2015-10
An Analysis of Household Electricity Saving
Behavior Using the Stochastic Frontier Function
Fumitoshi Mizutani Eri Nakamura

An Analysis of Household Electricity Saving Behavior
Using the Stochastic Frontier Function

Fumitoshi Mizutani
Kobe University
Graduate School of Business Administration
2-1 Rokkodai, Nada-ku, Kobe 657-8501 Japantoshi@kobe-u.ac.jp

Eri Nakamura
Kobe University
Graduate School of Business Administration
2-1 Rokkodai, Nada-ku, Kobe 657-8501 Japanenakamura@person.kobe-u.ac.jp

Correspondent Author: Fumitoshi Mizutani
toshi@kobe-u.ac.jp

1

An Analysis of Household Electricity Saving Behavior
Using the Stochastic Frontier Function[Abstract]: The main purpose of this study is to investigate the difference between incentive effects
and physical condition effects in electricity saving behaviors of households, by applying stochastic
frontier models for the demand function. As for incentives, we consider both internal incentives suchas environmental consciousness, and external incentives such as the price system and information
feedback.

This paper makes three contributions to the existing literature.

First, we consider thedifference in room for saving electricity among households by labeling in this paper the amount ofelectricity consumption which is possible to be reduced for energy saving as “consumption slack”(i.e. incentive effects), which we separate from the minimum necessary amount of consumption
impossible to be reduced for energy saving (i.e. physical condition effects).

Our second

contribution is that we take the novel approach of using the stochastic frontier model to distinguishthe reducible amount from the minimum necessary amount of electricity consumed amonghouseholds. Last, we empirically examine which of the internal or external incentives are more
effective in reducing household electricity consumption. Using data on 561 Japanese households in
2012, we obtain the following results.

Consciousness of consumption is more important toelectricity saving than external incentives such as demand response and information feedback.
Without such consciousness, demand response alone increases consumption slack. Conversely,
demand response can reduce consumption slack when combined with a household’s conscious
saving.

Other findings indicate that in evaluating saving performance, it is necessary to refer toconsumption slack rather than to households’ self-evaluation or the observed total amount ofconsumption.

[Keywords]: Energy saving; Electricity; Stochastic frontier model; Demand response; Consumer
behavior[JEL Classification]: D1, M2, L9, Q4

1. Introduction
In the area of consumer behavior research, the electricity saving behavior of households
has been an important issue.

Based on implications obtained from research, policy makers haveproposed various incentives to encourage households to save electricity, including demand response
schemes such as peak-pricing systems and information feedback.

At the same time, there has beena growing trend among consumers to save electricity voluntarily, due in large part to increased
environmental consciousness or consumers’ awareness of themselves as members of society. A
2good example of voluntary saving by consumers is the case of what happened in the aftermath of theGreat East Japan Earthquake of 2011, when nuclear electricity generation was greatly reduced bydamage to nuclear plants caused by the disaster.

In the wake of the nuclear shutdown, there wasvoluntary and united movement to save electricity not only among business entities but also amonghouseholds in Japan.

Moreover, even after the worst of the electricity crisis had passed, many

Japanese households continued to maintain the lower levels of consumption to which they had
become accustomed during the energy crisis.

Thus, energy saving behavior can be motivated bothby external incentives such as demand response schemes in the form of price systems, information
feedback, and internal incentives such as a saving consciousness or awareness of limitations.
However, room for reducing electricity consumption can differ according to households’
characteristics and lifestyle, since these factors affect the minimum necessary amount of electricity
households require.

A house with more rooms, more family members, and more appliances

naturally needs more electricity.

The electricity required by a household where family members

habitually stay home in the daytime exceeds that of a household where all family members go out.
Nevertheless, previous studies, such as those by Dianshu et al. (2010), Ek and Soderholm (2010),
and Gronhoj and Thogersen (2011), have overlooked the difference in the minimum necessary
consumption of electricity among households and have focused simply on the total amount ofelectricity consumption observed.

As far as we know, no studies have identified electricity

consumption as the sum of the minimum necessary amount which cannot be reduced for electricity
saving and the amount which can be reduced to achieve more efficient consumption.
The purpose of this study is to investigate the effects of internal and external incentives forhouseholds to save electricity consumption, while considering the difference in the minimum
necessary amount of electricity among households. This paper makes three contributions to theexisting literature.
Our first contribution is that we consider the difference in room for saving electricity
among households by labeling in this paper the reducible amount of electricity consumption as“consumption slack,” which we separate from the minimum necessary amount of consumption
which cannot be reduced for energy saving.

Although the minimum necessary amount ofelectricity can differ depending on households’ characteristics, most previous studies simply refer tothe observed amount of total electricity consumption when they discuss policies for electricity
saving, instead of recognizing that the observed amount includes not only reducible consumption but
also necessary consumption.

