Project Center, Graduate School of Economics, Kyoto University, Working Paper No.11-11

Keys to Smart Home Diffusion:
A Stated Preference Analysis of Smart Meters, Photovoltaic
Generation, and Electric/Hybrid Vehicles
Takanori Ida

Graduate School of Economics, Kyoto University,
Yoshida, Sakyoku, Kyoto 606-8501, Japan
E-mail: ida@econ.kyoto-u.ac.jp

Kayo Murakami
Tokyo City University,

3-3-1 Ushikubo-nishi, Tsuzuki-ku, Yokohama, Kanagawa 224-8551, Japan
E-mail: kayo-murakami@eco.mbox.media.kyoto-u.ac.jp

Makoto Tanaka

National Graduate Institute for Policy Studies,

7-22-1 Roppongi, Minato-ku, Tokyo 106-8677, Japan
E-mail: mtanaka@grips.ac.jp

Abstract: As expectations have risen about the deployment of smart grids, it is important
to investigate the diffusion process of smart equipment such as smart meters, photovoltaic

generation, and electric/hybrid vehicles. However, since the revealed preference data havenot been accumulated for smart equipment diffusion, this paper conducts a conjoint
analysis to examine consumers’ stated preferences on the basis of an online survey

administered in March 2011. A mixed logit model that allows for individual heterogeneity
is adopted for estimation, and willingness-to-pay values are calculated for the attributes.

Furthermore, the rates of diffusion, the reduction of greenhouse gas emissions, andinterdependencies among types of smart equipment are investigated.

Keywords: smart grid, smart meter, smart home, electric vehicle, conjoint analysis
JEL Classifications: O33, Q48, Q51

1000Environmental policy, 1700: Natural resource management and policy, 3200:
Econometrics, 5600: Economics

11. Introduction
Awareness of global environmental problems and the requirements for renewable energy

utilization are increasing. In light of the March 11 earthquake and the Fukushima nuclear

crisis, a radical reconsideration of Japanese energy policy is now being discussed. Given

these circumstances, the development of a smart grid in electricity systems has raised hopesof meeting goals for climate change, energy competition, and the safety of systems andtechnology. This leads to many economic questions related to incentive policies for
operators to implement such technology (Clastres, 2011).

Smart grids can be classified into two systems: the upstream power supply system andthe downstream power demand system. This paper deals with the latter, specificallyconcerning residential demand for smart equipment to make up smart homes. In particular,
we investigate the future diffusion of advanced or smart meters (SM) that measure electric

consumption hourly, photovoltaic (PV) generators that are installed on residential rooftops,

clean-fuel electric vehicles (EV), and hybrid electric vehicles (HEV). The implementationof these technologies in Japan is imminent, and policy is needed to prepare for smart home

diffusion. This paper will conduct a conjoint analysis of the future diffusion of SM, PV, EV,
and HEV using the results of an online survey administered in March 2011.

The SM is an advanced electrical meter that records electric consumption in intervals of

an hour or less and relays information to the utility company for monitoring and billing. In
European countries and the USA, where power supply systems can be unstable andelectricity shortages are experienced, governments have been aggressive in introducing SM

infrastructures. However, in Japan, where the power supply used to be secure and stable,

there had been no need to control demand by deploying an hourly metering SM. However,after the March 11 earthquake disaster, power shortages became a matter of concern,

particularly at peak times in summer and winter; thus, the early deployment of SM

infrastructure is now necessary. With advanced metering infrastructure, a local distribution
company (LDC) can implement innovative service options, from resource adequacy
requirements to greenhouse gas emissions reduction, in the following three ways: (1)two-way communication between the LDC and its customers, (2) hourly metering for2

time-varying dynamic pricing and demand management by the LDC and its customers, and(3) real-time billing information for the LDC’s customers (Woo et al., 2008; Duke et al.,2005).

The residential PV system is a method of generating electrical power by converting solar

radiation into electricity via semiconductors. Residential rooftop PV systems may emerge
as a major new market once module prices fall below the critical level. The Japanese
government introduced a net metering system in order to boost residential PV deployment.
Despite the large potential markets, inefficient energy pricing discourages prospective PV
consumers, while environmental externalities and market failures make it difficult for
manufacturers and homebuilders to obtain the benefits of their investment (Keirstead,2007).

An EV uses one or more electric motors for propulsion, while an HEV combines aninternal combustion engine and electric motors. A plug-in hybrid electric vehicle (hereafter,

PHEV) uses batteries that can be recharged via an external electric power source. When an

EV or PHEV is connected at home, it serves as a home battery for charging and discharging,if necessary, and seasonal or daily fluctuations of renewable energy are mitigated.

As the full-scale deployment of smart equipment has not yet been realized, empiricalrevealed preference data have not been sufficiently accumulated. Therefore, we adopt a
stated preference (SP) data method (or conjoint analysis). SP data are hypothetical, and take

into account certain types of market constraints useful for forecasting changes in consumer
behaviors, although these may be affected by the degree of contextual realism of
respondents.

The key study for our purpose is Banfi et al. (2008), which first evaluated consumers’

willingness to pay for energy-saving upgrades to residential buildings, including air
renewal systems and the insulation of windows and facades. The researchers found that

respondents valued the benefits of the energy-saving attributes. However, to the best of ourknowledge, no conjoint analysis of SM and PV diffusion has been conducted.

On the other hand, many studies have conducted conjoint analysis of clean-fuel vehicles.

Bunch (1993) conducted a conjoint analysis to determine how demand for clean-fuelvehicles and their fuels varied as a function of the attributes that distinguished these3

vehicles from conventional gasoline vehicles; clean-fuel vehicles encompassed bothelectric and unspecified liquid and gaseous fuel vehicles. Recently, Karplus (2010) foundthat vehicle cost could be a significant barrier to PHEV entry unless fairly aggressive goals
for reducing battery costs were met. If a low-cost PHEV was available, its adoption had thepotential to reduce greenhouse gas emissions and refined oil demand. Other past studies
that studied clean-fuel or electric vehicles are summarized in Table 1 (cf. Hidrue et al.
2011).

<Table 1>
In this paper, the online conjoint survey was administered in March 2011 (just before theearthquake disaster) to 649 households that planned to purchase or remodel a house within
five years and 694 households that did not plan to do so. We first estimated the SP data byusing a mixed logit model allowing for individual heterogeneity, then investigatedwillingness to pay (WTP) for attributes of SM, PV, EV, and HEV.

This paper contributes to the existing literature in three ways. First, we study SM and PV

diffusion, which has not been fully analyzed but which is now indispensable for smart

homes. Second, we clarify the differences from previous studies of EV diffusion in that we
mainly focus on the advantages of PHEV. Third, in addition to the diffusion analysis, we

examine the reduction of greenhouse gas emissions and the interdependencies among
multiple types of smart home equipment.

We now summarize the main conclusions to be obtained in this paper. First, a decrease in

price level is most effective for the future diffusion of smart equipment such as SM, PV,
EV, and PHEV. On the other hand, greenhouse gas emission reduction effects vary among

them, with PV being the most effective. Second, accompanying the diffusion of smart

equipment, it is expected that greenhouse gas emissions will be substantially reduced.

Assuming the present circumstances, the expected reductions are 4% per household for SM,
7% per household for PV, and 17% per car for PHEV. Taking into account innovations,further reduction of greenhouse gas emissions will be achieved. Third, the purchase of one
form of smart equipment is associated with other smart equipment purchases; in particular,

4the diffusion of PV is promoted the most in this manner.

This paper is organized as follows. Section 2 explains the online survey method of

conjoint analysis and the experimental design. Section 3 describes the mixed logit model
used for estimation. Section 4 discusses the current utilization and future deployment ofsmart equipment. Section 5 displays the estimation results and measures the WTP values of
the attributes. Section 6 extends the analysis to various aspects: the expected diffusion, thereduction rate of greenhouse gas emissions, and the interdependencies among smart
equipment diffusions. Section 7 presents concluding remarks.

2. Survey and design
This section explains the survey method of conjoint analysis and the experimental design.

The survey was conducted online with monitors who were registered with a consumer

investigative company. When conducting the sampling, we considered geographicalcharacteristics, gender, and age to represent an average Japanese population.

