Market segmentation and willingness to pay for green electricity among urban residents in China: The case of Jiangsu Province

Energy Policy 51 (2012) 514–523

Contents lists available at SciVerse ScienceDirect

Energy Policy journal homepage: www.elsevier.com/locate/enpol

Market segmentation and willingness to pay for green electricity among urban residents in China: The case of Jiangsu Province Lei Zhang n, Yang Wu School of Management, China University of Mining and Technology, Quanshan district, Daxue Road, Xuzhou 221116, PR China

H I G H L I G H T S c c c c c

The value of green electricity manifests itself primarily in the form of non-use value. The average WTP for green-e ranges from RMB 7.91 yuan/month to 10.30 yuan/month. The differences in demographic variables across varying WTP are significant. The marginal effects of demographic variables at the same WTP are different. Green-e is still a luxury product and Veblen effect exits in particular segment.

a r t i c l e i n f o

a b s t r a c t

Article history: Received 14 August 2011 Accepted 23 August 2012 Available online 13 September 2012

The objective of this study is to identify market segments and estimate the residents’ willingness to pay (WTP) for green electricity (green-e) in China for the large-scale promotion of energy projects from renewable sources that do not rely solely on energy policies. Based on an analysis of non-use values of green-e as well as the application of the contingent valuation (CV) method and payment card (PC) introduction technology, the average WTP ranges from RMB 7.91 yuan/month to 10.30 yuan/month (approximately US$ 1.15–1.51/month with an exchange rate of 6.83 yuan/US$ yuan/US$) for urban residents in Jiangsu Province. The current work also explores the differences in demographic variables across varying WTP amounts and the different marginal effects of demographic variables at the same level of WTP. The findings reveal that there are significant differences in demographic variables, such as level of education, household income and location of residence, across the population segments. Moreover, the finding that some respondents with high income and higher education prefer higher WTP amounts to lower WTP amounts suggests that green-e is a luxury product, and consequently, a Veblen effect exists in certain Chinese market segments. & 2012 Elsevier Ltd. All rights reserved.

Keywords: Green electricity Willingness-to-pay Market segmentation

1. Introduction Electricity is a basic force that drives the development of the national economy. The power generation energy mix in China consists mainly of thermal power (79%), hydro power (20%) and nuclear power (1%). Among these, thermal power is the predominant source, with 76% of the total generated by burning coal, which is responsible for resource depletion, alarming environmental impacts and climate change. Currently, the government is developing wind power, solar photovoltaic, biomass and other

Abbreviations: AGE, age; CV, contingent valuation; DC, dichotomous choice; EDU, education; GEN, gender; Green-e, green electricity; INC, income; LOC, location; Mlogit, multinomial logit; NOAA, national oceanic and atmospheric administration; OCC, occupation; PC, payment card; SERC, state electricity regulatory commission; WTP, willingness to pay n Corresponding author. E-mail address: [email protected] (L. Zhang). 0301-4215/$ - see front matter & 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.enpol.2012.08.053

renewable energy sources that can provide green electricity (green-e) without local air pollution or greenhouse gas emissions while simultaneously decreasing the country’s excessive dependence on fossil fuels. The cumulative installed wind generation capacity in China reached 25,800 MW in 2009, ranking second in the world. However, due to constraints brought about by lowlevel technology, the pool purchase price of green-e is RMB 0.51 yuan per KW h to 0.61 yuan per KW h (Li, 2011), while the pool purchase price of thermal power is RMB 0.37 yuan per KW h (SERC, 2010). Nevertheless, the green-e and thermal power end-user purchase prices are equal, at RMB 0.52 yuan per kW h. Thus, the benefits of green-e are insufficient to justify the cost, which makes green-e non-competitive in the electricity market. Currently, green-e development depends primarily on statesupported policies. Government subsidies provided to the wind power industry in 2009 reached RMB 4.7 billion yuan (SERC, 2010), stimulating the production of green-e. Nevertheless, other than these subsidies, few policy instruments have been