Thus, they rarely consider that the effort levels to be achieved forsaving are different among households depending on certain characteristics.

For example, Dianshu

et al. (2010) and Wang et al. (2011) implicitly assume that all electricity consumption can beclassified as reducible consumption, since they do not consider that there can be necessary
consumption for households to subsist.

In contrast, Thogersen and Gronhoj (2010) consider that3

electricity consumption is determined by the physical characteristics of a house, an approach which
excludes the idea that there can be extra consumption arising from the family’s intention and attitude
regarding efforts to save electricity.
Second, we use the stochastic frontier model because it enables us to identify electricity
consumption over and above what is necessary. While the stochastic frontier model is frequently
used in the area of productivity analysis, it has not been applied to other areas, such as consumer
behavior.

Thus, we contribute to the area of consumer behavior research by introducing this new

methodology to examine the above-mentioned two types of consumption, and we contribute to thearea of stochastic frontier methodology by showing its application to cases in other fields.
Last, we empirically examine which of the internal or external incentives are more
effective for households.

Previous studies mostly investigate either internal or external incentives.

For example, Momani et al. (2009), Gronhoj and Thogersen (2011), and Karjalainen (2011) focus onexternal policies such as daylight saving time (DST) and information feedback about electricity
consumption, environmental costs, proportion of consumption by appliance, and historicalcomparison.

On the contrary, Dianshu et al. (2010) and Wang et al. (2011) investigate mainlyinternal incentives such as intention and the attitude toward saving electricity.

The effects ofinternal and external incentives are rarely compared, except for in a few studies such as Ek andSoderholm (2010), who, however, do not consider the differences arising from household
characteristics.
This paper consists of five parts after the introduction.
studies on saving electricity.
the empirical model.

Section 2 reviews previous

Section 3 explains the theoretical background of our model and shows

Section 4 explains the data and variables used in the estimation.

shows the estimation results.

Section 5

Section 6 summarizes the main conclusions.

2. Previous Studies
So far, there has been no research examining consumption slack separated from necessary
consumption by using the stochastic frontier model to determine the relationship or to compare theeffects of internal and external incentives on households’ electricity saving behavior.

Table 1shows previous studies on consumer electricity-saving behavior, with a special focus on empiricalstudies.

***************
Table 1***************

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Previous studies have certain common characteristics.

First, they investigate various

determinants such as demand response1 (DR) including price systems, saving time policies, energy
efficiency labels and standards for products, environmental attitudes, social interaction, and physicalaspects of houses, though they rarely categorize these factors into external or internal incentives orcompare their effects on electricity saving behavior. For example, Faruqui and George (2005),
Herter et al (2007), Momani et al. (2009), Herter and Wayland (2010), Faruqui and Sergici (2011),
Gronhoj and Thogersen (2011), Karjalainen (2011), Ida et al. (2013), Faruqui et al. (2014), andJessoe and Rapson (2014) focus on external incentives, such as demand response in the form of timeof use (TOU), critical peak pricing (CPP), and variable peak pricing (VPP), information feedback
about consumption, historical data, consultation, and saving time policy.

Some studies, such as

Dianshu et al. (2010), Ek and Soderholm (2010), Thogersen and Gronhoj (2010), and Wang et al.
(2011), investigate internal incentives such as intention, attitude, and subjective norms for savingenergy, in addition to external incentives, though most of these rarely examine the relationshipbetween these two types of incentives or determine which type is more effective on electricity saving
behavior.
The second common characteristic of previous studies is that they neglect to identify theirreducible and reducible consumption of electricity.

The studies mentioned above all examine thetotal electricity consumption observed in experiments, instead of breaking it into the irreducible andreducible amounts of electricity consumption.

Even when households consume the same amount ofelectricity, the consumption slack to be reduced is different among these households, since theminimum necessary amount to subsist differs according to households’ physical aspects. Thus,
there is a need for studies identifying the above-mentioned two types of consumption.
The third common characteristic of previous studies is that they use the logit/probit modelor OLS estimation for analysis or speculate based on descriptive statistics of questionnaires. For
example, Momani et al. (2009) summarize the descriptive statistics of a survey of residential andcommercial sectors in Jordan.

Similarly, Dianshu et al. (2010) examine survey questionnaires from615 households in 14 cities in China based on descriptive statistics. Gronhoj and Thogersen (2011)
and Karjalainen (2011) also speculate based on descriptive statistics.

On the other hand, Ek andSoderholm (2010) use an ordered probit model, and Wang et al. (2011) use a logit model.

Faruquiand George (2005), Herter and Wayland (2010), Ida et al. (2013), and Jessoe and Rapson (2014)
implement OLS estimation.

