eff. improvement) 0.147 0.226 0 1 3,555
Direct RE (Large eff. improvement) 0.106 0.187 0 1 3,555
Direct RE (Middle eff. imp. + CO 2 neutral) 0.113 0.193 0 1 3,555
Direct RE: pooled 0.122 0.203 0 1 10,665
Note: The means are calculated using formula (2) and can be multiplied by 100
to obtain to direct RE in %. About half of the respondents have a zero direct RE.
In the literature, the only possibility usually considered for a direct RE in
heating is an increase in the temperature. Yet, people can react to an efficiency
improvement in various ways, for instance by airing more often their
house, or starting to heat earlier in the season, etc. The purpose of the qualitative
question in the survey is precisely to investigate such possibilities. The
question is made of 5 items about the hypothetical change in behaviour, with
three possible choices: no, maybe, yes. Figure A.6 in the appendix shows
the answers. Airing longer and more frequently is indeed what respondents
chose the more often, before paying less attention to heating in general. Increasing
temperature only comes as the third most popular choice. It shows
that by considering only the increase in temperature, the direct RE will be
underestimated (see also Volland, 2016).
15

One concern with data coming from a choice experiment is to make sure
that respondents understood correctly the questions. In our setting, we can
control for that by comparing
answers to the qualitative question on the
rebound effect (an easy question) and to the quantitative question (more
difficult). We attributed zero, one and two points to the qualitative question
(no, maybe, yes) to obtain an overall view of the changes in the heating
habits. Hence, more points indicate more substantial modifications of habits
and a larger direct rebound effect. Someone who would change nothing gets
0 point. Table 3 highlights that respondents answered consistently to both
questions. The more they decided to change in the qualitative question, the
largest changes they reported in the quantitative question.
Table 3: Consistency in the stated preferences
Qualitative RE 0 1 2 3 4 5 6
Quantitative RE 0% 17% 21% 26% 29.8% 33.9% 32.8%
Respondents with 0 point in the qualitative question get mechanically a direct RE
of zero as the quantitative question did not appear for them. The qualitative RE is
an index, while the quantitative rebound is estimated with equation (2).
By comparing the answers of each respondent to the qualitative and quantitative
questions, we can define who is inconsistent. An inconsistent respondent
is someone who said in the qualitative question that he would change his heating
behaviour along at least one dimension, but did not report any change
in the corresponding quantitative question. Hence for him the quantitative
direct rebound we calculate is zero, while his qualitative rebound is non-zero.
It is likely that he did not understand well the quantitative question, or he
was not able to answer it. Inconsistent respondents are less educated and
have a lower income than the average. As a robustness check, we perform
our analysis by excluding them. It does not change significantly our results.
Once the direct RE estimated for each individual, we can assess which characteristics
influence the magnitude of the direct RE, or who rebounds the
most. This is investigated using equation (3) with a two-part model, whose
results are reported in table 4). For the first part of the model (a probit es-
16

timation), we report the marginal effects at the means. The marginal effects
indicate the increase in the probability of having a positive direct rebound
(versus a zero direct rebound) associated with a one-unit increase of the corresponding
variables. Only respondents with a positive direct RE are kept for
the second part, and the coefficients in this estimation are to be interpreted
as the increase in the magnitude of the direct rebound following a one-unit
increase of the corresponding variable.
In both estimations, education and income have the expected negative sign.
For instance, people with the highest level of education have a 20% lower
probability to have a positive direct rebound, compared to people who stopped
at compulsory school. However the effect of education is not significant in
the linear regression. Being a woman decreases both the probability and
the magnitude of the direct RE. Environmental attitudes and heating satisfaction
also have the expected negative sign: an increase in one standard
deviation for environmental attitudes leads to a decrease by 4% in the probability
of direct rebound, for heating satisfaction it is by 8%. People more
environmentally conscious have also a lower direct RE.
The coefficients related to indoor temperature are unexpectedly positive. It
is robust even if we change the choice of the degrees in the categories, and
also if we drop the outliers (less than 17 degrees or more than 25). However
if the reference category is (19.5-22.5), the last category is not significantly
different. Our interpretation is that a part of the people with a low indoor
temperature have a preference for this and will not change even if the service
is cheaper. A question in the survey is “Would you be willing to diminish
the temperature in winter?” They are 56% answering that they already
do it, versus 38 and 20% respectively for the middle and high temperature
categories.
The negative signs on the magnitude of the efficiency improvement shows that
people rebound, but up to a certain point and not indefinitely. Imagine you
would feel more comfortable if you heat your home slightly more, for instance
using 15% more of heating energy. Then if the efficiency improvement is 15%
you have a direct RE of 100%, but if the efficiency improvement is 50% your
17