Data sampling was performed in two stages. In the first stage, we randomly drew 8,997

households from the monitors in February 2011 and asked basic demographic questions

and whether they planned to purchase or remodel a house within five years. The purpose ofthis question was to classify the respondents according to interest, as the smart equipment
demand is supposed to be closely related to house purchases. A total of 1,630 households(18.1%) planned to purchase or remodel a house within five years, while 7,357 households(81.9%) did not have such a plan.

In the second stage, in March 2011, we surveyed a random sample of 649 households(39.8%) from the 1,630 high-interest households. At the same time, we surveyed a random

sample of 694 households (9.4%) from the 7,357 low-interest households. We conducted
three kinds of conjoint analysis of SM, PV, and EV/HEV for the 649 high-interest

households and 694 low-interest households after asking questions about their current andthe future usages. The respondents received a small remuneration for completing thequestionnaire.

All surveys ended on March 10, 2011, and thus our data are free from the influences of5the earthquake of March 11. After the earthquake, confronted with the Fukushima nuclear

crisis and power shortages, Japanese attitudes toward energy and environmental policychanged drastically. In this sense, we collected ordinary Japanese preferences before thecrisis, and the conclusions obtained may apply to other countries without a special concern.

Next, we explain conjoint analysis. Conjoint analysis considers the attributes of a service

or product. If an excessive number of attributes and levels are included, respondents find it
difficult to answer the questions. On the other hand, if too few are included, the descriptionof alternatives becomes inadequate. Since the number of attributes becomes unwieldy if we
consider all possible combinations, we adopted an orthogonal planning method to avoid

this problem (see Louviere et al., 2000, Ch. 4, for details). We thus obtained an appropriate
number of questions for which the levels of the attributes varied stochastically and asked
the respondents these questions.

First, we explain the setup of the SM conjoint analysis. Advanced metering infrastructure

measures residential or business electricity consumption on a real-time basis and makes it

possible to set dynamic pricing depending on peak and off-peak times. Here, we include thevisualization of electricity consumption and the remote control of the air conditioner in theadvanced-metering or home energy management system (HEMS) service. When a peaktime demand is cut or shifted by introducing SM, greenhouse gas emissions are expected tobe reduced. As such, after conducting several pretests, we determined the alternatives,
attributes, and levels as follows:

Attribute levels of SM conjoint analysis(1) Monthly usage charge

Levels: free, US$2, US$4, US$6 per month(2) Visualization of electricity consumption

Levels: none, WEB display, plus private monitor display, plus energy-saving

advice(3) Off-peak discount (compared with standard electricity charge)
Levels: none, 10%, 30%, 50%(4) Peak surcharge (compared with standard electricity charge)

6

Levels: none, double, four times, six times(5) Remote control of air conditioner during a power shortage
Levels: none, automatic control at 82°F, temporary restriction, usage interception(6) Reduction in greenhouse gas emissions (per household)
Levels: none, 10%, 30%, 50%

Figure 1(a) displays an example of the SM conjoint questionnaire. There are threealternatives: Alternatives 1 and 2 denote different SM deployments and Alternative 3 is no
SM deployment. All respondents were asked the same eight questions.
<Figure 1>
Second, we explain the setup of the PV conjoint analysis. Owners of residential rooftop

PVs decrease electricity bills by cutting electricity consumption and sometimes gain aprofit by selling electricity to an LDC. Currently, the average initial cost to install a PV
system is around US$20,000 in Japan, and a reduction in price is necessary for extensive

diffusion. PV is a renewable energy source that is expected to reduce greenhouse gas

emissions. As such, after conducting several pretests, we determined the alternatives,
attributes, and levels as follows:

Attribute levels of PV conjoint analysis(1) Initial cost of introducing a PV system

Levels: US$10,000, US$15,000, US$20,000, US$25,000(2) Annual reduction in fuel and lighting charges
Levels: 50%, 60%, 70%, 80%(3) Reduction in greenhouse gas emissions (per household)
Levels: 30%, 40%, 50%, 60%(4) Free inspection and maintenance period

Levels: none, 5 years, 10 years, 20 years(5) Stylishly designed PV panel

7

Levels: none, additional
Figure 1(b) displays an example of the PV conjoint questionnaire. There are threealternatives: Alternatives 1 and 2 denote different PV deployments and Alternative 3 is no
PV deployment. All respondents were asked the same eight questions.

Third, we explain the setup of the EV/HEV conjoint analysis. Driving a fuel-efficient

EV or HEV can eliminate the cost of gasoline or other fuel, and pollution is much lower
compared with gasoline vehicles. On the other hand, the price is relatively high compared
to standard gasoline vehicles (at present, an additional US$5,000–10,000 for EV and afurther US$2,000–3,000 for HEV), the driving range on a full battery is still very limited(100–150 km per charge for EV), and it can take time to find a charging station (up to 30

min). Plug-in is now optional for HEV, and PHEV can serve as a home battery for charging
or discharging if necessary. As such, after conducting several pretests, we determined thealternatives, attributes, and levels as follows:
Attribute levels of EV conjoint analysis(1) EV Premium (relative to the same class gasoline vehicle)
Levels: none, US$3,000, US$5,000, US$10,000(2) Annual reduction in fuel cost necessary for the same distance covered (relative tothe same class gasoline vehicle)
Levels: 60%, 70%, 80%, 90%(3) Driving range on a full battery (km)Levels: 100 km, 200 km, 300 km, 400 km(4) Reduction in greenhouse gas emissions (relative to the same class gasoline vehicle)
Levels: 70%, 80%, 90%, 100%

(5) Time to find a charging station (min.)Levels: up to 10 min., up to 30 min.

Attribute levels of HEV conjoint analysis(1) HEV Premium (relative to the same class gasoline vehicle)

8

Levels: none, US$1,000, US$3,000, US$5,000(2) Annual reduction in fuel cost necessary for the same the distance covered (relative
to the same class gasoline vehicle)
Levels: 20%, 40%, 60%, 80%(3) Driving range before refueling (km)Levels: 700 km, 1,000 km, 1,500 km, 2,000 km(4) Reduction in greenhouse gas emissions (relative to the same class gasoline vehicle)
Levels: 40%, 50%, 60%, 70%(5) Home plug-in

Levels: none, additional

Figure 1(c) displays an example of the EV/HEV conjoint questionnaire. There are threealternatives, where Alternative 1 denotes EV, Alternative 2 denotes HEV, and Alternative 3

is gasoline vehicle purchases. There are sixteen questions in total and they are divided into
two versions. All respondents are asked either version (consisting of eight questions) at
random.

3. Model specification
This section describes the estimation model. Conditional logit (CL) models, whichassume independent and identical distribution (IID) of random terms, have been widelyused in past studies. However, independence from the irrelevant alternatives (IIA) property
derived from the IID assumption of the CL model is too strict to allow flexible substitution
patterns. A nested logit (NL) model partitions the choice set and allows alternatives to havecommon unobserved components compared with non-nested alternatives by partiallyrelaxing strong IID assumptions. However, the NL model is not suited for our analysis
because it cannot deal with the distribution of parameters at the individual level (Ben-Akiva
et al., 2001). Consequently, the best model for this study is a mixed logit (ML) model,
which accommodates differences in the variance of random components (unobserved
9heterogeneity). This model is flexible enough to overcome the limitations of CL models by
allowing for random taste variation, unrestricted substitution patterns, and the correlation of
random terms over time (McFadden and Train, 2000).
Assuming that parameter β n is distributed with density function f ( β n ) (Train 2003,
Louviere et al., 2000), the ML specification allows for repeated choices by each sampleddecision maker in such a way that the coefficients vary by person but are constant over each
person’s choice situation. The logit probability of decision maker n choosing alternative i inchoice situation t is expressed as
Lnit ( β n ) = ∏ t =1[exp(Vnit ( β n )) / ∑ j =1 exp(Vnjt ( β n ))] ,
T

J

(1)

which is the product of normal logit formulas, given parameter β n , the observable portionof utility function Vnit , and alternatives j=1, …, J in choice situations t=1, …, T. Therefore,
ML choice probability is a weighted average of logit probability Lnit ( β n ) evaluated atparameter β n with density function f ( β n ) , which can be written as
Pnit = ∫ Lnit ( β n ) f ( β n )d β n .