Since logit/probit model, OLS estimation, or descriptive statistics

cannot identify the irreducible or reducible consumption of electricity, other methodology taking into
1

Demand response is the system of encouraging consumers to save electricity by themselves, whichis commonly implemented in many countries. TOU, CPP, and VPP are the typical examples of DR.
TOU applies different price systems for peak-time and off peak-time (e.g. night-time fee). CPP
applies high price during an emergency such as a sharp increase in electricity demand or a system
crisis. VPP is the variable CPP in which the price varies according to the crisis level.
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account this identification is needed in the analysis. In our paper, we propose the application ofstochastic frontier methodology.
Other common characteristics of previous studies are that they use mostly household datafrom specific areas (e.g. Herter et al 2007; Dianshu et al. 2010; Ek and Soderholm 2010; Herter andWayland 2010; Thogersen and Gronhoj 2010; Faruqui and Sergici 2011) and that they derive thedata from experiments and/or questionnaires (e.g. Faruqui and George 2005; Momani et al. 2009;
Faruqui et al. 2014).

Our paper shares these characteristics, since we obtain data from anexperiment and questionnaires yielding information from households in the Keihanna area in Japan.

3. Model3.1 Theoretical BackgroundIn general economics, the determination model of demand (q) is expressed as price (p) andthe other control vectors. However, since electricity has the aspect of a product essential for living,
we consider that the total amount of electricity consumption can be the sum of an irreducible amountrequired regardless of price, and a reducible amount arising from consumers’ wasteful use ofelectricity.

Thus, in our paper, the total amount of electricity consumption

is expressed as

equation (1).
| :(1)Equation (1) shows, under the condition that the pricehouseholds (is within the acceptable range for), that the total amount of electricity consumption

minimum necessary amount of electricity of a house“consumption slack,” expressed as .

andis the sum of the

reducible amount called

We consider that the necessary amount of electricity isdetermined by the physical aspects of a household, shown as.

For example, a house with morerooms, more appliances, and more family members requires more electricity by nature. Thesephysical aspects are difficult to change, or at least would require great cost for the household tochange.

Thus, assuming that the household maintains its current lifestyle,

fixed cost.

As thisis interpreted as

cannot easily or soon be changed by a household, this would beinterpreted as a mid- or long-run aspect in electricity saving behavior.
On the other hand, consumption slack, , is determined by a household’s level of effort tosave electricity, expressed aselectricity saving behavior.

(That is,0).

Therefore, thisis considered a short-run aspect inis expressed as equation (2).

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(2)

is a decreasing function of
Moreover,

0.

, and

is determined at the level maximizing a household’s utility, as shown inequation (3).
–(3)

In equation (3), the level of effort to save electricity

is determined to maximize the utility, definedas the difference between the reward from electricity saving shown aselectricity shown as.

result in more utility.

This

is an increasing function ofand the cost of saving, which means that more efforts

means that, for example, by saving electricity, a household can

reduce its electricity expenses.

In contrast,

includes the negative feelings arising fromhaving to pay attention to saving electricity, for example, by diligently switching off lights.
Meanwhile, the utility maximization in equation (3) relies on various conditions related toa household’s environment and intention, such as the existence of price policy or consciousness ofenergy saving.

Thus, the level ofis determined on the premise of these surrounding conditions.

The conditions are categorized into two types: (i) internal incentives and (ii) external incentives.
Internal incentives include environmental consciousness and the awareness of being a member ofsociety.

For example, people who are educated that too much electricity consumption hurts theglobal environment can be habitually vigilant about electricity consumption.

External incentives

include demand response such as peak-time price system and information feedback.

We assumethat price, considered to be the main and the most important factor for demand in general economics,
is one of the external incentives influencing a household’s level of effort, and there are otherincentives to be considered.

Thus, the situation of utility maximization is shown in Figure 1.

***************
Figure 1***************In Figure 1, I is the vector of internal incentives and E is the vector of external incentives.
When the value of (I, E) is (I1, E1), the functions ofandare determined asand, respectively.

Similarly, when the value of (I, E) is (I2, E2), the functions are expressed as

andFor simplicity, we depict.

as a linear function in Figure 1, but the7discussion here can also be applied for the other functional forms.
is the point at which the difference between
is

∗

and
∗

when the value of (I, E) is (I1, E1) andmaximized utility is∗effort level shown as

and
∗

∗

, respectively.

Since the maximized level ofis maximized, the optimal effort level

when the value of (I, E) is (I2, E2). The

This means that there is an individual optimized

according to each value of the vectors (I, E) and that whichever∗ischosen in reality depends on the realized value of these condition vectors.
Therefore, a household’s utility is maximized bydetermined. This means thatafter the condition factors (I, E) areis a function of (I, E), as shown in equation (4).
,(4)

Substituting equation (4) into equation (2) generates equation (5).
,(5)

Substituting equation (5) into equation (1) generates equation (6).
,

| :

(6)

Equation (6) shows that the demand
aspects

can be deconstructed into necessary consumption in physical,and consumption slack.

3.2 Empirical ModelIn this section, based on the previous discussion, we specify the empirical model onelectricity consumption.