Table 4:
Two-part model for the Direct RE
Probit model, MEs
Linear regression
Apprenticeship −0.151 ** −0.037
(0.076) (0.042)
Baccalaureate −0.186 ** −0.036
(0.079) (0.044)
University and higher vocational
training
−0.200 *** −0.067
(0.076) (0.042)
Income 4500-9000 −0.029 −0.025 *
(0.023) (0.014)
Income >9000 −0.074 *** −0.023
(0.025) (0.015)
Female −0.035 ** −0.035 ***
(0.017) (0.010)
Owners shared costs 0.032 0.044 **
(0.035) (0.021)
Tenants shared costs −0.029 −0.015
(0.023) (0.014)
Tenants individual costs −0.002 0.004
(0.021) (0.013)
Environmental attitudes −0.006 *** −0.002 ***
(0.001) (0.001)
Heating satisfaction −0.024 *** −0.002
(0.003) (0.002)
19.5-22.5 degrees 0.066 ** −0.005
(0.029) (0.019)
>22.5 degrees 0.089 *** 0.004
(0.034) (0.021)
Large eff. improv. −0.020 ** −0.079 ***
(0.008) (0.005)
Middle eff. improv. + CO2 neutral −0.033 *** −0.057 ***
(0.008) (0.005)
Constant − 0.468 ***
#Obs. 8, 391 3, 812
Note: Standard errors in parentheses. ∗ p < .1, ∗∗ p < .05, ∗∗∗
p < .01
The dependent variable is the direct RE as estimated with formula
(2).
18
(0.054)

direct RE is only 30% (= 15/50). Here the magnitude of the improvement
decreases both the probability to rebound and the magnitude of the direct
rebound. In the probit model, the scenario with the middle improvement
and the CO 2 neutral technology leads to a higher decrease in probability
of rebound than the large efficiency improvement (the two coefficients are
statistically different at the 10% level). Contrary to what was expected, the
green technology does not seem to lead to a higher direct rebound, but rather
to a decrease in the probability of rebound. On the magnitude of the rebound,
the effect is not clear. Some people rebound only in this third scenario,
but they are only 116 respondents (3.3% of the sample). Interestingly, a
probit model shows that environmental attitudes as well as a low indoor
temperature increase the likelihood of belonging to this group of respondents.
It thus seems that a “green rebound” does exist, but only a marginal one.
Hypotheses on who will rebound the most have often been made in previous
research, but very rarely tested due to the lack of data. We here show that
education, income, environmental attitudes and heating staisfaction decrease
the probability of the direct rebound effect. Our findings highlight important
facts: First, the magnitude of the direct RE is very heterogeneous among
people. Second, the direct RE depends on the level of the efficiency improvement,
large improvements do not mechanically imply a higher rebound than
smaller improvements. Third, measures targeting low income households (for
instance subsidies conditional on income) will be less effective in terms of energy
saved, because they are associated with larger rebound effects. Chitnis
et al. (2014), by using expenditure elasticities for different socio-economic
groups, have reached similar conclusions concerning income.
5.2 Estimation and Analysis of the Indirect
Rebound Effect
To estimate the indirect RE, we use the answers to the re-spending question
(i.e., how
respondents would allocate CHF 1,000 saved on heating among
19

nine categories of goods and services) and the embodied energy for each
category. Potential savings are calculated as the amount of embodied energy
saved when spending CHF 1,000 less on heating: 1,000 [CHF] × 10.64
[kWh/CHF] = 10,640 [kWh] (see figure A.7 in appendix). We then compute
the emodied energy amount consumed when re-spending the CHF 1,000 on
various categories of goods. We thus have potential and actual savings for
each household, which allows us to apply formula 2 and retrieve an estimation
of the indirect rebound effect.
We obtain an average indirect RE of 24.7%. This result means that on
top of the direct RE, about a quarter of the potential energy savings are
lost due to the re-spending of the savings initially made on heating. The
distribution of the individual indirect RE is shown in appendix figure A.8.
The median rebound effect is at 14.5% and the largest values are above
100%, which corresponds to a backfire situation because the individual ends
up consuming more energy than before the efficiency improvement. Such a
situation is possible if a large part of the savings made on heating is used
for additional air travel.
As for the direct RE, we can assess which personal characteristics have an
impact on the magnitude of the indirect RE. However, there is an important
difference with the direct RE, in the sense that the indirect RE is less salient
and tangible for people because we take into account the grey (embodied)
energy. They may know that travelling by plane or driving their car is energy
intensive, but they are much less likely to know whether going to a restaurant
is more or less energy intensive than buying clothes.
In our model, we include similar determinants as in equation (3): socioeconomic
characteristics, environmental attitudes, and tenant/owner status.
Furthermore, in order to investigate potential links between the direct and
indirect rebound effect, we include the direct rebound effect estimated earlier
in the determinants of the indirect rebound effect. In theory, there is an
automatic negative link between the direct and indirect RE, because the
larger the direct RE, the lowest the remaining savings to spend on other goods
(Thomas and Azevedo, 2013b). However there could be other links between
20