(2)Accordingly, we can demonstrate variety in the parameters at the individual level using
the maximum simulated likelihood (MSL) method for estimation with a set of 100 Haltondraws 1. Furthermore, since each respondent completes eight questions in the conjoint
analysis, the data form a panel, and we can apply a standard random effect estimation.
In the linear-in-parameter form, the utility function can be written as
U nit = γ ' xnit + β n ' znit + ε nit ,1(3)

Louviere et al. (2000, p. 201) suggested that 100 replications are normally sufficient for atypical problem involving five alternatives, 1,000 observations, and up to 10 attributes (alsosee Revelt and Train, 1998). The adoption of the Halton sequence draw is an important

issue (Halton, 1960). Bhat (2001) found that 100 Halton sequence draws are more efficientthan 1,000 random draws for simulating an ML model.
10where xnit and znit denote observable variables, γ denotes a fixed parameter vector,

β n denotes a random parameter vector, and ε nit denotes an independently and identically
distributed extreme value (IIDEV) term.
Because ML choice probability is not expressed in closed form, simulations must be
performed for the ML model estimation. Let θ denote the mean and (co-)variance ofparameter density function f ( β n | θ ) . ML choice probability is approximated through thesimulation method (see Train, 2003 p. 148 for details). We can also calculate the estimatorof the conditional mean of random parameter s conditional on individual specific choiceprofile yn (see Revelt and Train, 1998 for details), given ash( β | yn ) = [P( yn | β ) f ( β )] / ∫ P( yn | β ) f ( β ) d β .

(4)From Eq. (4) h( β | yn ) , the conditional choice probabilities P̂nit ( β n ) can be calculated

individually:P̂nit ( β n ) = exp(Vnit (h( β | yn )) / ∑ j=1 exp(Vnjt (h( β | yn ))) .
J(5)

After conducting three kinds of conjoint analysis, we expect that a person who has ahigher preference for energy conservation is more likely to implement various types ofsmart equipment, including SM, PV, EV, and HEV. As such, this conjunction leads to a
positive interdependency among those choice probabilities. Letting the number of conjoint
analysis be m = 1,2, 3 , given that the conditional choice probability P̂nitM =m is influenced
by the other conditional choice probabilities P̂nitM ≠m , the random utility function forchoosing m can be written asm
U nit= γ ' xnit + β n ' z nit + γ n ' P̂nitM ≠m ( β n ) + ε nit .

(6)

At this point, parameter γ n indicates interdependencies among the smart equipment

implementations. We will analyze these interdependencies with the utility function shownin Eq. (6) in Section 6.

4.

Data description

11This section discusses the data used for the estimation. Table 2 carries the demographic

characteristics of the respondent households; the highly interested households that plan topurchase or remodel a house are shown in the left column, households with little interest
are in the center, and the weighted average is shown in the right column. We presently
comment on the remarkable difference between the high-interest and the low-interest

households.

<Table 2>
No remarkable differences are observed between households with high interest and thosewith little interest in respect to residence information such as owned/rented anddetached/apartment. There is also little difference among individual characteristics andannual electricity expenses. On the other hand, those who are married, employed, and havemore household income appear more often in the highly interested group. Theseobservations are all intuitive.

4.1 Utilization of smart meter
Table 3(a) shows the SM utilization. We comment on the remarkable differences that are

observed between the highly interested and the little interested households.
<Table 3>

The overall SM utilization rate is only 1% at present, and the degree of recognition ishigher for the highly interested. The future utilization rate is only a few percent and ishigher for highly interested households. If various values are added to advanced meteringor HEMS, the future utilization will increase.

At this point, we asked the respondents to rate how much they emphasized added value,

using a five-point scale (from very important to not important at all). The values in Table
3(a) show the sum of the ratio replying “very important” to those replying “quite12

important.”

The value of a WEB display is around 45% for the weighted average, and is 10% higher

for the highly interested group. The value of a private monitor display is around 45%, andis 10% higher for the highly interested. The value of energy-saving advice device, anon-peak surcharge, and an off-peak surcharge is around 50% for each, and is, respectively,

15%, 10%, and 10% higher for the highly interested. The values of automatic air
conditioner control at 82  F and of temporary restriction remain only 25%, while that ofusage interception remains 20%, and each value is 10% higher for the highly interested. In
addition, the value of a reduction in greenhouse gas emissions is around 40%, and it is 15%
higher for the highly interested.

Finally, when we asked the respondent households how much they wanted to pay

monthly for overall service, 40% stated that they wanted to pay nothing, while some werewilling to pay US$3 per month. The ratio of fee-payers is 10% higher for the highlyinterested, and some of them actually replied that they would be willing to pay around
US$5.

4.2 Utilization of residential photovoltaic generation
Table 3(b) shows the PV utilization. The overall PV utilization rate is only a few percent

at present, and the degree of recognition is higher for the highly interested. The future
utilization rate moves up to 10% for the weighted average, and is higher for the highlyinterested. If such varying values are added to the PV, the future utilization will increase.

The values in Table 3(b) show the sum of the ratio of those replying “very important” tothose replying “quite important.” The average value of reduction in fuel and lighting

charges is around 70% on average, and is 10% higher for the highly interested. The valueof selling surplus electricity is also around 70%, and is 5% higher for the highly interested.

The value of reduction in greenhouse gas emissions is around 50%, and is 15% higher forthe highly interested. Furthermore, 70% of respondents attach importance to free inspection
and maintenance periods, while 45% deem a stylishly designed PV panel as important.

Finally, when we asked the respondent households how much they wanted to pay for13

deploying a residential PV system, 25% replied that they would like to pay nothing, whilesome were willing to pay US$10,000. The ratio of fee-payers is 10% higher for the highlyinterested, and some of them replied that they would be willing to pay around US$15,000.
4.3 Utilization of electric and hybrid electric vehicles
Table 3(c) shows the EV/HEV utilization. The overall EV utilization rate is less than 1%

at present, and the degree of recognition is higher for the highly interested. The future

utilization rate rises to 10%, and is higher for the highly interested. On the other hand, theoverall HEV utilization rate is around several percent at present, and the degree ofrecognition is higher for the highly interested. The future utilization rate increases to a few
tens of percent, and is much higher for the highly interested.

The average values in Table 3(c) show the sum of the ratio of those replying “veryimportant” to those replying “quite important.” The value of annual reduction in fuel cost isaround 75% on average, and is 10% higher for the highly interested. The value of driving

range on a single recharge or refueling is around 70%, and is 10% higher for the highlyinterested. The value of reduction in greenhouse gas emissions is around 50%, and is 10%

higher for the highly interested. Furthermore, 65% of the respondents attach importance toa home plug-in option.

5.

Estimation results and analysis
This section displays and discusses in order the estimation results of SM, PV, and

EV/HEV for both the highly interested households and those with little interest. The
number of observations is 5,192 (649 respondents × 8 questions) for the former and 5,552(694 respondents × 8 questions) for the latter.
5.1 Estimation results for a smart meter
Table 4(a) shows the estimation results of the SM conjoint analysis, where the left

14

column displays results for the highly interested and the right column those for the little

interested. The McFadden R2 values are 0.2167 for the former and 0.3148 for the latter,both of which are sufficiently high for a discrete choice model. We assume that, except fora monthly usage charge, which is set as a numeraire, the parameters are distributed

normally, and the mean and standard deviation values are reported. (Note that *** denotes
1% significance; **, 5% significance; and *, 10% significance.)<Table 4(a)>First, for the highly interested, the statistically significant estimates (mean) are formonthly usage charge (-), private monitor display (+), energy-saving advice (+), off-peakdiscount (+), peak surcharge (-), automatic control at 82  F (-), and reduction in greenhousegas emissions (+). Note that the symbols in the parentheses are the signs for each estimate.
The statistically significant estimates (standard deviation) are WEB display, private

monitor display, off-peak discount, peak surcharge, usage interception, and reduction ingreenhouse gas emissions.

Next, for those with little interest, the statistically significant estimates (mean) aremonthly usage charge (-), private monitor display (+), energy-saving advice (+), off-peakdiscount (+), peak surcharge (-), automatic control at 82  F (-), and usage interception (-).
The statistically significant estimates (standard deviation) are WEB display, private

monitor display, off-peak discount, peak surcharge, and reduction in greenhouse gas
emissions.