L. Zhang, Y. Wu / Energy Policy 51 (2012) 514–523

implemented for end-users and a forced quota obligation has been widely practiced for green-e distribution, leading to dissatisfaction and opposition on the part of the suppliers (Sina.com, 2010). These political and financial barriers inhibit an increase in the supply of green-e in the national grid, while lower market consumption hampers the rapid increase of green-e generating capacity in China. Thus, it is important to conduct research on this topic and to explore the market mechanisms of the green-e industry in China with respect to both theoretical and practical aspects. At present, some countries have attempted to establish voluntary green purchase systems, and these can be used as references. However, these voluntary programs are different from forced quota obligations because they place emphasis on voluntary acts, thus allowing environmentally conscious citizens to fulfill their social responsibilities by paying for electricity generated from renewable energy sources. Researchers in various countries have studied willingness to pay (WTP) programs using local residents as the subjects of their research. For instance, according to Farhar (1999), 52% to 95% of the respondents in the USA are willing to pay for green-e at a reasonable price, among who 70%, 38% and 21% are willing to pay US$ 5, 10 and 15, respectively. In Japan, the monthly household average WTP amount for green-e is US$ 17 (Nomura and Akai, 2004). The monthly average WTP amount ranges from US$ 8.5 to 21.95 in the USA, with an average value of US$ 6.13 (Roe et al., 2001). Meanwhile, the monthly household average WTP estimated in the Republic of Korea is US$ 1.8 (Yoo and Kwak, 2009). The WTP estimates vary substantially across countries due to significant differences in the levels of economic development and environmental awareness as well as social customs and cultural backgrounds, thus motivating us to study the WTP for green-e in China. Existing works have reported a positive WTP for green-e; nevertheless, the actual participation rate is lower than the expected figure. In a survey conducted in Sweden, 75% of the households seriously consider buying green-e, and approximately 40% of these households also consider paying a voluntary price premium. Despite such positive responses and the fairly modest price premiums (US 0.1 cents to 0.8 cents) in the Swedish market, only 1% of the households actually purchased green-e in 1998 (Swedenergy, 1999). In Finland, 30% of households are interested in green-e, but only a nominal 0.2% demonstrates purchasing behaviors (Salmela and Varho, 2006). In the UK, corresponding rates for interest in green-e and purchasing behaviors are lower at 25% and 0.07%, respectively (Lipp, 2000). The low participation rates indicate that the survey results based solely on the WTP are insufficient for motivating green-e suppliers to expand the market. In other words, the stated WTP for green-e as a new product does not necessarily translate into actual purchasing action. Purchasing action is categorized according to the following sequence order: innovators (2.5%), early adopters (13.5%), early majority (34%), late majority (34%) and laggards (16%) (Rogers, 2003). As seed customers, innovators play a critical role in the diffusion of new products, which is the most important step in identifying the innovator segment for developing the green-e market (Fuchs and Arentsen, 2002). Rowlands et al. (2003) note that those population segments with higher WTP amounts usually show certain demographic, attitudinal and social attributes. Specifically, he focuses on environmental concerns, egoistic reasons and altruistic behavior; however, he fails to explain the influence of demographic attributes on market segmentation even though he acknowledges the existing correlations among these factors. The purpose of the current research is to study the market segmentation and estimate urban residents’ WTP for green-e in China. More importantly, we employ the multinomial logit