Based on equation (6), we specify the empirical model as equation (7).

ε,
:

,
:

,
,

,
,

:

,
,

,

,

,
,

8

:

.
(7)

In this equation, the error term, ε, follows the normal distribution N(0, ).

We consider

the minimum necessary amount of electricity to be determined by the physical aspects of ahousehold, which include housing facilities and family characteristics.

These are the factors whicha household cannot change without any alteration of their current lifestyle.
is floor size in the house,

aspects of households,

is the number of people in theis the number of air conditioners in the house,family,in the house,is house type, andis the number of refrigerators

is the number of people at home in the daytime,
is LED use for lighting.

normal distribution Nt(A’X, ).

Among the physicalis house age,is consumption slack following truncateds is specified as follows.

,
:

,
,

,

,

:

,
,

,

,

ϵ: error term.
(8)

As for internal incentives, we consider the following three types: (i) consciousness ofsaving energy ((). Concretely,), (ii) attitude toward real action (), and (iii) willingness to save electricity

is proxied by attitude toward renovation of the house, which is considered

to relate with real action for saving.
As for external incentives, we also consider the following two types: (i) demand responseis the variable expressing the DR scheme, that is,(DR) scheme and (ii) information feedback.

the number of days on which the household is under the DR scheme.
variables expressing information feedback.
information, andandare theis the frequency of checking the demandis the frequency of checking the graph of electricity consumption.

Moreover, in order to clarify the relationship between internal and external incentives, we generate
the cross terms of Icon and EDR, IconEDR.

The cross-terms of these variables are used to test whether

or not these incentives jointly contribute to households’ saving on electricity consumption.
We apply the stochastic frontier model for the system of equations (7) and (8).

Originally,

the stochastic frontier model was used in the area of efficiency and productivity analysis.
9Itestimates the theoretically-derived frontier, that is, the set of levels which actors (mostly firms) canachieve when they operate most efficiently.
based on economic theory.

This frontier is defined as production or cost function,

And the distance of the observed production or cost level from thefrontier is defined as inefficiency.

Therefore, the stochastic frontier model identifies inefficiency

and the frontier from the observed production or cost level.
Applying this model to our case, the frontier is defined as the minimum necessary amountsof electricity for households, as these have in common that they express the resources actors require
to maintain their lives even when they operate most efficiently.

Consumption slack is an analogy

for inefficiency, since both signify resources arising from wasteful use that can be reduced.
For the estimation, equations (7) and (8) are estimated simultaneously by the maximum
likelihood method.

4. Data and Variables
The data for the estimation is obtained from a project named Experimental Project forEnergy and Social Systems for the Future Generation in Keihanna Eco City. This project wasorganized by the city, electric companies, related private companies, and research institutes in order
to conduct social experiments regarding energy optimization in the area of Kyoto, Japan. We
obtained summer-time experimental data from July 23th to September 28th in 2012.

The data

consists of observed households’ electricity consumption, the characteristics and profiles ofhouseholds, and the questionnaires implemented at the end of the experiment.

Some data, such as

on electricity consumed, temperature of individual days, and the various actions of households
during the experiment are panel data, while some data related to questionnaires and thecharacteristics of households are cross-section data. We transformed panel data into cross-section
data through aggregating the data by household.

As a result, our data consists of the cross-section

data of 561 households in the summer of 2012.
In the experiment, each household is categorized into Groups A, B, C, or D, respectively,
according to the treatment of the demand response (DR) scheme.

Group A is the control group,

which is provided information monthly on electricity consumption in the previous period through a
web page but is precluded from any DR scheme.

Group B is the treatment group, which is notified

monthly of information on electricity consumption in the previous period but is precluded from any
DR scheme.

The difference between Groups A and B is whether there is notification of information

about electricity consumption from the previous period. Groups C and D are treated as the same inthe summer-time experiment, that is, the treatment group which is provided information onelectricity consumption in 30 minute-units as a graph on a web page, and subject to two types of DR,
TOU and CPP.
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The definitions and the summary statistics of the variables are shown in Table 2.

***************
Table 2***************

q is the total amount of electricity consumption in the experimental period, which is theobserved data obtained by the experiment.

The other data are obtained by questionnaires. Hfloor isfloor size in the house, Hfamily is the number of people in the family, Hair is the number of air
conditioners in the house, Hrefrig is the number of refrigerators in the house, Hdaytime is the number ofresidents at home in the daytime, and Hage is house age. Htype is the dummy of house type whichtakes a value of one if the house is detached, otherwise zero. Hled is the dummy of LED use whichtakes a value of one if the house uses mainly LED for lighting, otherwise zero. Hfloor and Hage arecategory variables which take higher value when the floor size becomes larger and the house
becomes older, respectively.
The data for internal incentives are also obtained by questionnaires.
consciousness of saving energy,
save energy.