It follows from the foregoing that the results for the highly interested and those with little

interest are similar overall; however, the highly interested have a statistically significant
preference for a reduction in greenhouse gas emissions, while the groups with little interest
do not.

The WTP values are derived from subtracting the parameter of the attribute divided by

the numeraire. The WTP values are summarized in Figure 2(a) for the statisticallysignificant attributes. The WTP values are almost the same for the two groups. Note thatthe average values are close to those for the group with little interest, since the ratio for this15

group accounts for around 80%.
<Figure 2>
At this point, the average WTP values show the positively evaluated items to be a private

monitor display (US$0.7/month), energy-saving advice (US$1.1/month), off-peak discount(10%) (US$0.2/month), and reduction in greenhouse gas emissions (10%) (US$0.1/month).

On the other hand, items negatively evaluated are peak surcharge (double)(US$-0.6/month), automatic control at 82  F (US$-0.4/month), and usage interception(US$-0.5/month).

5.2 Estimation results of photovoltaic generation
Table 4(b) lists the estimation results of the PV conjoint analysis. The McFadden R2

values are 0.4214 for the highly interested and 0.5305 for those with little interest, both ofwhich are very high for a discrete choice model.

First, for the highly interested, the statistically significant estimates (mean) are initialcost (-), annual reduction in fuel and lighting charges (+), reduction in greenhouse gas

emissions (+), and free inspection and maintenance period (+). The statistically significant

estimates (standard deviation) are an annual reduction in fuel and lighting charges,

reduction in greenhouse gas emissions, and free inspection and maintenance period.

Next, for the group with little interest, the statistically significant estimates (mean) are

initial cost (-), annual reduction in fuel and lighting charges (+), reduction in greenhousegas emissions (+), free inspection and maintenance period (+), and stylishly designed PV

panel (+). The statistically significant estimates (standard deviation) are an annual

reduction in fuel and lighting charges, reduction in greenhouse gas emissions, and freeinspection and maintenance period.

The WTP values are summarized in Figure 2(b) for the statistically significant attributes.

The two groups show significantly different WTP values. The highly interested group iswilling to pay an additional US$1,500 for the initial cost of deploying a PV, and the group16

with little interest an additional US$600 to take advantage of a 10% reduction in fuel andlighting charges. That is to say, the highly interested group is relatively sensitive to a

reduction in fuel and lighting charges, while the latter group is relatively sensitive to a

reduction in initial costs. Note that it is this latter group alone that is willing to pay
US$1,000 for a stylishly designed PV panel.

Average WTP values in the all households show the positively evaluated items to be anannual reduction in fuel and lighting charges (10%) (US$750), a reduction in greenhousegas emissions (10%) (US$1,400), free inspection and maintenance period (10 years)(US$300), and a stylishly designed PV panel (US$800).
5.3 Estimation results of electric/hybrid vehicle
Table 4(c) lists the estimation results of the EV/HEV conjoint analysis. The McFadden

R2 values are 0.3107 for the highly interested and 0.3625 for those with little interest, bothof which are sufficiently high for a discrete choice model.

First, for the highly interested, the statistically significant estimates (mean) are premium(-) for an EV/HEV, annual reduction in fuel costs (+), and driving range on a full battery(+) for an EV, as well as annual reduction in fuel costs (+), driving range upon refueling (+),
and home plug-in (+) for an HEV. The statistically significant estimates (standard
deviation) include all parameters for both EV and HEV.

Next, for those with little interest, the statistically significant estimates (mean) arepremium (-) for an EV/HEV, annual reduction in fuel costs (+),driving range on a fullbattery (+), and reduction in greenhouse gas emissions (-) for an EV; as well as annual
reduction in fuel costs (+), driving range upon refueling (+), and home plug-in (+) for an

HEV. The statistically significant estimates (standard deviation) include all parameters forboth an EV and HEV, except for a home plug-in for an HEV2.
2

It is counterintuitive that a reduction in greenhouse gas emissions for an EV has anegative sign. One reason for this may be that an EV is a cleaner fuel type vehicle than an
HEV, but few respondents chose an EV as an alternative. Consequently, the reduction in
17

Figures 2(c) and 2(d) summarize the WTP values for the statistically significant

attributes. Average WTP values indicate that the items positively evaluated are annual

reduction in fuel costs (10%) (US$240) and driving range on a full battery (100 km)(US$200) for an EV3, as well as annual reduction in fuel costs (10%) (US$150), driving

range upon refueling (100 km) (US$90), and home plug-in (US$770) for an HEV.

6.

Discussions and implications
This section discusses the elements of smart-equipment deployment. We begin bycalculating the diffusion rates for four different scenarios, and then calculate the reductions

in greenhouse gas emissions on the basis of the diffusion rates. We finally investigate theinterdependencies of smart-equipment deployments.
6.1 Analysis of diffusion rates
We assume two levels for two key attributes (price and greenhouse gas emission

reductions) and then calculate the diffusion rates for an SM, PV, EV, and HEV.

We first suppose existing standard prices and estimated reductions of greenhouse gas

emissions, which were calculated from the available data, to be the default values (see the
APPENDIX for details).

Estimated reduction rates of greenhouse gas emissions:

Visualization with a smart meter: minus 1.1% per household
Peak surcharge (triple): minus 3.7% per household
PV deployment: minus 38.2% per household
EV deployment: minus 83.5% per car

greenhouse gas emissions of an EV is not associated with a gain in utility.
3

We do not enter into a detailed discussion regarding the unexpected result of emission

reduction. See Note 2.

18




HEV deployment: minus 52.4% per car

PHEV deployment: minus 64.8% per car

In the following analysis, which is based on a discussion with experts, we also determine

the targeted ranges of price and emission reductions for a 5-year period.
6.1.1 Diffusion rates of a smart meterWe determine the attribute levels for calculating the SM diffusion rates as follows:Monthly usage charge: nothing or US$3
Visualization and energy-saving advice
Off-peak discount: 50% discountPeak surcharge: triple

Automatic air conditioner control at 82  F

Reduction in greenhouse gas emissions: 10% or 30%

We calculated the SM diffusion rates for the highly interested group, those with little

interest, and the weighted average of the two groups. The results are listed in Table 5(a).

Scenario 1 denotes a combination of [monthly usage charge, reduction in greenhouse gas
emissions] = [US$3, 10%]; Scenario 2, [US$0, 10%]; Scenario 3, [US$3, 30%]; andScenario 4, [US$0, 30%]. Therefore, for consumers, Scenario 1 is the worst and Scenario 4is the best.

In Scenario 1, the diffusion rates are 61.4% for the highly interested, 36.0% for thosewith little interest, and 40.6% for the average between the two. On the other hand, in
Scenario 4, the diffusion rates are 87.8% for the highly interested, 73.4% for the little
interested, and 76.0% for the average. Note again that the average figure is closer to that for
the group with little interest, as this group accounts for around 80% of the respondents.

Furthermore, comparisons of Scenarios 1 and 2, and Scenarios 3 and 4 reveal thedifferences to be more than 30% for the average. These become much larger for those with19

little interest, as the latter are so sensitive to price that they are willing to introduce smart

meters for free. On the other hand, few differences are observed when comparing Scenarios

1 and 3, and Scenarios 2 and 4. This is because the highly interested have a very small,though statistically significant, preference for a reduction in greenhouse gas emissions,while the group with little interest does not have a statistically significant preference. In
short, the free charge policy is very effective, but the incentive of a decrease in greenhousegas emissions is less effective for the spreading of a smart meter infrastructure.
<Table 5>6.1.2 Diffusion rates of residential photovoltaic generation
We determine the attribute levels for calculating the PV diffusion rates as follows:






Initial cost: US$15,000 or US$20,000

Annual reduction in fuel and lighting charges: 60%

Reduction in greenhouse gas emissions: 40% or 60%
Free inspection and maintenance period: 10 years
Stylishly designed PV panel

The PV diffusion rates are listed in Table 5(b). Scenario 1 denotes a combination of[initial cost, reduction in greenhouse gas emissions] = [US$20,000, 40%]; Scenario 2,[US$15,000, 40%]; Scenario 3, [US$20,000, 60%]; and Scenario 4, [US$15,000, 60%].