the two RE, for instance if people caring very few about the environment
would display both a large direct RE and a large indirect RE. Thanks to the
design of our survey, this automatic link is absent, and we are able to study
an empirical and unconstrained relation that could potentilly exists between
the direct and the indirect RE. We use the natural logarithm of the indirect
RE to diminish the impact of the long right tail in the distribution. The
model is:
ln (Indirect RE i ) = α + β 1 Direct RE i + β 2 Educ i
+ β 3 Income i + β 4 Environ i + β 5 Owner i + ε i (5)
We expect a negative coefficient for environmental attitudes, as people with
green values pay probably more attention to the CO 2 footprint of their consumption.
For the other explanatory variables, we do not make any hypothesis.
The results are presented in table 5.
The environmental attitudes have the expected negative sign. By comparing
the re-spending patterns of people in the first quartile and the last quartile
of the environmental attitudes, we observe that people in the first quartile
re-spend more on air travel, car fuel and electrical appliances, and less on
public transport and leisure. The differences were tested with t-tests on the
means. Tenants have a higher indirect RE than owners, and now the type
of the heating bill does not matter any more. The main difference in the
re-spending patterns of tenants is that they choose to allocate less to savings
than owners, and as savings have a low embodied energy it explains why they
have a larger indirect RE.
The positive coefficient of the direct RE shows that the larger the direct RE,
the larger the indirect one. The main difference in terms of re-spending
patterns between people who never rebound and those who always rebound,
is that the latter save less money. They also re-spend a larger part on air
travel and car fuel.
21

Table 5: Indirect Rebound Effect
Direct RE 0.212 ***
(0.057)
Apprenticeship 0.188
(0.117)
Baccalaureate 0.247 **
(0.122)
University and higher 0.188
(0.117)
Income 4500-9000 −0.037
(0.037)
Income >9000 −0.066 *
(0.039)
Environmental attitudes −0.008 ***
(0.002)
Owners shared costs 0.060
(0.055)
Tenants shared costs 0.123 ***
(0.034)
Tenants individual costs 0.124 ***
(0.031)
Constant −1.774 ***
#Obs. 2, 822
Linear regression
(0.129)
Note: Standard errors in parentheses. * p < 0.10, ** p < 0.05, *** p < 0.01.
The dependent variable is the natural logarithm of the indirect RE.
22

5.3 Estimation of the Total Microeconomic
Household Rebound Effect
To compute the total household rebound effect we add up the direct and
indirect rebound effects. We obtain an average total household RE of 36.7%
with a median at 23.2%. Figure A.9 in appendix shows the distribution of
this total RE.
Our main finding is that, in the domain of residential heating, about one
third of the energy savings (in terms of kWh) initially expected after an
efficiency improvement is lost due to the direct and indirect rebound effects.
However, heterogeneity is large, and this total household RE is an average.
Very few studies use the same dataset to estimate direct and indirect RE
together. One comparable result is from Chitnis et al. (2014) for the UK.
Using expenditure elasticities, they find a rebound effect (direct and indirect)
between 0 and 32% but in terms of greenhouse gas.
6 Conclusion
In this paper we estimate the rebound effect in residential heating in Switzerland
at the household level, including both direct and indirect rebound effects
(RE). Direct RE arises when households adapt to an improvement in
efficiency of their heating system by increasing their demand for heating services.
The rationale is as follows: improved efficiency means a cheaper service,
then why not consume more of this service? Using an original dataset
created for this purpose, we estimate an average direct RE between 10 and
15%. It means that only 85-90% of the energy savings expected after an
efficiency improvement from an engineering perspective are in fact realised.
This result is not too worrying for policy makers who can still count on
efficiency to play a major role in the energy transition. However to see the
whole picture, one should also take the indirect rebound effect into account.
Indirect RE stems from the savings made on heating after an improvement in
23

