OSIPP Discussion Paper : DP-2007-E-005

Does energy efficiency label alter consumers’ purchasedecision? A latent class approach on Shanghai data
May 11, 2007
Junyi Shen
Assistant Professor, OSIPP, Osaka University
andTatsuyoshi Saijo

Professor, ISER, Osaka University【キーワード】C25, C93, D12, Q49, Energy efficiency label, Consumers’ purchasedecision, Latent class model, Willingness to pay, China【要約】In this paper we apply hypothetical choice experiments through a field survey
in Shanghai of China to examine whether China Energy Efficiency Label affects
consumers’ choices of air conditioner and refrigerator. A latent class approach is used toobserve both heterogeneities among the respondents and product brands. The results
suggest that the effect of energy efficiency label on consumers’ preferences is twofold.
First, more energy efficient air conditioners or refrigerators are preferred by consumers,
no matter whether they are with foreign brands or domestic brands and whether they
are new or second-hand. Second, energy efficiency label per se is recognized byconsumers. In addition, presence of a (hypothetical) label that indicates the electricity
bill’s difference comparing to a standard model is significantly preferred by the
respondents in most of the cases, suggesting that more information provided toconsumers makes them much happier. Finally, the class probability weighted
willingness to pay values for one rank upgrading in energy efficiency of refrigerator are
higher than those of air conditioner, implying that consumers have an incentive to pay
more for appliances used more frequently.
The authors would like to appreciate the financial support of Japanese Ministry of theEnvironment (Global Environmental Research Fund H-062) and Japanese Ministry of
Education, Culture, Sports, Science and Technology (Grants-in-Aid for Young Scientists(B), 19730175). Comments and suggestions on the survey issues from Yoshiyuki
Shimoda, Osamu Saito, Yugo Yamamoto, and Kenju Akai are greatly appreciated. All
the views expressed in this paper and any errors are the sole responsibility of theauthors. Correspondence to: Junyi Shen, Osaka School of International Public Policy(OSIPP), Osaka University, 1-31 Machikaneyama, Toyonaka, Osaka 560-0043, Japan.
E-mail: junyi@osipp.osaka-u.ac.jp
11. Introduction
Studies on consumer behavior are usually conducted by policy decision makers forsolutions to environmental policy problems, because they reason, at least implicitly, thatenvironmental degradation comes from economic activity, that consumer expenditures
account for most of gross domestic product, and that therefore changing consumer
behavior can go a long way toward reducing environmental degradation (Stern, 1999).
During the last decade, there has been a number of public policies interest in
sustainable consumption and the role of consumers in environmental conservation.
However, the needed change in consumption patterns toward sustainable consumption
normally cannot be achieved by force, but only with a kind of “voluntary approach” suchas information providing policy, which plays a key role in environmental policy
targeting consumers. One of the most promising forms of environmental information
policy, in terms of providing timely and relevant information for the consumer, isenvironmental labeling that is authenticated and monitored by a third-party (Crespi
and Marette, 2005; Loureiro et al., 2002; Thøgersen, 2000). The main purpose ofproviding various environmental labels (also called eco-labels) to consumers is to avoid
the asymmetric information problem between producers and consumers, because someenvironmentally friendly products normally have unobservable characteristics, which
will cause potential inefficiencies resulting from imperfect information.
The idea that information provision could be an effective method of environmental
regulation is a significant and even revolutionary departure from traditional thinking(Russell et al., 2005). This departure is explicitly recognized as “the third wave” inpollution control policy in Tietenberg and Wheeler (2001).1 Beginning with the German
Blue Angel in 1977, a number of environmental labeling programs in the organic foodand electricity markets as well as the forestry products have developed (e.g. Nordic
Swan in the Nordic countries, EU Flower in EU countries, Green Seal in USA,
Environmental Choice in Canada, Eco-Mark in Japan, Green Label in Singapore, etc.).
Encouraged by widely increasing environmental labeling schemes, both theoretical andempirical studies in academic society on this issue have been rapidly growing (e.g.
Amacher et al., 2004; Banerjee and Solomon, 2003; Bjørner et al. 2004; Cason andGangadharan, 2002; Crespi and Marette, 2005; Dosi and Moretto, 2001; Howarth et al.,
2000; Johnston et al., 2001; Loureiro et al., 2002; Moon et al. 2002; Morris et al., 1995;
Roe et al., 2001; Russell et al. 2005; Thøgersen, 2000; Tisel et al., 2002, etc.). Concerning
the effect of environmental label on consumers’ purchase decision, Bjørner et al. (2004)
1 The first wave refers to as “command-and-control” regulation, while the second wave

involves the introduction of attempts to alter polluter behavior through the use of
economics incentives, or what are commonly called “market-based instruments” such as
emissions charges, tradable discharge permits, and deposit-refund schemes (Russell etal., 2005).
2

used a large Danish consumer panel with detailed information on actual purchasesfrom the beginning of 1997 to January 2001 to quantify the effect of the Nordic Swan on
consumers’ brand choices of toilet paper, paper towels and detergents. They found that
the Nordic Swan label has had a significant effect on consumers’ brand choices for toilet
paper and detergent. Similar effects of various eco-labels on consumers’ choice of
products can also be found in the studies of dolphin-safe label (Teisl et al., 2002),
eco-labeled apples (Loureiro et al., 2002), eco-labeled seafood (Johnston et al., 2001),
eco-labeled agricultural products (Moon et al., 2002), and energy label (Banerjee andSolomon, 2003; Howarth et al., 2000). In addition, two theoretical studies (i.e. Amacher
et al., 2004; Dosi and Moretto, 2001) stated that eco-labeling could be used as a means ofreliable environmental policy to improve market outcomes by which firms adopt
clean(er) technologies and produce green products based on consumers’ recognition on
eco-labels and willingness to pay for these certified environmentally friendly products.
Furthermore, Cason and Gangadharan (2002) examined various treatments that could
remedy the market failure arising from incomplete information through laboratory
posted offer markets. They concluded that the only reliable way to improve productquality in the experiment is to use a third party that charges a fee to certify productquality, i.e., environmental labeling.
To provide more empirical evidence, in this study we present an analysis of theeffect of a specific environmental label (China Energy Efficiency Label) on Chinese
consumers’ choices among different brands of air conditioner and refrigerator.
Nowadays, energy labels and minimum energy performance standard are fast becoming
commonplace throughout the world. It is axiomatic that the market for householdenergy services would be enhanced where buyers are able to take into account not justthe cost of the appliance but the otherwise invisible factor of energy consumption.
Energy labels improve the market’s operation by displaying accurate energy
consumption information on products, which is thought to be useful in the purchasedecision. There are two main types of energy labels: endorsement and comparative
labels. Endorsement labels indicate that products belong to the “most energy efficient”
class of products or meet a predetermined standard or eligibility criteria. Comparative
labels such as China Energy Efficiency Label allow consumers to form a judgment about
the energy efficiency and relative ranking of all products that carry a label.
Endorsement and comparative labels can coexist, and do so in many countries. The
main difference between endorsement label and comparative label is that the effect of
the latter on consumers’ purchase decision is twofold: (i) whether or not the label per seis recognized by consumers; (ii) whether or not different ranks presented on the label,
which indicate different levels in energy efficiency, alter consumers’ preferences andpurchases decisions.
Previous work on the effect of energy label on consumers’ response is limited.
Banerjee and Solomon (2003) presented a meta-evaluation of five US energy labeling3

programs: Green Seal, Scientific Certification Systems, Energy Guide, Energy Star, andGreen-e. They stated that “although most people indicate a strong preference for acredible, third-party labeling system and claim to use it, very few actually use the labels
for purchasing decisions” and “Such overstated responses leading to survey bias arethought to be due to the ‘warm glow’ effect”. Acknowledging this kind of “warm glow”
effect and to overcome it in our study, we apply a choice experiment approach in which
respondents are asked to select a most preferable alternative from a set of alternatives
based on a number of attributes and their levels. Since the respondent’s choice decision
is made based on the tradeoff among the associated attributes, we believe that “warmglow” effect, at least to some extents, could be avoided by this manipulation.
The analysis is based on hypothetical choice experiments for air conditioner andrefrigerator through a questionnaire survey conducted at the beginning of November
2006 in Shanghai of China. In each choice experiment, we provide four alternatives
named as new product A with foreign brands, second-hand product B with foreign
brands, new product C with domestic brands, and second-hand product D with domestic
brands, respectively. 2 This specification allows us to observe Shanghai consumers’
preferences on second-hand products and different brands (foreign or domestic). In
addition to the attribute associated with energy efficiency label, other attributes include
the prices, the electricity consumptions, applicable space/volume, with or without air
cleaning/silence function, and presence or absence of a label indicating the electricity
bill’s difference comparing to a standard model. Note that including the final attribute
related to presence or absence of another label serves as a second goal of this study toinvestigate whether or not additional information provision affects consumers’ purchasedecision.
A Latent Class(LC) model approach is applied in the estimation. Compared to a
standard Multinomial Logit (MNL) model in discrete choices, the LC approach allows
the analysts to observe individual heterogeneity through identifying and characterizing
various preference groups (Boxall and Adamowicz, 2002; Louviere et al., 2000; Greeneand Hensher, 2003; Shen, 2006). In addition, this approach can estimate simultaneously
the probability of individuals in each class, hence, it is even valid in welfare analysis
which considers to aggregate welfare gained from each class.
As a preview of results, it is found that China Energy Efficiency Label does have asignificant effect on Shanghai consumers’ purchase decisions of air conditioner andrefrigerator. Meanwhile, higher price and higher electricity consumption reduce
individuals’ preferences. Functions such as air cleaning of air conditioner and silence ofrefrigerator are preferred by consumers in most cases, while applicable space of air
conditioner and volume of refrigerator are evaluated differently by the respondents.