In Scenario 1, the diffusion rates are 48.2% for the highly interested, 9.8% for those little

interested, and 16.8% for the average between them. On the other hand, in Scenario 4, thediffusion rates are 86.6% for the highly interested, 52.8% for the group with little interest,
and 58.9% for the average. As expected, the gaps between the two groups are very large.

Furthermore, comparisons of Scenarios 1 and 2, and Scenarios 3 and 4 reveal the average

differences between the two groups to be around 20 to 30%. This means that the current

initial cost is around US$20,000, but this must be decreased to US$15,000 to encourage

20

further PV diffusion. On the other hand, comparisons of Scenarios 1, and 3 and Scenarios 2

and 4 highlight gaps of 10 to 20%. We, therefore, see that a reduction in greenhouse gas
emissions works as a strong motivation for PV deployment, which is very unlike the caseof a smart meter. The reason the effects of emission reduction incentives vary depending on

appliances is ambiguous. One possible answer is that consumers care about the process of

reducing greenhouse gas emissions, but the process of reducing emissions by means of a

PV is more explicit than that of an SM. Accordingly, a reduction in greenhouse gas
emissions is effective in PV deployment.

The effects of emission reduction incentives on diffusion rates might depend on the

consumer “literacy” regarding smart equipment. Note that PVs for households have beensold in Japan for more than a decade. The initial cost of introducing a PV into a household

is considerable (up to $20,000). Thus, PV manufacturers and sellers in Japan have been
making every effort to advertise several appealing aspects of PV, specifically its powerfulability to reduce greenhouse gas emissions, in addition to its helping reduce annual fuel andlighting charges. As a result, Japanese consumers have become increasingly aware of theability of PV to reduce greenhouse gas emissions. In contrast, SM and PHEV have not evenbeen sold in Japan as yet. The effects of emission reduction incentives on diffusion rates

may increase if there is an increase in consumer “literacy” regarding types of smart
equipment, such as PHEV, after these are introduced into the market in the near future.
6.1.3 Diffusion rates of electric and plug-in hybrid electric vehicles
We determine the attribute levels for calculating the EV/PHEV diffusion rates asfollows:EV

EV premium: US$5,000 or US$10,000
Annual reduction in fuel cost: 80%

Driving range on a full battery: 200 km

Reduction in greenhouse gas emissions: 80% or 100％21Time to find a charging station: within 10 min.

PHEV



HEV premium: US$2,500 or US$5,000
Annual reduction in fuel costs: 60%

Driving range on a full battery: 1,500 km

Reduction in greenhouse gas emissions: 60% or 80％

Home plug-in

Tables 5(c) and (d) list the EV and PHEV diffusion rates, respectively. For an EV,

Scenario 1 denotes a combination of [EV premium, reduction in greenhouse gas emission]= [US$10,000, 80%]; Scenario 2, [US$5,000, 80%]; Scenario 3, [US$10,000, 100%]; andScenario 4, [US$5,000, 100%]. On the other hand, for PHEV, Scenario 1 denotes a

combination of [PHEV premium, reduction in greenhouse gas emission] = [US$5,000,
60%]; Scenario 2, [US$2,500, 60%]; Scenario 3, [US$5,000, 80%]; and Scenario 4,[US$2,500, 80%].

In Scenario 1, the EV diffusion rates are 1.0% for the highly interested, 0.2% for thosewith little interest, and 0.3% for the average between the two groups; the PHEV diffusion

rates are 61.1% for the highly interested, 21.5% for those with little interest, and 28.7% for

the average. On the other hand, in Scenario 4, the EV diffusion rates are 5.1% for thehighly interested, 1.6% for those with little interest, and 2.2% for the average; while the
PHEV diffusion rates are 81.4% for the highly interested, 51.4% for the little interested,
and 56.8% for the average. It is important to note that the gaps between the EV and the

PHEV diffusion rates are extremely large. One possible reason for the significantly low
rates of EV’s diffusion might be its limited driving range. The EV’s driving range on a fullbattery is currently much lower than the driving range of a standard gasoline engine car,whereas the EV premium is quite high. Technological innovations that decrease production
costs and enable a much longer driving range would enhance the future diffusion of EVs.

Furthermore, comparisons of Scenarios 1 and 2, and Scenarios 3 and 4 show that even if

the EV premium decreases from US$10,000 to US$5,000, the EV diffusion rates do not22drastically increase. On the other hand, if the PHEV premium decreases from US$5,000 to
US$2,500, the PHEV diffusion rates double. We might say that a premium approachingless than US$5,000 is necessary for the full-scale deployment of electric vehicles(especially PHEV).

On the other hand, as with the diffusion of smart meters, a reduction in greenhouse gas

emissions does not have an influence on EV deployment, even though we may expect alarge reduction in greenhouse gas emissions by driving EVs. Since the clean efficiency of a
PHEV now compares favorably with that of an EV, however, the difference in the emission

reduction is neither distinct nor critical for the respondents. There has, as yet, been no

full-scale sale of EV and PHEV. As discussed before, the effects of emission reduction
incentives on diffusion rates could increase if there is an increase in consumer “literacy”
regarding those vehicles in the near future.

6.2 Analysis of reduction in greenhouse gas emission
Here, we analyze the social reductions in greenhouse gas emissions that are calculated

from the diffusion rates discussed in Section 6.1. The social reductions are derived asfollows.
The highly interested reduction rate of greenhouse gas emission = highly interested
diffusion rate × estimated reduction rates

The little interested reduction rate of greenhouse gas emission = little interested
diffusion rate × estimated reduction rates

The expected social reduction rate of greenhouse gas emission = 0.181 × highlyinterested reduction rate + 0.819 × little interested reduction rate

Table 6 lists the expected social reduction rates of greenhouse gas emissions for smart

equipment. The definitions of the scenarios shown were given in the previous section.
<Table 6>23

First, for the SM social reduction rate (per household), the values resulting from Scenario

1 (the worst) are 6.1% for the highly interested, 3.6% for those with little interest, and 4.1%
for the average between the two groups. On the other hand, the values resulting from

Scenario 4 (the best) are 26.3% for the highly interested, 22.0% for the little interested, and22.8% for the average.

Second, for the PV social reduction rate (per household), the values resulting from

Scenario 1 are 19.3% for the highly interested, 3.9% for those with little interest, and 6.7%

for the average. On the other hand, the values resulting from Scenario 4 are 52.0% for thehighly interested, 31.7% for the little interested, and 35.4% for the average.

Third, for the EV social reduction rate (per car), the values resulting from Scenario 1 are

0.8% for the highly interested, 0.2% for the little interested, and 0.3% for the average. On

the other hand, the values resulting from Scenario 4 are 4.1% for the highly interested,
1.3% for the little interested, and 1.8% for the average.

Fourth, for the PHEV social reduction rate (per car), the values resulting from Scenario 1

are 36.7% for the highly interested, 12.9% for the little interested, and 17.2% for the
average. On the other hand, the values resulting from Scenario 4 are 65.1% for the highlyinterested, 41.1% for the little interested, and 45.5% for the average.

To summarize, by the diffusion of smart equipment, a social reduction in greenhouse gas

emissions is extensively advanced. Assuming the present standard scenario, the reductions
are estimated to be 4% for SM, 7% for PV, and 17% for PHEV. If we add someinnovations to the equipment, larger reductions would be expected.

The social reductions in greenhouse gas emissions depend on the balance of the diffusion

rate of smart equipment and the individual reduction rate of greenhouse gas emissions. SM

has a much lower individual reduction rate of greenhouse gas emissions than PV, whereas
the diffusion rate of SM is higher than that of PV. Consequently, the difference between

SM and PV in terms of the social reductions is not so large. The EV’s social reductions are
significantly small because its diffusion rate is extremely low. In contrast, PHEV’s

diffusion rate and individual reduction rate of greenhouse gas emissions are somewhatbalanced, which results in a relatively great amount of social reductions for PHEV.

24

6.3 Analysis of interdependencies among smart equipment diffusion
We have thus far analyzed three different conjoint studies separately. However, there areinterdependencies among consumer preferences for smart equipment used in a smart home.
For example, a consumer who is interested in a PHEV is likely to receive a time-of-use

electricity price by using an SM, whereas a household that installs a residential PV may
consider a PHEV as a convenient home battery.