efficiency, and which are re-spent in various activities. Purchasing any good
or service implies the consumption of energy, because every good and service
consumes energy in order to be produced and used. Even bank savings
use energy as they are lent for some other purposes. As the majority of
goods and services are less energy intensive than heating, overall energy
consumption will generally decrease after an improvement in efficiency of
the heating system, except if the money saved is used on a very energy
intensive good (such as travelling by plane). In such cases, overall energy
consumption may even increase, which is called a backfire effect or in other
words a rebound effect of more than 100%.
Our analysis shows that backfire is extremely rare and that the average total
household rebound effect (the direct + indirect RE) is around 36%. This
amount is not negligible, and means that about one third of the energy
savings initially expected is lost due to the direct and indirect rebound effects.
For policy makers in charge of the energy transition it means that the role
of efficiency improvement is currently probably overestimated.
Our study also highlights the large heterogeneity of the magnitude of the
direct and indirect RE among individuals. Those who are affluent, educated,
satisfied with their heating comfort, and environmentally conscious are less
prone to have a direct rebound. We show for instance that people with a
lower income have a larger direct rebound effect. As a consequence, measures
targeting those people will be less effective in terms of energy saved. Finally
we also show that the level of efficiency improvement impacts negatively the
direct rebound, i.e., it is not because you face a large efficiency improvement
that you will rebound more.
24

Appendix A
Appendix
Figure A.1: The qualitative question about the direct RE
Note: The scenario is presented before, with the efficiency improvement and the
costs saved given in % of their current heating costs.
Figure A.2: The quantitative question about the direct RE
Note: The slider is constrained to be at the maximum equal to the efficiency
improvement in %.
25

Figure A.3: Level of education
Compulsory school
School of commerce/household school
Elementary vocational training
Apprenticeship
Diploma
Full−time vocational school
Baccalaureate
University and higher vocational training
0 200 400 600 800 1,000 1,200 1,400 1,600 1,800
Frequency
Note: Highest degree of education achieved by the respondent.
Table A.1: Level of education in four categories
Education Freq. Percent Percent in Switzerland
Compulsory school 41 1.15 11.9
Apprenticeship, diploma, etc. 1, 338 37.65 38.25
High school 382 10.75 8.25
University and higher vocational training 1, 793 50.45 41.65
The percent in Switzerland comes from the Federal Office of Statistics for 2015.
The category apprenticeship,... includes all the categories between compulsory school and baccalaureate.
26

Figure A.4: Household gross income in the sample versus at the national
level
Note: Rebound stands for the sample of the survey, SHP for the Swiss Household
Panel. The SHP provides data on the gross income at the household level.
27

Figure A.5: Direct RE
Density
0 4.622
Median
0 .2 .4 .6 .8 1
Direct RE (n=4’808)
Note: The average direct RE is 12.2%. In the histogram the zero-rebound are
dropped, they count for about half of the observations.
28

Figure A.6: Qualitative question: changes in behaviour
0 500 1,000 1,500 2,000 2,500 3,000 3,500
Increase temp. Sooner/later in the year Airing Less attention No decrease if absent
Maybe
Not:Note: Over 10,665 answers (3 answers per respondent). For the last category:
over 7,793 answers.
Yes
29

15
14.57
Figure A.7: Energy Intensity in kWh/CHF
13.90
10
10.24
kWh/CHF
6.96
5
2.61
0
1.75 1.61 1.36 1.21
0.92
0.29
Electricity
Air travel
Heating
Car fuel
Food and beverages
Public transport
Leisure and eating out
Electrical appliances
Other goods
Other services
Clothes and shoes
Note: Embodied energy from LCA data. Source: Ecoinvent (Zürich), and Ivan
Tilov (University of Neuchâtel).
30

0
Density
10.67
Median
Figure A.8: Indirect RE
0 .2 .4 .6 .8 1 1.2 1.4
Note: The average indirect RE is 24.7%.
Indirect RE (n=10’290)
31

Figure A.9: Total household rebound effect
Density
0 5.204
Median
0 .2 .4 .6 .8 1 1.2 1.4 1.6 1.8 2 2.2 2.4
Total microeconomic RE (n=10’290)
Not:Note: The average total household rebound effect is 36.7%.
32

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