2 The fact that there exists a second-hand appliance market in China encourages us to

examine the issue on Chinese consumers’ preferences of second-hand appliances.
4

Furthermore, consumers prefer to have a (hypothetical) label indicating clearly theelectricity bill’s difference comparing to a standard model in most cases, suggesting that
information provision at an explicit way such as through a label is an effective means toaffect consumers’ choice decisions.
The remainder of the paper is organized as follows. Section 2 provides a briefsummary of China Energy Efficiency Label program. Section 3 presents the model
specification. The survey issue is described in Section 4 and the empirical results arereported in Section 5. Finally, we provide conclusion remarks and future implications in
Section 6.

2. China Energy Efficiency Label program
Home appliance ownership and production has increased dramatically in China in thepast two decades. From extremely low levels in 1980, China’s appliance industry has
become one of the largest in the world, with sales topping U.S.$ 49.55 billion in 2005.
Meanwhile, the ownership ratio of refrigerator by urban Chinese households increases
from less than 1 percent in 1981 to over 90 percent by 2006 (China State Statistics
Bureau, 2006). A similar trend has been observed for room air conditioner and other
home appliances as well. Such a dramatic increase in appliance ownership hassignificantly led to the growth of China’s electricity use. Between 1980 and 2001, percapita annual electricity consumption grew from 254.6 to 995.2 kwh, with an averageannual growth rate of 8 percent, while per capita residential electricity use grew from10.0 to 144.6 kwh, averaging 15 percent growth per year (nearly twice as fast as overall
electricity consumption). The growth in household electricity consumption has
contributed substantially to the tremendous increase of generating capacity in China inrecent years. Since 1990, China has added on average 16 GW of new capacity each year.
Well acknowledging the environmental impacts of electricity generation, China
started to implement a number of programs including energy labeling program to
improve energy efficiency. 3 The basis for energy labeling program in China wasestablished in the “Energy Conservation Law”, which was approved by the National
People’s Congress on

November 1, 1997 and came into force on

January 1, 1998. The

law superseded earlier laws and set the basis for measures to develop energy efficiency
standards and energy labeling of appliances and equipments.
At first, the government decided to issue a voluntary endorsement label−China
Energy Conservation Label (see Figure 1). The State Economic and Trade Commission

3 Gains in energy efficiency has been regarded as one of the explanations for China’s

decline in energy intensity in recent years. For details on this issue, see Fisher-Vanden
et al. (2004).
5

(SETC, now NDRC: National Development and Reform Commission) and the China
State

Bureauof

QualityandTechnical

Supervision(CSBTS,nowAQSIO:

Administration for Quality, Supervision, Inspection and Quarantine) together
established an independent, non-profit organization−the China Certification Center for
Energy Conservation Product (CECP), aiming at developing requirements in order tocertify products as being safe, high-quality, and energy-saving. The endorsement label
designed by the CECP was formally launched in September 1999 and first applied inrefrigerators. The application for China Energy Conservation Label is similar to theUSA’s Energy Star Label and so far has been awarded to over 100s of products.
In addition to the above voluntary energy conservation label, the China National
Institution of Standardization (CNIS) was assigned to develop a mandatory andcomparative energy information label−China Energy Efficiency Label (see Figure 2).
The designed label came into effect from March 2005. The first two products wererefrigerator and room air conditioner. The China Energy Efficiency Label is similar to
EU Energy Label, which rates the energy efficiency of the appliance in terms of a set ofenergy efficiency ranks from 1 to 5 on the label. Rank 1 is the most energy efficient,while rank 5 is the least efficient. The CNIS reports that by the end of March 2006, theproducts with energy efficiency ranks 1 and 2 in China share about 10 percent in room
air conditioner market and 68 percent in refrigerator market. In contrast, the productswith energy efficiency rank 5 only occupy 7 percent in room air conditioner market and8 percent in refrigerator market. These evidences suggest that there is an energy
efficiency rank gap between air conditioner and refrigerator in China. Compared to themajority of high efficient energy-saving refrigerator on the market, most of room air
conditioners are produced with medium energy efficiency level (ranks 3 and 4).

3. Model specification
Choice model is based on random utility theory. The basic assumption embodied in the
random utility approach to choice modeling is that decision makers are utility
maximizers, i.e., given a set of alternatives the decision maker will choose thealternative that maximizes her utility. The utility of an alternative for an individual(U ) is modeled to consist of a deterministic component (V ) and a random error term ( ε ).
Formally, individual q ’s utility of alternative i can be expresses as:

U iq = Viq + ε iq

(1)Hence the probability that individual q chooses alternative i from a particular set J
that comprises j alternatives can be written as:
Piq = P (U iq > U jq ; ∀ j (≠ i ) ∈ J ) = P(ε jq < ε iq + Viq − V jq ; ∀ j (≠ i ) ∈ J )(2)

6

To transform the random utility model into a choice model, certain assumption about
the joint distribution of the vector of random error terms is required. If the random
error terms are assumed to follow the type I extreme value (EV1) distribution and be
independently and identically distributed (IID) across alternatives and cases (orobservations), the multinomial (or sometimes called conditional) logit (MNL) model(McFadden, 1974) is obtained. In the MNL model, the choice probability in Equation (2)is expressed as:
J

Piq = exp( µViq ) / ∑ exp( µV jq )

(3)

j =1

Then, making further assumption for the deterministic component of utility to be linear
and additive in parameters, Viq = β ′ Xiq, the probability in Equation (3) can be given as:
J

Piq = exp( µβ ′X iq ) / ∑ exp( µβ ′X jq )

(4)

j =1

where µ represents a scale parameter that determines the scale of the utilities, which
is typically normalized to 1.0 in the MNL model. Xiq are explanatory variables of Viq,
normally including alternative-specific constants (ASCs), the attributes of thealternative i and the social-economic characteristics of the individual q , β ′ is theparameter vector associated with the matrix Xiq.
It is well known that heterogeneity among individuals is extremely difficult to
examine in the MNL model. This limitation could be relaxed, to some extents, byinteracting individual-specific characteristics with various of the choices. However, thismethod is limited because it requires a priori selection of key individual characteristics
and attributes and only involved a limited selection of individual specific variables(Boxall and Adamowicz, 2002). One way of circumventing this difficulty is throughestimating the model by the Latent Class (LC) model. The LC model assumes that the
population consists of a number of latent classes S and the unobserved heterogeneity
among individuals can be captured by these classes through estimating a different
parameter vector in the corresponding utility function. Formally, the choice probabilityof individual q of class s is expressed as:
J

Piq|s = exp( µ s β s′ X iq ) / ∑ exp( µ s β s′ X jq )s = 1,..., S

(5)

j =1

where µ s and β s′ are class-specific scale and utility parameters, respectively. Then,following Boxall and Adamowicz (2002), Louviere et al. (2000), and Swait (1994, 2007),the probability of individual q in class s (Hqs ) can be expressed as:

H qs = exp(αλs′Z q ) /S∑ exp(αλ ′Z )
s

q

(6)

s =1

7

where α is a scale factor normally normalized to 1.0, λs′ is the parameter vector inclass s, and Z q denotes a set of characteristics (e.g. individual-specific characteristics)
determining the classification probability. Combining conditional choice equation (5)and membership classification equation (6), the unconditional probability of choosing
alternative i is given as:

Piq =

S

S

⎡

⎤⎡

J

⎤

S

∑ P H = ∑ ⎢⎢exp(µ β ′ X ) / ∑ exp(µ β ′ X )⎥⎥ ⎢⎣exp(αλ ′Z ) / ∑ exp(αλ ′Z )⎥⎦
iq| s

qs

s =1

s =1 ⎣

s

s

iq

s

j =1

s

jq

⎦

s

q

s

q

(7)

s =1

In equation (7), when we set µ s and α equal to one4, the parameter vectors β s′ andλs′ can be simultaneously estimated by the maximum likelihood method to explain
choice behavior.5
However, the LC model cannot be estimated unless S (the number of classes) inequation (7) is given, because S is discrete but maximum likelihood estimation theory
requires that the parameter space be continuous and estimates be in the interior of thespace (Swait, 2007). Therefore, the central issue in the LC model is how to determine S.
The literature has recommended a number of information criteria to determine S (e.g.
Boxall and Adamowicz, 2002; Greene and Hensher, 2003; Louviere et al., 2000; Morey elal., 2006; Shen, 2006; Swait, 2007, etc.). Among them, four measures based on the log
likelihood at convergence with s classes, sample size and number of parameters are
popular to be used to determine S. They are defined as:
Akaike Information Criterion, AIC = −2(log L*s − K s )Akaike’s ρ 2 , ρ s2 = 1 − [ AICs /(2 ⋅ log L0 )]Bozdogan Akaike Information Criterion, AIC 3 = −2log L*s + 3K s
Bayesian Information Criterion, BIC = − log L*s + ( K s ⋅ log N ) / 2

(8)
(9)
(10)
(11)

where log L*s is the log likelihood at convergence with s classes, K s is the number ofparameters in the model with s classes, L0 is the log likelihood of the sample with equal
choice probabilities, and N is the sample size.
An alternative approach accounting for individual heterogeneity is called Random
Parameter Logit (RPL) or Mixed Logit (ML) model, which allows model parameters tovary randomly through assumed distributions (e.g. normal, log-normal, triangular, etc.)
over individuals (e.g. Bhat and Gossen, 2004; Bjørner et al., 2004; Greene and Hensher,
2003; Hess et al., 2005; McFadden and Train, 2000; Revelt and Train, 1998; Train, 1998,
etc.). In this approach, each individual has their own set of scale and utility parameters.

4 Boxall and Adamowicz (2002) note that utilizing the LC model in empirical estimation

requires that all the scale factors in equation (7) are set equal to one.
λs′ in one of the latent classes must be normalized to zero (e.g.

5 The parameter vector

λ1′ ≡ 0 ) to run the estimation. Therefore, the remaining λ ′ s are identified relative tothis normalization.
8

From this viewpoint, one could regard the RPL/ML model as the case where eachindividual in the sample could be considered as an individual class, which is indeed the
LC model with N (sample size) classes. In other words, the LC model controls individual
heterogeneity with s classes where s is between 1 and N. Compared to the RPL model,
the potential advantages of the LC model are twofold. First, the LC approach issemiparametric, therefore, it does not require the analyst to make specific assumptions
about the distributions of parameters across individuals (Greene and Hensher, 2003).
Second, the LC model yields the probabilities in each class. This means that althougheach respondent is assumed to belong to one class, it is taken into account that there is
uncertainty about a respondent’s class membership.

4. Survey issue
4.1 Choice experiment design
In the choice experiment, a number of attributes and assigned levels are used togenerate hypothetical scenarios. The attributes and their levels included in eachscenario for this study are summarized in Tables 1 (air conditioner) and 2 (refrigerator).
We have four alternatives (products) named as new product A with foreign brands,
second-hand product B with foreign brands, new product C with domestic brands, andsecond-hand product D with domestic brands, respectively for both air conditioner andrefrigerator.6 Each air conditioner or refrigerator has six attributes. For air conditioner,
they are(i) price (three different levels for each alternative)(ii) hourly electricity consumption (two same levels for A and C, and two same levels for
B and D)(iii) applicable space (two levels for all alternatives)(iv) whether there is an air cleaning function (two levels for all alternatives)(v) energy efficiency ranks presented on the label (four levels for all alternatives)(vi) whether there is a label of indicating the electricity bill’s difference comparing to a
standard model (two levels for all alternatives).7
6 A non-choice (the choice not to select one of the available alternatives) alternative

could be considered to provide. However, following the argument of Hensher et al.
(2005) that “if the objective of the study is to examine the impact of the relationships
different attribute levels have upon choice, then any non-choice alternative is likely to
be a hindrance to the analyst”, we did not provide a non-choice alternative in this study.
7 Including the label of indicating the electricity bill’s difference comparing to a
standard model as an attribute of appliance is to examine in addition to energy
efficiency label whether information provision about running cost saving through alabel could be an effective issue influencing consumer’s purchasing decision. This could
9For refrigerator, three attributes are the same as (i), (v), and (vi) for air conditioner,
another three attributes are considered as daily electricity consumption (two samelevels for A and C, and two same levels for B and D), volume (two levels for all
alternatives), and whether there is a silent function (two levels for all alternatives).
Choice experiment design is concerned with how to create the choice sets in an
efficient way, i.e. how to combine attributes levels into profiles of alternatives andprofiles into choice sets (Alpízar et al., 2003). It is obvious that it would generate too
many choice sets if we apply a full factorial design, which is too much for respondents toanswer even we divide the choice sets into a number of versions. In this study, we adopt
the D-optimal design approach for choice experiments based on multinomial logit model.
The objective of the D-optimal design is to extract the maximum amount of informationfrom the respondents subject to the number of attributes, attribute levels and othercharacteristics of the survey such as cost and length of the survey (Carlsson andMartinsson, 2003). The D-optimal design is implemented to maximize a chosen
optimality criterion (e.g. D-, A-, and G-efficiency) based on the pre-specified model (e.g.
multinomial logit model in a choice experiment). One common measure of efficiency iscalled D-efficiency that is applied in this study and given as
D-efficiency = ⎡| Ω |1/ K ⎤

⎣

⎦

−1

(12)

where K is the number of parameters to estimate, Ω is the covariance matrix of avector of parameters. Besides D-efficiency, there are also several other criteria ofefficiency such as A- and G-efficiency. The main reason for choosing D-efficiency is that
it is less computationally burdensome and could be directly run by a number ofstatistical software.8
As a result of running the D-optimal design through Design-Expert 7.0 (Stat-Ease,Inc.), we created 48 choice sets for air conditioner and 48 choice sets for refrigerator,
respectively. These choice sets were further randomly divided into 8 versions, i.e., eachversion of the questionnaire consists of 6 choice sets for air conditioner and 6 choice sets
for refrigerator. The respondents were asked to select the most favorite air conditioner
or refrigerator in each choice set and answer several other questions related to theirsocioeconomic characteristics. The examples of choice sets for air conditioner andrefrigerator are presented in Tables 3 and 4, respectively.
4.2 Data collection
A survey study was conducted at the beginning of November 2006 in Shanghai of China.
be viewed as an additional test for the validity of information provision through label
programs.
8 For more details on D-optimal design, see, for example, Alpízar et al. (2003), Carlsson
and Martinssonb (2003), and Huber and Zwerina (1996), etc.
10This survey aimed to evaluate Shanghai residents’ preferences and awareness on China
Energy Efficiency Label, which is a mandatory and comparative energy information
label in China (see detailed introductions in Section 2). The survey was conducted by
two professional marketing firms. One firm called Nikkei Research was in charge ofcollecting 600 observations through face-to-face interview. The respondents wererandomly recruited on the street of two districts (business center district andresidential district), with 300 observations in each district. The trained investigators
were assigned to ask the respondents various questions according to the questionnaire.
The average time for the face-to-face interview was approximate 15 minutes. In
addition, another firm called Searchina Research conducted the web-based survey andalso collected 600 valid samples. The questionnaires for both surveys were with thesame contents, which include (i) a number of questions to reveal the respondents’environmental concern 9 ; (ii) choices of air conditioner and refrigerator; and (iii)
respondent’s socioeconomic characteristics such as gender, age, education level,
occupation, annual household income, and household size, etc. The summary ofsocioeconomic characteristics for both samples is provided in Appendix 1. From thesummary, we may find out that both samples cover most types of the residents andhouseholds in Shanghai. In addition, compared to face-to-face interview sample,
web-based sample seems encounter a potential bias in age and education level.