Icon,

Icon is theIatt is intention to renovate the house, and Iwill is willingness toIatt, and Iwill are category variables which take higher value when the

consciousness, the attitude for real action, and the willingness become higher, respectively.
The data for external incentives are obtained from the experiment. EDR is the number ofdays on which the household is under DR (TOU or CPP).
to information feedback.

Enotice and Egraph are the variables related

Enotice is the frequency of the household’s checking the demand

information.

Egraph is the frequency of the household’s checking the graph of electricity

consumption.

EDR and Egraph take an integer value only for Groups C and D, while they take zerofor Groups A and B, since the demand response and the information feedback by graph are
implemented only for Groups C and D. Enotice takes an integer value only for Group B, otherwise
zero.

This means that the base group is Group A, which is provided information in the simplestway.

Hence, Enotice expresses the difference between notification and simple feedback through a

web page, and Egraph expresses the difference of more frequent feedback and visualization by graph.
The variables of PFMself, PFMsavecon, and PFMsaveact are not included in the estimation ofequations (7) and (8) but are used in the analysis after the estimation to see the relationship between
consumption slack and the other evaluation measures of the saving performance of a household.
PFMself is the monthly average of self-evaluation for energy saving performance in a day.
PFMsavecon is the household’s self-recognition of room for saving energy.

PFMsaveact is thehousehold’s self-recognition of the extent to which they act to save energy.

These data are

obtained from the questionnaires implemented after the summer experiment.
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5. Estimation Results
5.1 Results of the Stochastic Frontier Model
The estimation results are shown in Table 3.

***************
Table 3***************The upper part shows the result of equation (7) which has

as a dependent variable.

The lower part shows the result of equation (8) which has electricity consumption slack, s, as adependent variable.

As we stated before, these two equations are simultaneously estimated, and weshow six cases from Case 1 to Case 6 according to the explanatory variables included.
These estimation results seem to be reasonable for several reasons. First, most variables
show the expected signs and they are consistent among different cases.

For example, thecoefficients of Hfamily, Hair, Hrefrig, and Hdaytime are positive, showing that as a family becomes larger
and a household has more air conditioners and refrigerators, more electricity is needed.

We

estimated more cases in addition to those shown in Table 3 in order to check the robustness of themain variables.

Obtained results are similar to the results shown in Table 3.

Second, themagnitudes of coefficients of the variables are also consistent among these cases. Third, the valueof the Wald chi-squares is large enough. This means that the results have significant importance toexplain electricity consumption.

Fourth, we estimated the same models but with different

estimation methods in order to test our data specification (i.e. cross-section data of each householdvs. panel data of each household plus time difference).

That is, we also apply the random effectmodel for the panel data and compared with the results shown in Table 3.

We concluded that theresults achieved by using the cross-section model shown in Table 3 reflect the attitudes and theactions of households more properly than by using the panel data model.

Since H and I are thecross-section variables whose data were obtained by the questionnaires implemented after theexperiment, the time-varying variables are only E and q, whose data had been obtained continuallyduring the experiment.

The combination of these variables in the estimation generates a large

time-gap between real actions and the recognition after the experiment by households, and this leads
to results which cannot be reasonably interpreted.

Hence, we decided to use the data as a crosssection by summing E and q as the total amount in the experimental period.

Thus, we will proceed

to discuss the results in Table 3, focusing mainly on the significant variables.
The upper part showing the result of equation (7) is mostly reasonable.
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The fact that thecoefficients of Hfloor, Hfamily, Hair, Hrefrig, Hdaytime, and Htype are positive indicates that, as thehousehold becomes larger or has more air conditioners or refrigerators, the more people stay at homein the daytime, or the house is detached or has larger floor, more electricity is required to maintainthe household’s lifestyle.

These results are consistent with Thogersen and Gronhoj (2010),

showing that larger households consume more electricity, and Faruqui and George (2005),
suggesting that household characteristics are important in electricity consumption.

In contrast,

since the coefficient of Hage is negative, an older house requires less electricity to be maintained.
This might be because newer houses tend to have more electric facilities and thus require more
electricity. However, Hled is not significant.
Focusing on the lower part (i.e. consumption slack) showing the result of equation (8), theimportant results are the following three: (i) the consciousness of households to save is more
important than external incentives such as demand response and information feedback, (ii) without
the consciousness of households, demand response alone increases consumption slack, and (iii)
demand response can reduce consumption slack when combined with the saving consciousness ofhouseholds.
First, as the coefficient of Icon is negative with statistical significance, the consciousness tosave energy surely reduces consumption slack.

This suggests that the media and advertisements toestablish a social atmosphere conducive to saving electricity might constitute good policy which cantruly influence the individual’s consciousness to save. In fact, in the electricity crisis occurring as aresult of the Great East Japan Earthquake in 2011, the government, through nationwide TVcommercials and various other advertising venues, widely publicized the necessity of saving
electricity, and consumers willingly complied.