We try to ascertain these interdependencies by inserting other choice probabilities

depicted in Eq. (6) into the estimation equation of certain smart equipment as explanatory
variables. Table 7 lists the main estimation results (selectively, the choice probability

parameters) for those with high and little interest, respectively. The estimates aretransformed into elasticities, which indicate how much a percentage increase in a choiceprobability increases another choice probability. Note that the values in the parentheses aret values: *** denotes 1% significant; **, 5% significant; and *, 10% significant. We
adopted an orthogonal planning method in establishing the questionnaire, and therefore, thecorrelation is eliminated among explanatory variables. Thus, the introduction of expectedchoice probabilities has almost no influence on the estimates of the conjoint attributes, andwe have verified that the estimation results remain very robust.
<Table 7>
First, for the SM estimation results (choice probability elasticities), the statisticallysignificant items are PV (0.319**) and PHEV (0.863***) for the highly interested, and
PHEV (0.892***) for the little interested. We can therefore see that PHEVs will be a
driving force for the choice of an SM. Note, however, that here we show not causality but

correlation. Several interpretations are allowed. For example, with PHEV diffusion, theeconomic value of introducing an SM increases; or, only households that have alreadyinstalled an SM are willing to purchase a PHEV.

Next, for the PV estimation results, the statistically significant items are SM (1.748***);

25

EV (0.773***); and PHEV (0.933***) for the highly interested, and SM (1.162***), EV(0.395***), and PHEV (1.352***) for those with little interest. All smart equipment
influences PV adoption, and the effects of SM and PHEV are sometimes elastic. These

results may reflect the fact that early adopters who deploy residential PV systems have highenvironmental consciousness. PV is also considered a hopeful renewable energy source andexplicitly contributes to the reduction in greenhouse gas emissions.

Finally, for the EV estimation results, the statistically significant items are SM(0.365***) and PV (0.698***) for the highly interested, and SM (0.378***) and PV(0.491***) for the group with little interest. On the other hand, for the PHEV estimation
results, the statistically significant items are SM (0.251***) and PV (0.286***) for thosewith high interest, and SM (0.162***) and PV (0.273***) for those with little interest. We
see that the preferences for an EV/PHEV are associated with the other smart equipment

deployments. This is partially because an EV/PHEV serves as a home battery, which
alleviates the problem of an unstable power supply associated with a renewable energy

source.

To summarize, the choice behaviors for smart equipment are not independent and they

reinforce the purchases of other smart equipment. Therefore, an incentive policy thatinduces consumers to purchase smart equipment simultaneously should be considered.

7.

Concluding remarks
This paper conducted three kinds of conjoint analysis studies using a mixed logit modelon the basis of an online survey carried out in March of 2011. First, we examined the WTP

values for the attributes of smart equipment. Furthermore, we investigated the diffusion

rates, the reduction in greenhouse gas emissions, and interdependencies among the smart
equipment deployments. We obtained the following conclusions. First, a decrease in priceis the most effective promotion for smart equipment deployments. On the other hand, theeffects varied across the different types of equipment for the reduction in greenhouse gas

emissions, although it was highest for PV deployment. The effects of emission reduction
incentives may also depend on the consumer “literacy” regarding smart equipment. Second,26

with smart equipment diffusion, greenhouse gas emissions would be reduced in society.
According to the standard scenario, the reduction rates were 4% for SM, 7% for PV, and

17% for PHEV. Innovation will enlarge these reductions in the future. Third,

interdependencies among the different types of smart equipment deployments wereobserved, and PV deployment is particularly associated with all other equipment

deployments. As a final remark, we acknowledge that all of these results are based on adata analysis of stated preference, which must be reconfirmed using a revealed preference

data analysis in the future. The results of such an analysis remain an important question for
the future.

27

Appendix
In Japan, an average household emits 3216kg of CO2 annually by consuming electricity(4500kWh) and gas (600m3) (Central Research Institute of Electric Power Industry:
CRIEPI, Ministry of Internal Affairs and Communications: MIC). The CO2 emission ratesof the electric power companies when generating power are 0.44kg-CO2/kWh in thedaytime, 0.39kg-CO2/kWh in the nighttime, and 0.42kg-CO2/kWh on average (CRIEPI,Minister of Economy, Trade and Industry: METI, Ministry of Environment: MOE).


Visualization with a smart meter
Introduction of a smart meter and private monitor display reportedly reduced theelectricity use of an average household by 1.8% (New Energy and Industrial Technology
Development Organization: NEDO, Mitsubishi Research Institute: MRI). This corresponds
to an annual reduction in electricity usage by 81kWh and in CO2 emissions by 34kg-CO2
per household, when applying the average CO2 emission rate. Thus, the reduction rate of
CO2 is 1.1％ (34/3216kg-CO2) per household.


Peak surcharge
Faruqui et al. (2010) reported that the price elasticity of electricity demand ranges from

0.073 to 0.13 by surveying the existing demand response (DR) programs in U.S. We here
assume that the price elasticity of electricity demand is 0.1. When the electricity tariff
during the peak period is tripled, a household will reduce its electricity usage by 270kWh
per year (in relation to the annual electricity usage in the peak period defined in the TOU
tariff, i.e., 1350kWh; METI). This amounts to an annual reduction in CO2 emissions of
119kg-CO2, when applying the CO2 emission rate in the daytime. Thus, the reduction rateof CO2 is 3.7％ (119/3216kg-CO2) per household.


PV deployment
The power output of home solar PV is 3kW on average, which generates 3000kWh ofelectricity annually (National Institute of Advanced Industrial Science and Technology:

28

AIST). This leads to an annual reduction in CO2 emissions by 1230kg-CO2, when applying
the CO2 emission rate in the daytime (CO2 emissions caused in the process of producing
PV panels is deducted, i.e., 0.44kg-CO2/kWh − 0.03kg-CO2/kWh). Thus, the reduction
rate of CO2 is 38.2％ (1230/3216kg-CO2) per household.
In Japan, the average annual travel distance of a gasoline engine car is 9188km (Ministryof Land, Infrastructure, Transport, and Tourism: MLIT). The annual CO2 emissions
amount to 2345kg-CO2 per car, based on the fuel efficiency of 9.1km/L and CO2 emission
rate of 2.32 kg-CO2/L (MLIT).
EV deployment

The electric efficiency of EV is estimated to be 10.0km/kWh (METI). Based on thisestimate, the annual reduction in CO2 emissions is 1959kg-CO2 per car. Thus, thereduction rate of CO2 is 83.5％ (1959/2345kg-CO2) per car.
HEV deployment

The fuel efficiency of HEV is 19.1km/L (Toyota and Honda). Based on this estimate, theannual reduction in CO2 emissions is 1228kg-CO2 per car. Thus, the reduction rate of CO2
is 52.4％ (1228/2345kg-CO2) per car.
PHEV deployment

The PHEV is estimated to run with gasoline for 60% and with electric power for 40% of
the travel distance (METI). Based on this estimate, the annual reduction in CO2 emissions
is 1520kg-CO2 per car. Thus, the reduction rate of CO2 is 64.8％ (1520/2345kg-CO2) per
car.