5. Empirical results
Tables 5-13 present the results associated with the MNL/LC specifications and WTP
and elasticity estimates. The results presented were analyzed by using NLOGIT 3.0, a
specialist discrete choice modeling package in LIMDEP (Econometric Software, Inc.).
The definition of the variables used in this study can be found in Appendix 2. As a wholeimpression of the MNL and LC estimates, we may find that compared to the MNLmodel, the goodness of fit measures (Pseudo R2 and predictive power) gets significantly
improved after applying the LC approach.
5.1 Likelihood Ratio test for sample selection
We start by discussing the results of Likelihood Ratio (LR) test for whether or not thetwo samples (face-to-face interview, hereinafter interview, and web-based survey,
hereinafter web) could be pooled. It is said that if the estimated utility parameter are

9 These questions aiming at revealing the respondents’ environmental concern are forthe purpose of another study, which examines the socioeconomic determinants ofindividual environmental concern. See Shen and Saijo (2007) for details.
11equal across interview sample, web sample, and the pooled sample, then we may
estimate the model by pooling these two samples. The null hypothesis on equal utility
parameters is formally given as
H0: β interview = β web = β interview+web
The statistics of the likelihood ratio test suggested by Swait and Louviere (1993) arecalculated as: for air conditioner model, LR=-2(-7100.49-(-3212.07-3804.55))=167.76; forrefrigerator model, LR=-2(-7360.61-(-3332.92-3949.71))=155.97. Since the critical valueof the chi-square is 38.93 at the 1% significance level on 21 degree of freedom, the null
hypothesis that the vector of common utility parameters is equal across two samples
can be rejected in both air conditioner and refrigerator cases. Therefore, the following
estimations are conducted by each sample.
5.2 Determining the number of latent classes
As discussed in Section 3, the measures of AIC, ρ 2 , AIC3 and BIC were applied to help
determining the number of latent classes. We attempted various number of classes (1, 2,
3, 4 and 5 classes) and summarize the statistics in Table 5. The log likelihood values atconvergence reveal that in all cases the model fit improves with the numbers of classes,
especially with the 2- and 3-class models. This is not surprising because log likelihood
values normally increase in magnitude when there are more parameters need to be
estimated. From the measures of AIC3 and BIC (columns 6 and 7), we may find that theminimum values of AIC3 and BIC are clearly related to the 3-class model in all cases,suggesting that 3-class model is optimal. Furthermore, the minimum AIC and the
maximum ρ 2 seem also support 3-class model as the best solution in most cases. Thereis an exception in refrigerator model of interview sample, which suggests that 4-classmodel is better than 3-class one. However, it is noteworthy that in this case theimprovement from 3-class to 4-class is negligible because the reduction of AIC andincrease of ρ 2 are so small. Based on the above discussions, we therefore determined
to select 3-class for estimating the LC models in this study.
5.3 Characterizing the class members
Results of class membership for the 3-class LC model in air conditioner and refrigerator
are reported in Tables 6 and 7. We classified the 3 latent classes of the respondents by
their socioeconomic characteristics.10 Note that the variables associated with household
10 An argument can be made for including the respondent’s environmental concern scorein the class membership classification model, since the respondent’s environmental
concern could also be associated with the classification of various class membership.
However, as we analyzed in another study (Shen and Saijo, 2007), we found that the12size and occupation status are not listed in the tables, due to the reason that these twosocioeconomic characteristics are not significant even at 10% significance level in any
cases.11 The parameters for the third class in both tables (columns 4 and 7) are set to 0
due to their normalization during estimation. Therefore, the parameters of the othertwo classes should be explained relative to this third class.
In air conditioner case, the first class of interview sample could be classified as“middle age (36-55 years old) or old age groups (above 56 years old), or university/college
graduates” because the dummy variables of mid-age, old-age, and high-edu are
significant at 5% significance level. We may find that the effect of age on determiningclass membership is larger than that of education for this class, since the estimatedparameters of old-age and mid-age are about 1.3 times bigger than that of high-edu. For
the second class, since male is estimated with significantly negative sign and old-age iswith significantly positive sign, we label this class as “old age group or females”. Besides
the labeled two classes, the remained respondents in interview sample are classified to
be in the third class. On the other hand, in web sample, class 1 and class 2 arecharacterized as “middle age group or university/college graduates” and “high income
group with annual household income more than 100 thousand Chinese yuan”,
respectively.12
With respect to refrigerator case, the classification of three classes for interview
sample is similar to that in air conditioner case, with the difference that instead of old
age group, high household income group is more likely to be in class 2. For web sample,
based on the statistical significance and sign of each variable, the first class ischaracterized as “university/college graduates or income group with annual householdincome below 100 thousand yuan”, while the second class is classified as “ income groupwith annual household income below 100 thousand yuan or males”.
5.4 Results of the 3-class LC model
The estimated results of the 3-class LC model are listed in Tables 8-11. For purpose ofcomparison, the MNL estimates are also provided in these tables. We estimate thealternative specific constants (ASCs) of air conditioners A, B, C in air conditioner modeland refrigerators A, B, C in refrigerator model. Note again that for both appliances, A, B,
socioeconomic characteristics are highly correlated with each environmental concern
measure. Therefore, if we include these measures into classification model, it would
cause serious co-linearity among the independent variables. Hence, in this study it isassumed that the respondents’ environmental concern measures influence the classmembership through their socioeconomic characteristics. We believe that this should be
the issue.
11 There is no any significant effects on the model’s goodness of fit and other variables’
significance when we drop these two characteristics from the model.
12 100 thousand yuan approximates 12.82 thousand US$ if 1 US$=7.8 yuan.
13and C stand for new product with foreign brands, second-hand product with foreign
brands, and new product with domestic brands, respectively. Besides the ASCs, weestimate the alternative specific attributes to examine the respondents’ preferences on
all the specified attributes in detail. We believe that this approach can allow us makethe respondents’ preferences on different alternatives more clear. In other words,
heterogeneity in alternative specific attributes can be examined.

Alternative specific constants (ASCs)
Look at first the parameters of three alternative specific constants (A_asc, B_asc, andC_asc) in Tables 8-11. Most of the ASCs of A and C are estimated with significantly
positive signs, suggesting that individuals prefer new air conditioner or refrigerator per
se no matter it is a foreign or domestic product. In both interview and web samples, theA_asc of refrigerator in class 3 and C_asc of air conditioner in class 2 are not significant,
implying that at least some respondents do not care whether it is a new product or not.
Note that these effects are absent in the single MNL model, exhibiting that theheterogeneity among individuals could be captured by the latent class approach. The
results of B_asc are somewhat mixed. In interview sample, the constant of air
conditioner B is insignificant in all classes, while that of refrigerator B is significant inall classes. In particular, the B_asc of refrigerator in class 1 is estimated with negative
sign, suggesting that individuals belonging to this class dislike used refrigerator even it
is with foreign brands. This kind of disfavor with used refrigerator or air conditioner
can also be found in web sample, for example, in class 3 for refrigerator (see B_asc in
Table 11) and class 2 for air conditioner (see B_asc in Table 9). Considering theprobabilities of respondents in these classes, we may conclude that compared to usedseasonal appliance (i.e. air conditioner), more respondents dislike second-hand daily
appliance (i.e. refrigerator).

Energy efficiency ranks listed on energy label
The key issue being worthy of remark in this study is how individuals evaluate theenergy efficiency ranks presented on energy label. From Tables 8-11, it is noteworthy to
see that all the parameters associated with energy efficiency ranks are significant andwith expected negative sign in all the classes of both samples. As we discussed in the
Introduction section, the effect of energy efficiency label on consumer’s preference is
twofold, i.e. if the information provided by the means of energy efficiency label works,
consumers should significantly react on the ranks indicating energy efficiency on thelabel. Thus, the explanations on these estimated significant and negative parameters
can be made as (i) more energy efficient air conditioners and refrigerators are preferred,
no matter whether they are new or second-hand and whether they are with foreign or14

domestic brands; (ii) an energy efficiency label per se is recognized by the consumers,
otherwise consumers would not have significant effect of the energy efficiency ranks ontheir preferences. We argue that this highly significant effect is most possible due to thefact that in recent years individuals’ environmental consciousness and concern havebeen rapidly increasing in Shanghai.13
To illustrate how individuals evaluate the energy efficiency ranks with monetary
value, we provide the Willingness to Pay (WTP) values for both two appliances in Table
12. Consumers’ willingness to pay for one rank upgrading in energy efficiency is givenby the equation:

WTPa|s =β a|r | s
β a| p| s(13)where β a| p|s and β a|r |s denote the estimated parameters associated with attributes of
price and energy efficiency ranks in each class s for alternatives a , respectively.
Because these two parameters vary across classes and alternatives, therefore, theestimated WTP values could identify heterogeneity among individuals for the energy
efficiency ranks of different air conditioners and refrigerators. In addition, classprobability weighted WTP in Table 12 is calculated by applying the following equation:
3