515

(Mlogit) model to identify demographic characteristics with varying WTP amounts. The green-e market segmentation and the seed customers are identified based on this model. We find that the consumption behaviors of different population segments differ when individual contributing factors vary. Moreover, green-e is a luxury product in China that exhibits the Veblen effect. Accordingly, strategic recommendations are provided for government support mechanisms and for green-e suppliers’ marketing decisions. We use the payment card (PC) technique in the questionnaire rather than the conventional dichotomous choice (DC) method. Although the latter has been widely adopted in modeling market pricing behavior, it can only examine the probability of choosing different WTP amounts for the respondents, thus failing to shed light on the characteristics of a population segment with varying WTP amounts, which is a critical step in segmenting a market. The paper is organized as follows. Section 2 analyzes the nonuse values of green-e and the model of contingent valuation (CV) method. Section 3 describes the research methods and processes, including sampling and survey methods, questionnaire design, survey description and the Mlogit model. Section 4 presents the estimated results, including the WTP and the market segmentation. Section 5 presents the conclusions of the study and recommendations. 2. Non-use values of green-e and the CV method As a form of electricity, green-e is a source of energy itself and has the same use value as thermal power, but currently, it has more non-use values than use values based on the perspective of the customer. First, in the regulated markets such as China, consumers are not allowed to choose their electricity supplier, as all their power demand is satisfied by a uniform national grid that cannot distinguish among the different types of energy. Therefore, consumers have no way to actually own the usevalue of green-e, even if they are willing to pay for it. This is referred to as the abstract nature of green-e (Salmela and Varho, 2006). Second, in the above situation, the key factor that drives consumers to buy green-e is its environmental impact. As Jalas (2004) notes, when discussing ‘green’ consumerism, the emphasis is primarily on environmental issues. Considering many environmental goods characterized by non-excludability and positive externalities in consumption, the value of green-e manifests itself primarily in the form of social benefits, such as resource efficiency and environmental protection, as non-use values. According to the welfare economics literature, public goods are typically under-provided in the market place (Bergstrom et al., 1986), but with respect to green-e, the effects of crowding-out due to government intervention play only a minor role, while market mechanisms play a major role (Clark et al., 2003; Menges et al., 2005). Because concerns for the environment have had a profound impact on consumer behavior and this appears to be inconsistent with the type of utility-maximizing behavior assumed in standard economic models of consumer decisionmaking in households (EK and Patrik, 2008), the CV method is widely applied internationally to estimate the maximum utility of green-e. The CV method involves directly asking people, through surveys or interviews, how much they would be willing to pay for environmental benefits or resource conservation in a hypothetical market. This method is referred to as a ‘stated preference’ method. In the 1980s, the technique was widely acknowledged by the USA government, and it then became the most important and widely used method for estimating non-use values in the field of ecological and environmental economics (Bennett and Blamey, 2001).

516

L. Zhang, Y. Wu / Energy Policy 51 (2012) 514–523

One prerequisite to the application of the CV method is to establish a hypothetical scenario for the environmental product to be valued. The research steps include the following: Step 1: Establishing a hypothetical scenario; Step 2: Designing a survey questionnaire; Step 3: Acquiring information about personal WTP via a survey; Step 4: Estimating the mean WTP amount; and Step 5: Deriving the WTP curve. Of the five steps above, steps 2 and 4 are the critical ones. Step 2 is based primarily on the WTP format as established by the National Oceanic and Atmospheric Administration (NOAA). However, for step 4, the modeling of the mean WTP estimate varies according to two main alternative introduction techniques: the DC method and the PC technique. Using the DC approach, individuals are asked whether they would pay a specified amount for a given commodity, with possible responses usually being ‘yes’ or ‘no’. The PC technique designs an ordered array of bid amounts, which allows respondents to choose the maximum amount they are willing to pay. Given the environmental attributes associated with green-e, the CV method has been widely used to estimate the WTP amount of residents in many countries. Table 1 presents an overview of the research subjects, the introduction techniques and the key findings of those research programs. The DC approach considers all respondents as the research subject. However, it fails to shed light on the characteristics of a population segment with different WTP values. The PC introduction technique is used in this paper because it can successfully elicit the WTP amount of potential respondents and provide sample characteristic values for the purpose of market segmentation with different WTP values. Furthermore, the DC approach may lead to greater WTP values than the PC method and no evidence of range bias or midpoint bias are found with PC responses (Ryan et al., 2004).

3. Survey design and Mlogit model 3.1. Sampling and survey method In this paper, we chose to conduct our survey in Jiangsu Province for several important reasons. First, it is one of the

seven Gigawatt wind-power bases in China, and it is particularly rich in offshore wind resources. Second, Jiangsu Province is economically developed with a profound cultural background. Specifically, there are unique advantages in conducting green-e marketing in the province where green-e generation is proximally closer to the end-users. Third, there are 13 local municipalities located in the southern and northern parts of the province, across which there exist substantial regional variations. Accordingly, these reasons make Jiangsu Province a perfect study area that can provide the research program with sufficient sample information. The socio-economic and demographic characteristics of the nation and of Jiangsu Province are illustrated in Table 2. Given that our survey covers a large geographical area, we followed the sampling formula established by Scheaffer and Mandenhall (1990). The sample size is determined by: n¼