Similarly, the willingness to save energy shown as

Iwill tends to reduce consumption slack, but the coefficient is not significant.

This might mean thatthe consciousness for electricity consumption works enough for reducing slack, even when ahousehold has no overt will to save electricity.
Second, the result that the coefficient of Iatt is positive is not consistent with our
expectation.

We expected that households whose attitudes for saving energy are more firmly

established tend to have less consumption slack. The possible reason might be that this variable
does not accurately proxy the household’s attitude for saving electricity.

We assumed that theintention to renovate houses is positively related to this attitude, but our result of Iatt might indicatethat this assumption is not correct.

Thus, apart from the proxy function of the household’s saving

attitude, the coefficient of Iatt can be simply interpreted as the fact that households with the intention
to renovate their houses tend to consume extra electricity.

That might be because households

interested in renovating their houses tend to lean on the facilities rather than on their own efforts forsaving energy and hence their consumption outweighs the saved amount.

However, the minimum

necessary amount of electricity can be reduced by renovation, an issue that needs to be addressed in
13

future studies.
Third, as the coefficient of EDR is positive with statistical significance, the results suggestthat the demand response scheme alone is not effective to bring about electricity saving byhouseholds. Previous studies such as Faruqui and George (2005), Herter et al (2007), and Jessoe
and Rapson (2014) have shown that DR can reduce electricity consumption, a result not consistent
with ours.

However, Herter and Wayland (2010) suggest that DR has the rebound effect of

inducing excess consumption after DR implementation time, during which households have tried toreduce their electricity consumption.

Based on their discussion, our result that DR can increase

consumption slack during the season as a whole seems to be reasonable.
Fourth, as the coefficient of IconEDR is negative with statistical significance, demand
response can reduce consumption slack when combined with Icon.

This might be because therebound effect mentioned above can be weakened by the consciousness of a household. Thus, ourresult suggests the importance of the consciousness of households for saving electricity.

Without

such consciousness, demand response does not work effectively but rather induces the reboundeffect of excess consumption.
The fact that Enotice and Egraph are not significant suggests that the notification of
information about the amount consumed and the visualization of information by 30-minute interval
result in the same effect as the simple monthly information feedback.

5.2 Consumption Slack and the Other Measures for Saving Performance
We analyzed the relationship between consumption slack and the other measures for theevaluation of electricity saving performance by checking the correlations between them.

The resultis shown in Table 4.

***************
Table 4***************In Table 4, s1, s2, s3, s4, s5 and s6 are consumption slack calculated based respectively on
Case 1, 2, 3, 4, 5 and 6 in Table 3. The most important implication is that we should evaluate thehousehold’s saving performance based on consumption slack rather than self-evaluation by thehousehold or the observed amount of total electricity consumption, since these do not fully explain
the reducible consumption.

First, there seems to be no clear correlation between each consumption

slack and PFMself or PFMsaveact. This suggests that, even when households consider that they saveelectricity effectively, from the perspective of consumption slack, there is still much room for moresaving.

Similarly, as the correlation between consumption slack and PFMsavecon is very low, the14

room for saving recognized by households themselves does not necessarily correspond to
consumption slack.

Thus, consultation based on consumption slack objectively calculated might bea good policy for the proper recognition of room for saving by households.

Moreover, therelationship between consumption slack and the total amount of consumed electricity is also
ambiguous, since the correlation is only about 0.378 at most.

Thus, focusing on the observed

amount of total consumption can lead to misinterpretation of the saving performance of households.

6. Conclusions
We investigated the effects of internal and external incentives for households to saveelectricity while considering the difference in the minimum necessary amount of electricity amonghouseholds. Using data of 561 Japanese households in 2012 in the analysis of the stochastic
frontier model, we obtained the following results. First, as the household becomes larger or has
more air conditioners or refrigerators, the more people stay at home in the daytime, or the house isdetached or has larger floor, more electricity is required to maintain the household’s lifestyle. In
contrast, an older house requires less electricity to be maintained.
Second, the consciousness of a household is more important than external incentives suchas demand response and information feedback. Without the saving consciousness of a household,demand response alone increases consumption slack. This might be because households try to
reduce their electricity consumption during DR time but consume more electricity on the reboundafter DR time.

However, demand response can reduce consumption slack when combined with thesaving consciousness of a household.

In contrast, households who are willing to renovate theirhouses tend to consume extra electricity.

This might suggest that households interested inrenovating their houses tend to lean on the facilities rather than on their own efforts to save energy
and hence their consumption outweighs the saved amount.
Third, in evaluating performance in saving electricity, we should observe consumption
slack rather than households’ self-evaluation or the observed total amount of consumption. Even
when households consider they save electricity effectively, from the perspective of consumption
slack, there is still much room for saving.