WTPa _ weighted = ∑ WTPa|s Ps

(14)

s =1

where Ps is the probabilities of respondents in class s. Note that in several cases, theestimated parameter of energy efficiency ranks is not significant. We treat the WTP
values associated with these insignificant parameters as zero.
From table 12, we observe several evidences. First, the WTP values do vary acrossthe groups, alternatives, and appliances. Second, the estimated class specific WTP as
13 In the questionnaire, we created nine questions in order to reveal the respondents’concern about general environmental issue and some specific environmental problems
including global warming, cross-boundary and acid rain, air/water/soil pollution, urban
energy problem, green land and ecological problem, the effect of harmful substances on
health, disposal/reduction/recycling of waste, and living environmental problems. The
surveyed respondents were asked to choose to what extent they concern about theseenvironmental issues. The percentages of answers of “concern” and “somewhat concern”
in interview and web samples are respectively 91% and 93.2% for general issue, 80.7%
and 87% for global warming, 67.3% and 83.5% for cross-boundary and acid rain, 78.5%
and 87.2% for air/water/soil pollution, 74.2% and 83.8% for urban energy problem,
83.3% and 91.2% for green land and ecological problem, 89.5% and 91.3% for the effectof harmful substances on health, 86.5% and 88.3% for disposal/reduction/recycling ofwaste, and 93.8% and 91.2% for living environmental problems. See more details in
Shen and Saijo (2007).
15

well as class probability weighted WTP values for refrigerator are larger than thosecorresponding values for air conditioner in most cases of both samples, suggesting thatin contrast to an appliance used seasonally, consumers are willing to pay more moneyfor energy efficiency if the appliance is used frequently. Similar effect has been also
found in Bjørner et al. (2004), which stated that environmental labeling may be more
effective on products purchased more frequently, as consumers may feel that it would
make a greater environmental impact. Third, the WTP values for second-handappliances B are lower than other two alternatives A and C in each class. However, theresult of comparison between A and C is mixed. In some cases, the WTP values for A’s
energy efficiency ranks are higher than those for C, while in other cases they arereversed. Taking into account the class probability, we find that the class probability
weighted WTP values for C are larger than those for A. This evidence indicates that
individuals are willing to pay more on energy efficiency ranks of those appliances withdomestic brands.
Finally, we provide direct elasticities of energy efficiency ranks on choiceprobability in Table 13. As shown in the table, all the elasticity values, although small
in magnitude, are negative to choice probability. This result combined with the above
discussions are intuitive to both firm leaders and government decision makers because
energy efficiency ranks presented on the energy label is well recognized by the
consumers andalter consumers’ purchase decision. Therefore, China Energy

Efficiency Labeling program could be regarded as effective based on the criterion of
consumer response.14

Other attributes effectConcerning the attributes of monetary costs i.e. price and electricity consumption that
could be viewed as a kind of running cost, almost all the parameters associated with A
and C of both appliances are estimated with significant and negative to individual’s
preference in both samples, which is consistent with economic theory as expected. For
air conditioner B in interview sample, these two parameters are not significant. We
doubt that this is most probably due to the reason that second-hand air conditioner B
has no effect on consumers’ preferences in this sample (see B_asc in Table 8).
An air conditioner with air cleaning function or a refrigerator with silent function ispreferable in most classes of both samples, no matter it is a new or second-hand product.
In addition, compared to those estimated parameters in web sample, more applicablespace of air conditioner almost does not affect individual’s preference in interview
14 To formally evaluate a labeling program, it is necessary to consider both consumer

response and producer response. However, examining the effect of energy efficiency
label on firms’ production decision is not the issue of the present study and will be left as
our future task.
16

sample. Concerning the volume of refrigerator, high-capacity refrigerator raises thepreferences of those respondents belonging to classes 1 for all products in interview
sample. However, for other class members in interview sample and all the respondents
in web sample, the preference of this attribute differs according to different alternatives.
For example, in class 2 of both samples, the respondents care about the volume of new
refrigerators, while in class 3 of web sample, the members prefer high-capacity
refrigerators with foreign brands for both new and second-hand ones.
Meanwhile, another attribute, which is presence or absence of a label indicatingclearly the electricity bill’swith significant anddifference comparing to a standard model, is estimatedpositive sign in most cases especially for new products A and C.

This evidence suggests that individuals would be more pleased if more information isprovided explicitly. Combined with the results of energy efficiency label, it suggests that
information provision, at least to some extent, could be viewed as a valid means to affectconsumers’ preferences and consequently their behavior.

6. Conclusion
In this study we apply a survey data set to estimate models for consumers’ choices
among different brands of air conditioners and refrigerators, aiming at examining theeffect of China Energy Efficiency Label on consumers’ preferences. The latent classapproach allows us to control heterogeneity among individuals that cannot be observed
through a simple multinomial logit model. Four alternatives, which include two newones with foreign and domestic brands and two second-hand ones with foreign anddomestic brands, are considered in our choice experiments. This manipulation makes it
possible for us to investigate consumers’ preferences on second-hand products andconsequently their attributes.
From the empirical analysis, it appears that energy efficiency ranks presented onenergy label have a significant effect on the choice of air conditioner and refrigerator,
perhaps because in recent years individuals’ environmental consciousness and concern
have been rapidly increasing in Shanghai. The results from the class specificwillingness to pay for one rank upgrading in energy efficiency indicate that forsecond-hand products the values are relatively lower than other two new products.
Furthermore, the class probability weighted willingness to pay values suggest that
consumers prefer to pay more on products used more frequently, as they probably feelthat it would make a greater environmental impact and/or increase their running cost.
However, examining more deeply to distinguish between these two possible reasons is
impossible in the present study. We leave this issue as a future task.
Presence of another label indicating clearly the electricity bill’s differencecomparing to a stand model is also estimated to have significant effect on consumers’17preferences in most cases. This evidence suggests that information provided explicitly
through a label is preferred by consumers and could be considered as a valid alternative
to solve asymmetric information problem caused from market failure.
The fact resulted from this study that Shanghai consumers do react to the energy
efficiency ranks presented on China Energy Efficiency Label is intuitive to both firm
leaders and policy decision makers. For the government, it is a great confidence to saythat environmental labeling is one of the effective policies to solve environmental issues.
For the firms, it may create an incentive to invest in green technology or make themmore confident in enlarging the scale of green production to meet consumers’ needs. An
unanswered question whether the effects of China Energy Efficiency Label on
consumers’ purchase decision found in Shanghai will also be found in other provinces ofChina is remained. We leave it as an open question and encourage a great effort tostudy this issue at much deeper extents.

References
Alpízar F., F. Carlsson, and P. Martinsson. 2003 Using choice experiments fornon-market valuation. Economic Issues 8: 83-110.
Amacher, G. S., E. Koskela, and M. Ollikainen. 2004. Environmental quality
competition

andeco-labeling.

JournalofEnvironmental

Economics

andManagement 47: 284-306.
Banerjee, A. and B. D. Solomon. 2003. Eco-labeling for energy efficiency andsustainability: a meta-evaluation of US programs. Energy Policy. 31: 109-123.
Bhat C. R. and R. Gossen. 2004. A mixed multinomial logit model analysis of weekend
recreational episode type choice. Transportation Research Part B 38: 767-787.
Bjørner, T. B., L. G. Hansen, and C. R. Russell. 2004. Environmental labeling andconsumers’ choice – an empirical analysis of the effect of the Nordic Swan. Journalof Environmental Economics and Management 47: 411-434.
Boxall, P. C. and W. L. Adamowicz. 2002. Understanding heterogeneous preferences in
random utility models: A latent class approach. Environmental and Resource

Economics 23: 421-446.
Carlsson F. and P. Martinsson. 2003. Design techniques for stated preference methods
in health economics. Health Economics 12: 281-294.
Cason, T. N. and L. Gangadharan. 2002. Environmental labeling and incomplete
consumer information in laboratory markets. Journal of Environmental Economics

and Management 43: 113-134.
China State Statistics Bureau. 2006. China Statistics Yearbook 2006.
Crespi, J. M. and S. Marette. 2005. Eco-labelling economics: is public involvement
necessary?. in S. Krarup and C. S. Russell (eds), Environment, Information andConsumer Behaviour. Edward Elgar Publishing.
18

Dosi, C. and M. Moretto. 2001. Is ecolabelling a reliable environmental policy measure?.

Environmental and Resource Economics 18: 113-127.
Fisher-Vanden, K., G. H. Jefferson, H. Liu, and Q. Tao. 2004. What is driving China’s
declined in energy intensity?. Resource and Energy Economics 26: 77-97.
Greene, W. H. and D. A. Hensher. 2003. A latent class model for discrete choice analysis:contrast with mixed logit. Transportation Research Part B 37: 681-698.
Hensher, D. A., J. M. Rose, and W. H. Greene. 2005. Applied Choice Analysis: A Primer.
Cambridge University Press.
Hess S., M. Bierlaire, and J. W. Polak. 2005. Estimation of value of travel-time saving
using mixed logit models. Transportation Research Part A 39: 221-236.
Howarth, R. B, B. M. Haddad, and B. Paton. 2000. The economics of energy efficiency:
insights from voluntary participation programs. Energy Policy. 28: 477-486.
Huber, J. and K. Zwerina. 1996. The importance of utility balance in efficient choice
design. Journal of Marketing Research 33: 307-317.
Johnston, R. J., C. R. Wessells, H. Donath, and F. Asche. 2001. Measuring consumer
preferences for ecolabeled seafood: an international comparison. Journal of

Agricultural and Resource Economics 26: 20-39.
Loureiro, M. L., J. J. McCluskey, and R. C. Mittelhammer. 2002. Will consumers pay a
premium for eco-labeled apples?. Journal of Consumer Affairs 36: 203-219.
Louviere J. J., D. A. Hensher, and J. D. Swait. 2000. Stated Choice Methods: Analysis

and Applications. Cambridge University Press.
McFadden D. 1974. Conditional logit analysis of qualitative variables in econometrics,
Zarembka P. (ed.), Frontiers in Econometrics, New York, Academic Press, 105-142.
McFadden, D. and K. Train. 2000. Mixed MNL models for discrete response. Journal ofApplied Econometrics 15: 447-470.
Moon, W., W. J. Florkowski, B. Brückner, and I. Schonhof. 2002. Willingness to pay forenvironmental practices: implications for eco-labeling. Land Economics 78: 88-102.
Morey, E., J. Thacher, and W. Breffle. 2006. Using angler characteristics and attitudinal
data to identify environmental preference classes: A latent-class model.