N þ 1, ðN1Þ  g 2

ð1Þ

where n is the sample size, N denotes the population of samples (individuals) and g represents sampling errors (in this case, 5%). The probability ratio to size sampling technique was applied to 13 local municipalities in Jiangsu Province. Out of a total of 3.08 million urban residents, 401 effective samples were required. This province-wide survey was initiated by sending questionnaires via e-mail and mail during the period of May to June 2010 to married urban residents between 18 and 60 years of age. E-mail surveys were administered through e-mail and the QQ platform, in which the questionnaires (in electronic format) were completed and returned through the Internet. Meanwhile, we also sent the survey via mail, through which the questionnaires (in printed format) were completed and returned through the postal service. The latter method was used to offset the drawback of the age of some respondents, which did not allow them to participate by e-mail. Of the 1250 original questionnaires that were distributed, a total of 1188 were returned, including 652 by e-mail and 536 by postal mail. Through a preliminary screening, 1139 valid responses were received for a response rate of 91.1%. The returned questionnaires were measured in terms of geographical distribution, thus meeting the requirements for an intensive study.

Table 1 Recent developments in the literature on green-e based on the CV method. Author

Country

Introduction technique

Modeling

Key findings

Wood et al., 1995 Ek and Patrik, 2008 Ek, 2005 Nomura and Akai, 2004 George et al., 2007 Yoo and Kwak, 2009 Roe et al., 2001

USA Sweden Sweden Japan USA South Korea USA

DC DC DC DC DC DC PC and DC

Probit Probit Logit Weibull Logit Spike Hedonic regression

WTP is correlated with the decrease in cancer cases WTP is affected by social pressure and personal responsibility WTP increases with age, income and information Mean value of WTP is estimated at approximately US$ 17/month Positive WTP value, although there is a tendency toward solar photovoltaic WTP is US$ 1.8/month Mean WTP is US$ 6.13/month

Table 2 Socio-economic and demographic characteristics of China and Jiangsu Province. Source: NBSC, 2010 and SINJ, 2010. Parts of the data are processed. Characteristics

Nation

Jiangsu Province

South Jiangsu

North Jiangsu

Cities Population (million) Urbanization rate (%) Number of households (million) Household size (person) Annual household income (thousand yuan) Annual household power consumption (KW h) Number of graduates (per thousand)

N/A 1334.7 46.5 389.1 3.43 38.2 1138.4 N/A

13 74.0 58.2 24.0 3.08 62.4 1348.0 412.8

5 23.5 67.9 7.9 2.95 73.7 2238.5 290.6

7 50.5 48.5 16.1 3.14 51.1 906.1 122.2

L. Zhang, Y. Wu / Energy Policy 51 (2012) 514–523

3.2. The questionnaires According to the CV method implementation guidelines set by NOAA, the survey questionnaire must be designed to include (i) introductory descriptions of green-e, its environmental benefits, the challenges faced by the industry and the objectives of the survey; (ii) perceptions and attitudes of the respondents toward green-e; (iii) introductory techniques used to estimate the maximum WTP value of the respondents; and (iv) provisions for demographic information, including gender, age, occupation, education, household income and geographical location. As green-e cannot be directly perceived by the residents, we have no proper ways to assess its non-use value as a market good. For this reason, we designed a near real-life hypothetical market, which reduced the perceived WTP error of the respondents. Thus, we assume that the development and promotion of green-e is a major breakthrough required by Jiangsu Province to ride out the crisis because it is heading for an energy crunch brought about by the rapid social and economic developments it is experiencing. Second, we offer explanations for the sources of green-e and the important roles that green-e plays in mitigating the energy crisis and in protecting the environment. Two color photos of wind and solar photovoltaic power generation were included to enhance the perceptions of the respondents with respect green-e. Finally, we conclude that Jiangsu Province is endowed with significant amounts of potential green-e resources. Therefore, we conclude that green power industrialization should be prioritized. Moreover, due to cost constraints, green power requires the collective participation of various industry players. According to NOAA (1993), the CV method is likely to create anchoring and other types of bias. Two types of bias specifically identified as potentially arising when implementing the PC technique are range bias and mid-point bias (Mitchell and Carson, 1989).