29

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32

Table 1: Summary of past clean fuel or electric vehicle conjoint studies (cf. Hidrue et al. 2011)
Study

Econometric model

Beggs et al. (1981)

Ranked logit

Number of choice sets,attributes, and levels
16, 8, NA

Calfee (1985)
Bunch et al. (1993)

Disaggregate MNLMNL and Nested logit

30, 5, NA
5, 7, 4

Segal (1995)

First choice model

NA, 7, 2-3

Brownstone and Train(1999);
Brownstone et al. (2000)

MNL and Mixed logit; Joint
SP/RP Mixed logit

2, 13, 4

Ewing and Sarigollu ( 2000) MNL

9,7,3

Dagsvike et al. (2002)
Potoglou and Kanaroglou(2007)
Ahn et al. (2008)

Ranked logit
Nested logit

15, 4, NA
8, 7, 4

multiple discrete-continuous
extreme value (MDCEV
model)
Binominal logit withindividual fixed effects
MNL
MNL; Joint SP/RP MNL
Latent class

4, 6, 2-5

Banfi (2008)
Mau et al. (2008)
Axsen et al. (2009)
Hidrue et al. (2011)

List of attributes used
price, fuel cost, range, top speed, number of seats, warranty,
acceleration, air consitioning
price, opeating cost, range, top speed, number of seats
price, fuel cost, range, acceleration, fuel availability, emission
reduction, dedicated versus multi-fuel capability
price, fuel cost, range, fuel type, refueling duration, refuelinglocation, refueling time of day(*)
Price, range, home refueling time, home refueling cost,service station refueling cost, fuel availability, acceleration,
top speed, emission reduction, vehicle size, body type,luggage space
price, fuel cost, repair and maintenance cost, commuting
time, acceleration, range, charging time
price, fuel cost, range, top speed
price, fuel cost, maintenance cost, fuel availability,
acceleration, incentives, emission reduction
fuel type, body type, maintenance cost, engine displacement,
fuel efficiency, fuel priceNA, 4, 2-5

price, window, façade , ventilation18, 6, 3
18, 5, 3
2, 6, 4

price, fuel cost, subsidy, range, fuel availability, warranty
price, subsidy, horsepower, fuel efficiency, fuel priceprice, range, charge time, acceleration, emission reduction,
fuel costNA, not available.
(*) The two papers used the same data/study. Hence the list in the attribute column and the number of choice sets, atributes and levels columun are
the same for both.

Figure 1: Example of conjoint questionnaire(a) SM’s conjoint

A1

Alt 1

Alt 2

Off-peak discount0% discount

30% discount

Visualization of electricity consumption

Advise option
0% reduction

Monitor option

30％ reduction

Monthly usage charge$2 plus$6 plus / month

Peak surcharge

4 times plus

2times plus↓↓

Reduction in greenhouse gas emissions

Temperature remote-control

nothing

Choose one

34

Alt 3

No useof Smartmeter

82F↓(b) PV’s conjoint

1

Alt 1

Alt 2

Initial cost

$15,000$10,000

Annual reduction in fuel and lighting charges

80% reduction

60% reduction

normalexcellent

20 years

5 years

Stylishly designed PV panel
Free inspection and maintenance period
Reduction in greenhouse gas emissions

60% reduction

40% reduction↓↓

Choose one

35

Alt 3

No useof PV↓

(c) EV/HEV’s conjoint
Alt 1

Alt 2

EV

HEV

EV/HEV Premium$5,000 plus

$1,000 plus

Annual reduction in fuel cost

90%

60%

Driving range on a full battery

100km

1500km--Plugin option

A1

Time to find a charging station

30 minitues

Reduction in greenhouse gas emissions

80% reduction

70% reduction↓↓

Home plug-in option

Choose one

36

--

Alt 3

N o us eof eco car↓

Table 2: Demographic characteristics

Owned house
Detached house
Gender (Male)
Age
MarriedFulltime employedElectric Expences
Annual household income

High interest
65.9%
51.8%
59.5%
43.8
77.7%
63.1%
US$1,154
US$71,050

Low interest
68.3%
56.0%
50.7%
43.8
67.4%
50.5%
US$1,164
US$55,760

37

Weighted average
67.8%
55.2%
52.3%
43.8
69.3%
52.8%
US$1,162
US$58,528

Table 3: Utilization(a) SM’s utilization
Service recognition
Current utilization
Future utilization
Importance attached for
WEB display
Private monitor display
Energy-saving advice
On-peak surcharge
Off-peak discountAutomatic control at F
Temporary restriction
Usage interception
Reduction in greenhouse gas emission
Average monthly WTP

High interest
12.0%
1.4%
3.4%

Low interest
4.9%
0.9%
1.9%

Weighted average
6.2%
1.0%
2.2%

56.3%
55.6%
58.6%
61.0%
61.2%
33.1%
32.6%
30.7%
53.9%
US$1.8

43.1%
42.0%
44.8%
50.0%
51.9%
22.6%
20.7%
17.8%
38.3%
US$1.3

45.5%
44.5%
47.3%
52.0%
53.6%
24.5%
22.9%
20.1%
41.1%
US$1.438

(b) PV’s utilization

Service recognition
Current utilization
Future utilization
Importance attached for
Reduction in fuel and lighting charges
Selling surplus electricity
Reduction in greenhouse gas emission
Free inspection and maintenance period
Stylishly designed PV panel
Average initial cost WTP

High interest
54.1%
2.8%
19.4%

Low interest
41.9%
4.0%
6.6%

Weighted average
44.1%
3.8%
8.9%

80.0%
77.1%
61.5%
74.9%
53.0%
US$7,703

71.0%
72.0%
48.3%
68.4%
42.8%
US$5,395

72.6%
72.9%
50.7%
69.6%
44.6%
US$5,813

39

(c) EV/HEV’s utilization

EV Service recognition
EV Current utilization
EV Future utilization
HEV Service recognition
HEV Current utilization
HEV Future utilization
Importance attached for
Annual reduction in fuel cost
Driving range on a recharge or refuelReduction in greenhouse gas emission
Time to find a charging station
Home plug-in option
Average EV premiumHigh interest
61.1%
0.5%
13.7%
66.1%
6.5%
32.7%

Low interest
51.9%
1.0%
4.8%
53.8%
3.7%
15.7%

Weighted average
53.6%
0.9%
6.4%
56.0%
4.2%
18.8%

82.1%
78.5%
61.1%
74.9%
72.5%
US$2,191

73.8%
71.2%
49.0%
68.4%
63.0%
US$1,639

75.3%
72.5%
51.2%
69.6%
64.7%
US$1,739

40

Table 4: estimation results(a) SM’s estimation results
No. observations
Log likelihood
McFadden R2

High interest

Low interest

5192-4468.1
0.21675552-4179.6
0.3148

Coeff.
Std. Err.
Fixed parameters-0.00460.0002Monthly usage charge
Alt 3 constant-1.10210.1039Random parameters (mean)
WEB display
0.01040.0863
Private monitor display
0.38240.1118Energy-saving advice
0.35680.0960Off-peak discount0.14480.0234
Peak surcharge-0.2892
0.0255
Automatic control-0.2275
0.0726
Temporary restriction
0.10180.0964
Usage interception-0.0811
0.0916
Reduction in emissions
0.00650.0021
Random parameters (standard deviation)
WEB display
0.54000.1748
Private monitor display
1.17200.1287
Energy-saving advice
0.29240.3222Off-peak discount0.39910.0247
Peak surcharge
0.41530.0297
Automatic control
0.02870.2740Temporary restriction
0.01420.2222
Usage interception
0.30670.1445
Reduction in emissions
0.03670.0024

***
***

***
***
***
***
***

***
***
***
***
***

**
***

Coeff.

Std. Err.

-0.0055-0.57270.00020.10860.11920.33920.6253
0.0983-0.3614-0.1774
0.0708-0.3353-0.0035

0.10380.11670.10370.03100.03000.08460.10280.10700.00280.69711.03650.26330.54150.43790.15020.17300.2621
0.05350.18140.14600.18910.03070.03140.20320.18760.23810.0032

41

***
***

***
***
***
***
**
***

***
***
***
***

***

(b) PV’s estimation results

No. observations
Log likelihood
McFadden R2

High interest

Low interest

5192-3300.3
0.42145552-2863.70.5305

Coeff.
Fixed parameters
Initial cost
-0.0250
Alt 3 constant-0.1058
Random parameters (mean)
Annual reduction in charges
0.3697
Reduction in emissions
0.0343
Free inspection
0.0660Stylishly designed-0.1336
Random parameters (standard deviation)
Annual reduction in charges
0.6618Reduction in emissions
0.0306
Free inspection
0.0784Stylishly designed
0.1714

Std. Err.

Coeff.

Std. Err.

0.00080.3532***-0.0299
0.77150.00110.3976****

0.04850.00430.0056
0.0977

***
***
***

0.1756
0.04140.0846
0.28840.05790.00490.00720.1088

***
***
***
***

0.03420.00430.00620.1820

***
***
***

0.84430.0221
0.09500.31600.04440.00470.00810.2052

***
***
***

42

(c) EV/HEV’s estimation results
No. observations
Log likelihood
McFadden R2
Fixed parameters

EV/HEV PremiumAlt 1 constantAlt 2 constantRandom parameters (mean)
EV
Annual reduction in fuel cost
Driving range

Reduction in emissions

Time to find a charging station
HEV
Annual reduction in fuel cost
Driving range

Reduction in emissions
Home plug-in

High interest

Low interest

5192-3931.6
0.31075552-3888.4
0.3625

Coeff.