Environmental and Resource Economics 34: 91-115.
Morris, L. A., M. Hastak, and M. B. Mazis. 1995. Consumer comprehension ofenvironmental advertising and labeling claims. Journal of Consumer Affairs 29:
328-350.
Revelt D. and K. Train. 1998. Incentives for appliance efficiency in a competitive energy
environment: random parameters logit models of households’ choices. Review of

Economics and Statistics 80: 647-657.
Roe, B., M. F. Teisl, A. Levy, and M. Russell. 2001. US consumers’ willingness to pay forgreen electricity. Energy Policy 29: 917-925.
Russell, C. S., S. Krarup, and C. D. Clark. 2005. Environment, information andconsumer behaviour: an introduction. in S. Krarup and C. S. Russell (eds),

19

Environment, Information and Consumer Behaviour. Edward Elgar Publishing.
Shen, J. 2006. A Review of Stated Choice Method. International Public Policy Research
10(2): 97−121.
Shen, J. and T. Saijo. 2007. The socioeconomic determinants of individual
environmental concern: Evidence from Shanghai data. Discussion Paper in Osaka

School of International Public Policy (OSIPP), DP-2007-E-003, Osaka University.
Stern, P. C. 1999. Information, incentives, and proenvironmental consumer behavior.

Journal of Consumer policy 22: 461-478.
Swait, J. 1994. A structural equation model of latent segmentation and product choicefor cross-sectional revealed preference choice data.. Journal of Retailing andConsumer Services 1: 77-89.
Swait, J. 2007. Advanced choice models. in B. J. Kanninen (eds),Valuing Environmental

Amenities Using Stated Choice Studies. Springer.
Swait J. D. and Louviere J. J. 1993. The role of the scale parameter in the estimationand comparison of multinomial logit models. Journal of Marketing Research 30:
305-314.
Teisl, M. F., B. Roe, and R. L. Hicks. 2002. Can eco-labels tune a market? Evidence fromdolphin-safe labeling. Journal of Environmental Economics and Management 43:
339-359.
Thøgersen J. 2000. Psychological determinants of paying attention to eco-labels inpurchase decisions: model development and multinational validation. Journal ofConsumer Policy 23: 285-313.
Tietenberg, T. and D. Wheeler. 2001. Empowering the community: information
strategies for pollution control. in H. Folmer, H. Landis Gabel, S. Gerking and A.
Rose (eds), Frontiers of Environmental Economics. Edward Elgar Publishing.
Train, K. 1998. Recreation demand models with taste differences over people. Land

Economics 74: 230-239.

20

Figure 1. China Energy Conservation Label

Figure 2. China Energy Efficiency Label

21

Table 1. Air conditioner attributes and their levels in the survey
Attributes

Price (yuan)

Levels of attributes
Second-hand
New air
air conditioner
conditioner
BA
with foreign
with foreign
brandsbrands2500/3400/4300 1400/2000/2600

New air
conditioner
C
with domestic
brands
1800/2700/3600

Second-hand
air conditioner
Dwith domestic
brands800/1400/2000

Hourly electricity
consumption (kwh)

0.9/1.4

1.0/1.5

0.9/1.4

1.0/1.5

Applicable space (m2)

16/24

16/24

16/24

16/24

Air cleaning function

with/without

with/without

with/without

with/without

Energy efficiency
ranks on the label

1/2/3/4

1/2/3/4

1/2/3/4

1/2/3/4

Label of indicating
the electricity bill’s
difference comparing
to a standard model

presence/absencepresence/absencepresence/absencepresence/absence

Table 2. Refrigerator attributes and their levels in the survey
Attributes

Levels of attributes
Second-handNew
refrigerator
refrigerator
A
B
with foreign
with foreign
brandsbrands2300/3500/4700
1200/1800/2400

Daily electricity
consumption (kwh)

0.3/0.8

0.4/0.9

New
refrigeratorCwith domestic
brands
1600/2800/400
00.3/0.8

Volume (liter)

190/240190/240190/240190/240

Silent function

with/without

with/without

with/without

with/without

Energy efficiency
ranks on the label

1/2/3

1/2/3

1/2/3

1/2/3

Label of indicating
the electricity bill’s
difference comparing
to a standard model

presence/absencepresence/absencepresence/absencepresence/absence

Price (yuan)

Second-handrefrigerator
Dwith domestic
brands800/1400/2000
0.4/0.922

Table 3. An example of choice set from the choice experiment for air conditioner
New air
Second-hand
New air
Second-handconditioner
air conditioner
conditioner
air conditioner
A
B
C
D
Featureswith foreign
with foreign
with domestic with domestic
brandsbrandsbrandsbrandsPrice (yuan)
3400
2000
2700
1400
Hourly electricity
0.9
1
1.4
1
consumption (kwh)Applicable space (m2)
24
16
16
16
Air cleaning function
with
with
without
with
Energy efficiency ranks on
34
43
the label
Label of indicating theelectricity bill difference
absenceabsenceabsencepresencecomparing to a standard
model
Please choose one most□□□□desirable air conditioner
and ✔ in □Table 4. An example of choice set from the choice experiment for refrigerator
New
Second-hand
New
Second-handrefrigerator
refrigerator
refrigerator
refrigerator
A
B
C
D
Featureswith foreign
with foreign
with domestic with domestic
brandsbrandsbrandsbrandsPrice (yuan)
4700
1800
2800
1400
Daily electricity
0.8
0.9
0.8
0.4
consumption (kwh)Volume (liter)
240
190
240
190
Silent function
withwithout
withwithout
Energy efficiency ranks on
23
3
3
the label
Label of indicating theelectricity bill difference
absencepresence
presence
presencecomparing to a standard
model
Please choose one mostdesirable air conditioner□□□□
and ✔ in □23

Table 5. Information criterions for different numbers of latent classes#Par

Log Lik

AIC

ρ2

AIC3

BIC

1

21

-3212.07

6466.140.11296487.143249.412

48-3092.15

6280.30

0.13846328.30

3177.50375-2903.56

5957.120.18286032.123036.924102-2891.25

5986.500.17876088.503072.625129-2877.66

6013.33

0.17516142.33

3107.041

21-3804.55

7651.09

0.04507672.093841.892

48-3665.71

7427.420.07297475.423751.06

3

75-3511.26

7172.51

0.10487247.513644.624102-3485.127174.230.10457276.23

3666.495129-3463.93

7185.850.10317314.853693.311

21-3332.92

6707.83

0.10546728.83

3370.262

48-3110.65

6317.290.15756365.293196.00

3

75-2959.16

6068.310.19076143.313092.524102-2931.78

6067.56

0.19086169.56

3113.155129-2908.47

6074.930.18986203.933137.851

21-3949.71

7941.420.03707962.423987.052

48-3738.27

7572.540.08187620.543823.62375-3542.46

7234.930.12277309.933675.834102-3519.01

7242.020.12187344.023700.385129-3494.46

7246.91

0.12137375.913723.84

Classes

Air conditioner– Interview sample

Air conditioner– Web sample

Refrigerator– Interview sample

Refrigerator– Web sample

24

Table 6. Results of class membership for the 3-class LC model in Air conditioner case
Variables
Face-to-face interview
Web-based survey
Class 1
Class 2
Class 3
Class 1
Class 2
Class 3*****
constant
0.7163-1.139700.3502-1.18070(2.06)(-1.95)(0.96)(2.43)
old-age
0.7792**1.1201*0-0.5679-0.01400(2.24)(1.89)(-1.15)(-0.02)
mid-age0.7700**0.946800.5156*
0.52490(2.29)(1.63)(1.72)(1.41)
male-0.2597-1.0486***0-0.21240.23850(-1.02)(-2.60)(-0.91)(0.76)
high-inc-0.4042-0.4760
00.28150.5257**0(-1.23)(-0.88)(1.13)(2.06)
high-edu
0.5909**0.356500.2359**0.05210(2.11)(0.81)(2.35)(0.11)
Notes: t statistics are in parentheses.*,**, and*** denote that the parameter is significantly
different from zero at the 10%, 5%, and 1% level, respectively.