In addition, focusing on the observed amount of total

consumption can lead to misinterpretation of the saving performance of households.

Acknowledgements
For this study, data were provided by Mitsubishi Heavy Industries, Ltd (MHI). We would like tothank MHI, especially Mr. Tomoyuki Enomoto and Mr. Yasushi Imori, for providing data and initialcomments for our study.
15

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17,(when (I,E)= (I2,E2))(when (I,E)= (I1,E1))(when (I,E)= (I2,E2))∗(when (I,E)= (I1,E1))

∗

∗

∗

Figure 1 Utility Maximization under Internal and External Incentives

18

Table 1 Previous Empirical Studies
Study (Year)
Sample, Data, and Method
FaruquiGeorge(2005)

andHerter et al(2007)

Momani et al.
(2009)

Dianshu et al.
(2010)

Ek
andSoderholm(2010)

HerterandWayland(2010)
Thogersen
and Gronhoj(2010)
Faruqui and
Sergici (2011)

Gronhoj andThogersen(2011)

Karjalainen(2011)

Wang et al.
(2011)

Sample: Households and business
entities in California
Data: Experimental data
Method: OLS
Sample: Households data from 2003 to2004
Data: Experimental data
Method: Descriptive statistics
Sample: Residential and commercial
sectors in Jordan
Data: Survey questionnaire
Method: Descriptive statistics
Sample: 615 Chinese households in 14
cities within Liaoning Province in2008
Data: Survey questionnaire
Method: Descriptive statistics of survey
for households and interviews to thegovernment and relevant officials
Sample: Swedish household
Data: 1200 questionnaires by postal
surveyMethod:Orderedprobitmodel(Dep.Vars.: Laundry service, lightning,heating, hot water use)
Sample: 483 households in California
Data: Experimental data
Method: OLS
Sample: 312 Danish households in 2007
Data: Survey and experiment
Method: Structural model
Sample: Baltimore households in 2008
and 2009
Data: Experimental data
Method: SUR
Sample: 20 Danish households for 5
month
Data: Qualitative interviews to thehouseholds
Method: Descriptive statistics
Sample: 14 households in Finland
Data: Qualitative interviews to thehouseholds
Method: Descriptive statistics
Sample: 816 randomly selectedhouseholds in China
Data: Survey questionnaire
Method: Logit model

19

Results or Determinants of Electricity
SavingDeterminants: Demand response (timeof use (TOU) and critical peak pricing(CPP)), household characteristics,
weather
Determinants: Critical peak pricing,weather, income, automatic facilities tosave electricity at home
Determinants: Daylight saving time
(DST)

Results: 55 % of the sample households
have the intention to save electricity.
Government’s policy needs to bemodified to overcome the distrust inenergy efficiency labels and productstandards.
Determinants: Costs, environmental
attitudes, social interactions, concreteand specific information about
available savings

Determinants: Critical peak pricing

Determinants: Home size, household
size, teenagers, behavior, other’s
behavior, perceived impediments,
self-efficacy
Determinants:
Demandresponse(dynamic peak pricing, peak time
rebate high, peak time rebate low)
Determinants: Detailed feedback about
the electricity consumption

Determinants: Feedback on energy
consumption (especially, presentationsof costs, proportion of consumption by
appliance, and historical comparison)
Determinants: Attitude (environmental
awareness and information), subjective
norms (policy and social norms andsocial interaction), residue effect (pastexperience), perceived behavioral

Ida et(2013)al.

Faruqui et al.
(2014)

JessoeRapson(2014)

andSample: Japanese households
Data: Experimental data and survey
Method: OLS
Sample: Households and small business
entities in Connecticut
Data: Experimental data in 2009
Method: SUR
Sample: Households in Connecticut
Data: Experimental data in 2011
Method: OLS, 2SLS

Table 2 Summary Statistics
Variable
Definitionq
Total amount of electricity
consumption in the experimental
period (kWh)
HfloorFloor size in the house (category
variable)
Hfamily
The number of people in the family
Hair
The number of air conditioners in thehouse
Hrefrig
The number of refrigerators in thehouse
The number of residents at home in
Hdaytime
the daytime
Hage
House age (category variable)
Htype
Dummy of hose type (detachedhouse=1, otherwise=0)
Hled
Dummy of LED (house using mainly
LED=1, otherwise=0)
Icon
Consciousness of saving energy(category variable)
Intention to renovate the house
Iatt(category variable)
Iwill
Willingness to save energy (category
variable)
EDR
The number of days in which thehousehold is under DR (takes aninteger number only for Groups C
and D, otherwise 0)
Enotice
The frequency of checking demand
information (takes an integer
number only for Group B, otherwise
0)20

control (economic benefits, perceived
inconvenience), and demographic
variable
Determinants: Demand response (timeof use (TOU), critical peak pricing(CPP))
Determinants: Demand response (timeof use (TOU), critical peak pricing(CPP), and variable peak pricing(VPP))
Determinants: in-home display (IHD)
and demand responseN
Mean
Std. Dev. MinMax
561 932.866 451.645 107.300 2756.200