Std. Err.

Coeff.

Std. Err.

-0.0502-0.3338
0.39120.00200.54670.2518***-0.0587
0.6176
0.15380.0021
0.54950.2578***0.11800.00090.00720.00420.05860.00050.00580.0061***0.13940.0012-0.0250
0.00540.05930.00060.00660.0062*******0.14000.00060.00350.65970.02990.00010.00410.1011******0.03030.00010.00450.0949

***
***

***

0.09540.0005-0.00420.41370.50330.00260.02220.04910.02900.00080.00180.0063

***
***
***
***

0.17600.00370.04690.02530.02370.00060.00200.0065

***
***
***
***

0.51670.00090.02220.86120.03010.00010.00180.1594

***
***
***
***

0.48050.00050.04690.05320.04130.00020.00200.2012

***
***
***

Random parameters (standard deviation)EV
Annual reduction in fuel cost
Driving range

Reduction in emissions

Time to find a charging station
HEV
Annual reduction in fuel cost
Driving range

Reduction in emissions
Home plug-in

43

***

Figure 2: WTP values(a) SM’s WTP values

WTP(US$/month)

2

1

0

-1

Privatemonitordisplay

Energysaving
adviceOff-peakdiscount
(10%)

Peak
surcharge(double)High interest

0.8

0.8

0.3

-0.6

-0.5

Low interest

0.6

1.1

0.2

-0.7

-0.3

-0.6

Average

0.7

1.1

0.2

-0.6

-0.4

-0.5

WEB display

44

Automatic Temporary
control (82F) restriction

Usage
Reduction ininterception
emissions
0.4
0.1

(b) PV’s WTP values
1600.01400.0WTP(US$)

1200.01000.0
800.0
600.0
400.0
200.0
0.0

Annual reduction in
charges (10%)

Reduction in emissions(10%)

Free inspection (10 years)

High interest

1481.81375.6264.4

Low interest

587.3

1385.6

282.9

964.6

Average749.2

1383.8

279.6

790.0

45

Stylishly designed

WTP(US$)(c) EV’s WTP values
300.0
200.0
100.0
0.0
-100.0-200.0-300.0-400.0-500.0

Annual reduction infuel cost

Driving range

High interest

234.9

181.2

Low interest

237.6

199.5

-425.7

Average

237.2

196.2

-348.746

Reduction in emissions

Time to find a charging
stationWTP(US$)(d) HEV’s WTP values
1400.01200.01000.0
800.0
600.0
400.0
200.0
0.0

Annual reduction infuel cost (10%)

Driving range(100km)High interest

280.0

117.0

1310.0

Low interest

125.1

89.7

646.7

Average

153.1

94.6

766.8

47

Reduction inemissions (10%)

Home plug-in

Table 5: diffusion rates(a) SM’s diffusion rates
Smart meters
Monthly charge = $ 3

Monthly charge = Free

Household emissions = – 10%

Household emissions = – 30%

High interest = 61.4%

High interest = 64.4%

Low interest = 36.0%

Low interest = 34.4%

Average = 40.6%

Average = 39.8%

High interest = 86.4%

High interest = 87.8%

Low interest = 74.8%

Low interest = 73.4%

Average = 76.9%

Average = 76.0%

Household emissions = – 40%

Household emissions = – 60%

High interest = 48.2%

High interest = 64.8%

Low interest = 9.8%

Low interest = 20.0%

Average = 16.8%

Average = 28.1%

High interest = 76.4%

High interest = 86.6%

Low interest = 32.8%

Low interest = 52.8%

Average = 40.7%

Average = 58.9%(b) PV’s diffusion rates
Photovoltaic generation
Initial cost = $20,000

Initial cost = $15,000

48

(c) EV’s diffusion rates
Electric vehicles
EV premium = $10,000

EV premium = $5,000

Vehicle emissions = – 80%

Vehicle emissions = – 100%

High interest = 1.0%

High interest = 1.1%

Low interest = 0.2%

Low interest = 0.1%

Average = 0.3%

Average = 0.3%

High interest = 4.7%

High interest = 5.1%

Low interest = 2.3%

Low interest = 1.6%

Average = 2.7%

Average = 2.2%

Vehicle emissions = – 60%

Vehicle emissions = – 80%

High interest = 61.1%

High interest = 62.6%

Low interest = 21.5%

Low interest = 20.1%

Average = 28.7%

Average = 27.8%

High interest = 80.9%

High interest = 81.4%

Low interest = 52.6%

Low interest = 51.4%

Average = 57.7%

Average = 56.8%(d) PHEV’s diffusion rates
Plugin hybrid vehicles
PHEV premium = $5,000

PHEV premium = $2,500

49

Table 6: Social reduction in greenhouse gas emissions(a) SM’s reduction in greenhouse gas emissions
Smart meters
Monthly charge = $ 3

Monthly charge = Free

Household emissions = – 10%

Household emissions = – 30%

High interest = –6.1%

High interest = –19.3%

Low interest = –3.6%

Low interest = –10.3%

Average = – 4.1%

Average = – 11.9%

High interest = –8.6%

High interest = –26.3%

Low interest = –7.5%

Low interest = –22.0%

Average = – 7.7%

Average = – 22.8%(b) PV’s reduction in greenhouse gas emissions
Photovoltaic generation
Initial cost = $20,000

Initial cost = $15,000

Household emissions = – 40%

Household emissions = – 60%

High interest = –19.3%

High interest = –38.9%

Low interest = –3.9%

Low interest = –12.0%

Average = – 6.7%

Average = – 16.9%

High interest = –30.6%

High interest = –52.0%

Low interest = –13.1%

Low interest = –31.7%

Average = – 16.3%

Average = – 35.4%

50

(c) EV’s reduction in greenhouse gas emissions
Electric vehicles
EV Premium = $10,000

EV Premium = $5,000

Vehicle emissions = – 80%

Vehicle emissions = – 100%

High interest = –0.8%

High interest = –0.9%

Low interest = –0.2%

Low interest = –0.1%

Average = – 0.3%

Average = – 0.2%

High interest = –3.8%

High interest = –4.1%

Low interest = –1.8%

Low interest = –1.3%

Average = – 2.2%

Average = – 1.8%(d) PHEV’s reduction in greenhouse gas emissions
Plugin hybrid vehicles
PHEV premium = $5,000

PHEV premium = $2,500

Vehicle emissions = – 60%

Vehicle emissions = – 80%

High interest = –36.7%

High interest = –50.1%

Low interest = –12.9%

Low interest = –16.7%

Average = – 17.2%

Average = – 22.2%

High interest = –48.5%

High interest = –65.1%

Low interest = –31.6%

Low interest = –41.1%

Average = – 34.6%

Average = – 45.5%

51

Table 7: Interdependencies among smart equipment diffusions(a) SM's choice probability elasticities
Highly interested households
elasticities
0.319
PV-0.107
EV
0.863
PHEV
Litlle interested households
elasticities
0.080
PV
0.041
EV
0.892
PHEV

t values
2.2522-1.35433.7826*****t values
1.4883-0.28613.6169***(b) PV’s choice probability elasticities
Highly interested households
elasticities
1.748
SM
0.773
EV
0.933
PHEV
Litlle interested households
elasticities
1.162
SM
0.395
EV
1.352
PHEV

t values
11.59915.95044.4484

***
***
***

t values
10.62255.00098.6511

***
***
***

52

(c) EV’s choice probability elasticities
Highly interested households
elasticities
0.365
SM
0.698
PV
Litlle interested households
elasticities
0.378
SM
0.491
PV

t values
4.333510.0913******t values
8.2064
11.2661******(d) PHEV’s choice probability elasticities
Highly interested households
elasticities
0.251
SM
0.286
PV
Litlle interested households
elasticities
0.162
SM
0.273
PV

t values
6.14038.9201******t values
5.6447
11.1428******53