Table 7. Results of class membership for the 3-class LC model in Refrigerator case
Variables
Face-to-face interview
Web-based survey
Class 1
Class 2
Class 3
Class 1
Class 2
Class 3
constant
0.2132-0.1094
00.60940.54970(0.65)(-0.28)(1.57)(1.38)
old-age
0.7247**
0.48150-0.3756-0.28480(2.24)(1.24)(-0.70)(-0.51)
mid-age
0.6864**0.300600.0847-0.38380(2.19)(0.77)(0.28)(-1.12)-0.6034**00.12610.1410**0male-0.3181(-1.34)(-2.12)(0.49)(2.05)
high-inc-0.0026
0.3363***0-0.5597**-0.5110*0(-0.01)(2.29)(-2.15)(-1.79)high-edu
0.4577*0.265500.6080*0.19230(1.80)(0.86)(1.82)(0.49)
Notes: t statistics are in parentheses.*,**, and*** denote that the parameter is significantly
different from zero at the 10%, 5% and, 1% level, respectively.

25

Table 8. Estimation results for Air conditioner choice in Face-to-face interview sample
Attribute
MNL
LC
Class 1
Class 2
Class 3
***
***
***
A_asc
6.4401(17.54)
8.5357(18.18)
5.6345(3.15)
5.6204(7.03)***
B_asc
0.7626(1.19)
1.7324(1.27)
21.3357(0.23)
0.2582(0.34)
C_asc
5.4774(17.00)***7.8559(18.43)***-7.3070(-0.24)
3.2838(5.36)***
A_price-0.0009(-16.58)***-0.0012(-17.13)*** -0.0015(-3.66)*** -0.0013(-9.96)******
A_elechour-0.9069(-5.72)-1.2009(-6.23)***-1.1493(-1.49)-1.2136(-3.43)***
A_space0.0070(0.71)
0.0204(1.71)*
0.0682(1.29)
0.0395(1.80)*
A_airclean
0.4489(5.71)***
0.3263(3.50)***1.7579(3.96)***
1.5817(8.82)***
***
***
**
A_energyrank-0.3115(-8.83)-0.3695(-8.80)-0.4200(-2.13)-0.1881(-2.48)********
A_difflabel
0.3185(4.07)
0.2593(2.74)
0.1126(0.25)
0.9784(5.44)***
B_price-0.0002(-1.07)
0.00001(0.24)-0.0017(-1.64)-0.0001(-0.85)
B_elechour-0.5591(-1.90)*-0.7077(-1.07)-20.1221(-0.21)-0.4504(-1.33)
B_space0.0190(1.05)-0.0718(-1.55)
0.1289(1.13)
0.0484(2.42)********
B_airclean
0.9008(5.70)
1.8120(3.56)-0.3970(-0.537)
0.9146(5.79)***B_energyrank-0.2290(-3.55)***-0.4190(-2.80)***0.4124(1.33)-0.3017(-4.73)***
B_difflabel
0.3699(2.48)**0.5347(1.49)-0.0781(-0.11)
0.2518(1.86)*
C_price-0.0005(-9.32)***-0.0006(-8.74)***-0.0023(-4.25)*** -0.0006(-6.32)*********
C_elechour-2.1471(-7.54)-1.4336(-7.70)-1.7735(3.41)***-1.0827(-3.66)***
C_space-0.0068(-0.71)-0.0142(-1.21)
0.0262(0.43)
0.0009(0.05)
C_airclean
0.5408(7.20)***0.3274(3.48)***10.7518(0.36)
1.3409(9.05)***
***
***
*
C_energyrank-0.2909(-8.68)-0.3971(-9.39)-0.3556(-1.79)-0.2677(-4.26)***
***
**
***
C_difflabel
0.2460(3.25)
0.2292(2.46)
1.2665(2.67)
0.2613(1.77)*
Class probability
0.726
0.092
0.182
Log-likelihood-3212.07-2903.56
Pseudo R2
0.119
0.203
Predictive power 50.75%
68.94%
Observations
3600
3600
*
Notes: t statistics are in parentheses. ,**, and*** denote that the parameter is significantly
different from zero at the 10%, 5%, and 1% level, respectively. Predictive power index refers
to the proportion of choices correctly predicted by the model.

26

Table 9. Estimation results for Air conditioner choice in Web-based survey sample
Attribute
MNL
LC
Class 1
Class 2
Class 3
***
***
***
A_asc
3.8303(11.41)
5.7310(11.63)
3.9483(4.19)
2.8898(5.46)***
B_asc
0.7476(1.44)
3.6905(2.89)***-14.6620(-2.48)** 0.6563(1.24)
C_asc
3.7008(12.29)***6.3605(14.30)***-0.8529(-0.69)
1.4017(3.29)***
***
***
**
A_price-0.0004(-8.33)-0.0007(-8.96)-0.0004(-2.32)-0.0005(-6.37)*********
A_elechour-0.9114(-6.13)-1.5300(-7.36)-0.5472(-1.18)-0.4509(-1.90)*
A_space0.0196(2.13)**0.0448(3.49)***0.0702(2.25)**0.0105(2.71) ***A_airclean
0.2793(3.78)***0.2478(2.45)**1.2440(6.95)***0.4029(3.02) ***
***
***
**
A_energyrank-0.1176(-3.57)-0.1709(-3.70)-0.1200(-2.19)-0.1295(-2.43)**
***
***
**
A_difflabel
0.4705 (6.39)
0.6185(6.09)
0.4805(2.14)
0.5833(4.91)***
B_price-0.0001(-0.71)-0.0001(-0.45)-0.0059(-2.61)*** -0.0004(-3.60)***
B_elechour-0.4580(-2.01)**-1.8711(-3.07)***-4.1496(-1.87)*-0.1432(-0.64)**
B_space0.0226(1.59)-0.0272(-0.77)
0.2710(2.12)
0.0291(2.06)******
B_airclean
0.2580(2.25)
0.7454(2.54)
0.5374(0.76)
0.2325(2.12)**
B_energyrank-0.1467(-2.96)***-0.2446(-1.99)**-0.3714(-1.05)-0.0999(-2.08)**
B_difflabel
0.6494(5.52)***
0.0634(0.22)
1.9459(1.79)*
0.8590(7.51)***
***
***
***
C_price-0.0003(-6.07)-0.0004(-5.97)-0.0003(-2.77)-0.0003(-3.18)***
***
***
**
C_elechour-0.8503(-5.95)-1.4546(-7.40)-1.1885(-2.38)-0.3822(-1.78)*
C_space0.0048(0.54)-0.0167(-1.33)
0.0576(1.79)*
0.0277(2.16)**
C_airclean
0.4139(5.82)***0.3081(3.10)***1.0767(6.77)***0.5031(4.80)***
***
**
***
C_energyrank-0.1412(-4.48)-0.0622(-2.43)-0.0655(-5.27)-0.2673(-5.79)***
***
***
***
C_difflabel
0.4884(6.83)
0.4784(4.84)
0.8179(3.40)
0.6163(5.94)***
Class probability
0.544
0.150
0.306
Log-likelihood-3804.55-3511.26
Pseudo R2
0.050
0.123
Predictive power 41.42%
60.69%
Observations
3600
3600
*
Notes: t statistics are in parentheses. ,**, and*** denote that the parameter is significantly
different from zero at the 10%, 5%, and 1% level, respectively. Predictive power index refers
to the proportion of choices correctly predicted by the model.

27

Table 10. Estimation results for Refrigerator choice in Face-to-face interview sample
Attribute
MNL
LC
Class 1
Class 2
Class 3
***
***
***
A_asc
3.5344(9.26)
9.4728(10.96)
8.1802(2.75)
1.1297(1.60)
B_asc
0.3148(0.49)-14.7612(-2.78)*** 5.5393(1.75)*
2.0669(3.28)***C_asc
4.1513(10.92)***8.4262(9.93)***7.9693(2.67)***2.6112(4.27)***
***
***
***
A_price-0.0005(-11.89)-0.0011(-12.56)-0.0002(-3.70)-0.0005(-5.93)***
***
***
***
A_elecday-1.9514(-12.32)-3.6563(-11.50)-2.0865(-8.26)-1.5420(-5.22)***
A_volume
0.0060(3.84)***0.0054(2.00)**
0.0105(3.87)***0.0051(1.80)*
A_silent
0.6660(8.50)***0.5763(4.42)***0.6546(8.63)***0.9152(6.13)***
***
***
***
A_energyrank-0.2538(-5.30)-0.5967(-7.16)-0.2949(-3.96)-0.1915(-2.23)**
***
***
***
A_difflabel
0.2344(2.99)
0.3939(2.81)
0.3775(3.06)
0.1932(1.29)
B_price-0.0002(-1.51)
0.0010(1.57)
0.0003(1.24)-0.0006(-4.39)***B_elecday-1.0160(-3.69)***-2.0241(-1.80)*-3.3638(-4.88)*** -0.6065(-2.46)******
B_volume
0.0043(1.65)
0.0676(3.37)
0.0038(0.74)-0.0037(-1.32)
B_silent
0.8950(6.44)***1.3620(2.48)**0.7843(2.86)***
1.0775(8.17)***
B_energyrank-0.2412(-2.84)***-0.6627(-1.78)*-0.2377(-2.30)**-0.1616(-1.88)*
B_difflabel
0.3211(2.32)**
0.7568(1.28)
1.2237(3.77)***0.1482(1.07)
***
***
***
C_price-0.0006(-15.05)-0.0014(-14.95)-0.0003(-3.91)-0.0005(-8.48)***
***
***
***
C_elecday-0.6398(-4.31)-1.1445(-4.05)-1.0933(-4.22)-1.1361(-4.80)***C_volume
0.0031(2.06)**
0.0106(3.58)***0.0046(1.65)*
0.0025(1.00)
C_silent
0.5941(7.94)***0.8959(6.80)***0.8481(6.34)***0.5439(4.90)***
***
***
***
C_energyrank-0.3733(-8.14)-0.5627(-6.49)-0.3028(-3.76)-0.5351(-7.33)***
**
**
***
C_difflabel
0.1721(2.33)
0.2938(2.21)
0.6479(4.98)
0.5770(4.98)***Class probability
0.506
0.269
0.225
Log-likelihood-3332.92-2959.16
Pseudo R2
0.111
0.211
Predictive power 49.14%
67.42%
Observations
3600
3600
*
Notes: t statistics are in parentheses. ,**, and*** denote that the parameter is significantly
different from zero at the 10%, 5%, and 1% level, respectively. Predictive power index refers
to the proportion of choices correctly predicted by the model.