561 4.301

1.076

1.000

7.000

561 3.152

1.204

0.000

7.000

561 3.565

1.704

0.000

10.000561 1.130

0.430

0.000

3.000

561 1.326

0.959

0.000

4.000

561 3.178
561 0.754

1.619
0.431

1.000
0.000

6.000
1.000

561 0.109

0.312

0.000

1.000

561 2.863

0.903

1.000

4.000

561 2.800

1.415

1.000

5.000

561 4.301

0.589

2.000

5.000

561 22.620

20.7090.000

46.000561 6.016

17.7520.000

148.000

Egraph

IconEDR
PFMself

PFMsavecon
PFMsaveact

The frequency of checking graph ofelectricity consumption (takes aninteger number only for Groups C
and D, otherwise 0)
Cross term of Icon and EDR
Monthly average of self-evaluationfor electricity saving performance ina day (average of category variable)
Self-recognition of room for savingenergy (category variable)
Self-recognition of actions for savingenergy (category variable)

21561 69.111

111.995

0.000

1354.000

561 64.332
133 2.072

65.463
0.507

0.000
0.222

184.000
3.000

561 1.957

0.487

1.000

3.000

558 3.771

0.730

1.000

5.000

Table 3 Estimation Results
Dependent

Explanatory

Variable

Variablelnq

Case 1
Coef.

Case 2

Case 3

Std. Error

Coef.

Std. Error(0.019)

Coef.

Case 4

Std. Error

Coef.

Case 5

Std. Error

Coef.

Case 6

Std. Error

Coef.

Std. Error

Hfloor

0.032*(0.019)0.031

Hfamily

0.093

***

(0.014)

0.093

***

(0.014)

0.094

***

(0.014)

0.094

***

Hair

0.061

***

(0.013)

0.061

***

(0.013)

0.065

***

(0.012)

0.065

Hrefrig

0.127

***

(0.043)

0.126

***

(0.043)

0.130

***

(0.043)

Hdaytime

0.051

***

(0.017)

0.051

***

(0.017)

0.053

***

Hage

-0.022**(0.011)-0.022**(0.011)-0.020

Htype

0.199

***

(0.048)

0.201

***

(0.048)

0.235

Hled

0.035

(0.053)0.028(0.052)constant

5.136(49.485)

5.193(66.776)

5.212

Icon

-0.052*(0.027)-0.057**(0.027)-0.059

Iatt

0.042

***

(0.012)

0.042

***

(0.012)

0.042

Iwill

-0.037EDR

0.005

Enotice0.001(0.001)0.001(0.001)0.000(0.001)

Egraph0.000(0.000)0.000(0.000)0.000(0.000)IconEDR-0.002constant

0.838

0.032

*

(0.019)0.033*(0.020)(0.014)

0.093

***

(0.014)

0.097

***

(0.014)***(0.012)

0.060

***

(0.013)

0.068

***

(0.013)

0.131

***

(0.043)

0.131

***

(0.043)

0.113

***

(0.044)(0.017)

0.054

***

(0.017)

0.050

***

(0.017)

0.047

***

(0.017)*(0.011)-0.020*(0.011)-0.021**(0.011)-0.014***(0.043)

0.235

***

(0.043)

0.199

***

(0.048)

0.207

0.034

(0.052)0.029(0.053)(54.846)

5.212(73.199)

5.092(158.409)(53.235)

5.174

**

(0.027)-0.055**(0.027)-0.051*(0.027)***(0.012)

0.042

***

(0.012)

0.042

***

(0.012)(0.029)-0.037(0.003)0.004(0.029)*(0.003)-0.0340.004*(0.003)0.004(0.003)0.005*(0.010)***(0.048)(0.029)*(0.003)

0.008

***

(0.002)s*(0.001)-0.002(49.485)

0.648

**

(0.001)-0.002(66.776)0.716*(0.001)-0.002(53.235)

0.878*(0.001)-0.002(54.846)

0.763*(0.001)-0.003(73.199)

0.642

***

(0.001)(158.409)

N (sample size)

561

561

561

561

561

561

Wald chi-square

275.88273.75269.48271.19275.85299.2822

Log likelihood-252.0273-253.48438-254.92415(Note) Significant at 1% (***), 5% (**) and 10% (*).

23

-253.60014-252.65402-260.71515

Table 4 Correlation of Measures for Saving Performance
PFMself

PFMsavecon

PFMsaveactqs1-0.0810.205-0.2840.343

s2-0.0430.224-0.1950.342

s3-0.0390.231-0.1990.378

s4-0.0730.212-0.2800.368

s5-0.0780.206-0.282

0.321

s6

0.005

0.174

-0.224

0.334

N

133

561

558

561

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