28

Table 11. Estimation results for Refrigerator choice in Web-based survey sample
Attribute
MNL
LC
Class 1
Class 2
Class 3
***
***
*
A_asc
2.3200(6.57)
4.7116(7.87)
1.0719(1.79)
0.6148(0.73)
B_asc
1.0714(2.09)**-0.5693(-0.43)
2.4022(4.52)***-4.6923(-1.92)*
C_asc
2.3649(6.68)***3.6624(6.53)***
1.1533(2.13)**2.3198(2.44)**
***
***
***
A_price-0.0003(-7.63)-0.0006(-8.56)-0.0005(-6.86)-0.0003(2.24)**
***
***
***
A_elecday-1.0088(-6.86)-1.9038(-8.03)-1.5441(-6.11)-0.3500(-1.06)
A_volume
0.0041(2.77)***0.0030(1.34)
0.0055(2.16)**0.0168(4.82)***
A_silent
0.3175(4.32)***0.1313(1.16)
0.8627(6.90)***0.5068(2.90)********A_energyrank-0.0719(-1.71)-0.1280(-2.30)-0.0821(-2.08)-0.4023(-5.21)***
***
***
***
A_difflabel
0.2966(4.05)
0.3895(3.30)
0.3373(2.68)
0.5009(3.26)***
B_price-0.0001(-0.96)-0.0005(-1.92)*-0.0004(-3.46)*** 0.0001(0.18)
B_elecday-0.5726(-2.71)***-1.3560(-2.31)**-0.6853(-3.10)*** 0.8293(1.02)*
B_volume
0.0001(0.04)
0.0094(1.78)
0.0066(2.99)***0.0254(2.56)********
B_silent
0.5615(5.25)
0.2944(1.11)
0.6558(7.71)
0.8079(1.62)
B_energyrank-0.1830(-2.77)***-0.0540(-3.00)***0.0147(0.21)-0.7955(-2.95)***
B_difflabel
0.4019(3.72)***-0.1143(-0.43)
0.6964(6.39)***-0.0005(-0.00)
***
***
***
C_price-0.0003(-7.78)-0.0007(-10.88)-0.0004(-3.42)-0.0003(-3.00)***
***
***
***
C_elecday-0.5533(-3.94)-0.7116(-3.23)-1.0897(-4.76)-1.4043(-3.79)***C_volume
0.0033(2.32)**
0.0097(4.35)***0.0007(0.30)-0.0019(-0.52)
C_silent
0.4579(6.49)***0.4181(3.78)***0.9721(8.73)***0.3726(2.05)**
C_energyrank-0.1444(-3.34)***-0.2247(-2.06)**-0.2202(-3.17)*** -0.3405(-2.38)*****
C_difflabel
0.3229(4.60)
0.1132(1.05)
0.8433(7.43)***0.6273(3.43)***
Class probability
0.414
0.277
0.309
Log-likelihood-3949.71-3542.46
Pseudo R2
0.042
0.141
Predictive power 42.06%
61.61%
Observations
3600
3600
*
Notes: t statistics are in parentheses. ,**, and*** denote that the parameter is significantly
different from zero at the 10%, 5%, and 1% level, respectively. Predictive power index refers
to the proportion of choices correctly predicted by the model.

29

Table 12. Willingness to Pay (WTP) measures (yuan) for energy efficiency rank
Alternative
Latent class model
Class probability
weighted WTP
Class 1
Class 2
Class 3
Interview sample
Air conditioner A
307.92280.00144.69275.64Air conditioner B−−−−
Air conditioner C
661.83154.09446.17575.87
Refrigerator A
542.451474.50
383.00757.30Refrigerator B−−
269.33
60.60
Refrigerator C
401.931009.33
1070.20956.48Web sample
Air conditioner A
244.14300.00259.00257.07Air conditioner B−−
249.7576.42
Air conditioner C
155.50216.67891.00389.74
Refrigerator A
213.33164.201341.00
548.17
Refrigerator B
108.00−−44.71
Refrigerator C
321.00550.501135.00
636.10Notes: Signs of “−” denote that the estimated WTP measures are not statistically significant
due to the insignificance of the estimated parameters. WTP for energy efficiency rank refers
to willingness to pay for one rank upgrading in energy efficiency listed on the label such as
rank 3 to rank 2 or rank 2 to rank 1, etc.

Table 13. Direct elasticities of energy efficiency rank on choice probability
Alternative
Interview sample
Air conditioner A-0.288Air conditioner B-0.740Air conditioner C-0.316
Refrigerator A-0.317
Refrigerator B-0.459Refrigerator C-0.259Notes: The elasticities in the table are probability weighted.

Web sample-0.128-0.383-0.152-0.135-0.238-0.09030

Appendix 1. Socioeconomic characteristics of the sample
Characteristics
Face-to-face interview
n%Gender
Male
300
50.00
Female300
50.00
Age (years)
Below 20
0
0.00
20-29
120
20.00
30-39
120
20.00
40-49
120
20.00
50-59
120
20.00
Over 60120
20.00
Education level
Elementary school
Junior high school
Senior high school
Technical degree
Undergraduate degree
Graduate degree
Household annual income< 30,000 RMB
30,000-49,999 RMB
50,000-69,999 RMB
70,000-99,999 RMB
100,000-149,999 RMB
150,000-199,999 RMB>200,000 RMBHousehold size
1 person
2 persons
3 persons
4 persons
5 persons
Above 6 persons
Occupation
Fulltime-employed
Self-employed
Part time
RetiredStudent
Unemployed
Total observations

Web-based survey
n
%
283
317

47.17
52.83

22
401
125
20
25
7

3.67
66.83
20.83
3.33
4.17
1.17

8
93
168
43
273
15

1.33
15.50
28.00
7.17
45.50
2.50

0
4
34
35
488
39

0.00
0.67
5.67
5.83
81.33
6.50

99
163
129
100
70
21
18

16.50
27.17
21.50
16.67
11.67
3.50
3.00

44
77
115
146
121
55
42

7.33
12.83
19.17
24.33
20.17
9.17
7.00

30
117
313
87
42
11

5.00
19.50
52.17
14.50
7.00
1.83

27
122
322
62
61
6

4.50
20.33
53.67
10.33
10.17
1.00

350
42
20
145
36
7

58.33
7.00
3.33
24.17
6.00
1.17

522
1
6
2
62
7

87.00
0.17
1.00
0.33
10.33
1.17

600

100

600

100

31

Appendix 2. Definition of the variables
Variables

Definition

A_asc

Alternative specific constant of new air conditioner or refrigerator A withforeign brands

B_asc

Alternative specific constant of used air conditioner or refrigerator B withforeign brands

C_asc

Alternative specific constant of new air conditioner or refrigerator C withdomestic brands

price

Price of air conditioner or refrigerator

elechourHourly electricity consumption

elecday

Daily electricity consumption

space

Applicable space for air conditioner

volumeVolume of refrigerator

airclean=1 if the air conditioner is with air cleaning function

silent=1 if the refrigerator is with silent functionenergyrankRanks of energy efficiency presented on energy label

difflabel=1 if a label indicating the electricity bill’s difference comparing to a
standard model is presented

Old-age=1 if age of the respondent is above 56

Mid-age=1 if age of the respondent is between 36 and 55

Male=1 if the respondent is male

High-inc=1 if the respondent’s household annual income is higher than 100,000
yuanHigh-edu=1 if the respondent has at least an undergraduate degree32