517

Although they were not supported by previous empirical studies (Hanemann, 1989; Rowe et al., 1996; Ryan et al., 2004; Blaine et al., 2005), we adopted some measures introduced in the above literature to avoid these biases. That is, we established the starting point and its interval through a pilot survey and then used a maximum bid amount that did not exceed the average electricity bill of urban residents (RMB 30 yuan) in Jiangsu Province in 2009. The questions stated below were included in the beginning of the questionnaire to distinguish the respondents who did not display a WTP. (1) Are you willing to pay for green-e development from your household income? A. yes B. no If the respondent chooses ‘yes,’ the following item must be answered: (2) The maximum price you are willing to pay per month approximates to A. RMB 1 yuan B. RMB 2 yuan C. RMB 5 yuan D. RMB 10 yuan E. RMB 20 yuan F. over RMB 25 yuan

3.3. Descriptive statistics Table 3 represents the profiles of the respondents and the characteristics of individual variables involved in the valid responses. A cross check of selected survey demographics compared to the Jiangsu statistics show the survey sample to be representative of the Jiangsu population. The respondents generally show good environmental protection behavior. Those who occasionally or frequently use biodegradable shopping bags account for 71.93% of the respondents; if positively influenced by neighbors and fellow workers, the percentage of those who are willing to use green-e increases to

Table 3 Survey results and variable definitions. Source: Data of Jiangsu Province distribution is from SINJ, 2010. Variable (n¼ 1139)

Code

Units

Survey frequency and distribution

Jiangsu province distribution

Gender

GEN

Age

AGE

Occupation

OCC

1¼ Female 2¼ Male 1¼ 18–25 2¼ 26–35 3¼ 36–45 4¼ 46–55 5¼ 55–60 1¼ Professionals (lawyer, accountant, doctor, engineer, teacher, etc.) 2¼ Civil servants 3¼ Corporate employees 4¼ Self-employed and others 1¼ Elementary 2¼ High school (including polytechnic schools) 3¼ College 4¼ Postgraduate 1¼ Minus RMB 800 yuan 2¼ 800–1,600 3¼ 1,600–3,000 4¼ 3,000–5,000 5¼ 5,000–10,000 6¼ 10,000 plus 1¼ South Jiangsu 2¼ North Jiangsu 0¼No willingness to pay 1¼ RMB 1 yuan 2¼ RMB 2 yuan 3¼ RMB 5 yuan 4¼ RMB 10 yuan 5¼ RMB 20 yuan 6¼ Over RMB 25 yuan

585 554 104 531 267 140 97 326

(51.4%) (48.6%) (9.1%) (46.6%) (23.5%) (12.3%) (8.5%) (28.6%)

50.04% 49.96% N/A N/A N/A N/A N/A 21.90%

98 577 138 14 747 216 162 39 187 399 309 166 39 532 607 340 108 62 160 250 155 64

(8.6%) (50.8%) (12.0%) (1.2%) (65.6%) (19.0%) (14.2%) (3.3%) (16.3%) (35.4%) (27.1%) (14.5%) (3. 4%) (46.7%) (53.3%) (29.8%) (9.5%) (5.4%) (14.1%) (21.9%) (13.7%) (5.6%)

7.48% 55.23% 14.21% 5.24% 60.9% 17.2% N/A 5.0% 10.0% 40.0% 30.0% 10.0% 5.0% 40.2% 59.8% N/A N/A N/A N/A N/A N/A N/A

Education

EDU

Household income (in yuan)

INC

Geographical location

LOC

Willingness to pay

WTP

518

L. Zhang, Y. Wu / Energy Policy 51 (2012) 514–523

Table 4 Reasons for rejecting green-e. Reasons (multiple choice)

Percentage (%)

1. Does not know how to purchase green-e. 2. Does not want to spend extra time and effort in green-e consumption. 3. Unsure of whether green-e can be delivered to my household. 4. Unsure of the renewable resource the green-e they purchased came from. 5. Use of green-e does not affect the normal use of household appliances. 6. Monthly green-e bill is an economic burden for the household.

40.1 57.4 37.5 39.6 51.8 72.0

90.79%. However, when asked about their willingness to support the promotion of green-e using part of their household income, those who expressed a WTP decreased to 799 (70.1%), while 340 respondents (29.9%) rejected the idea. The reasons for rejecting green-e are presented in Table 4. Of all respondents, 242 (72.02%) reject green-e due to low income, in which case it can be considered as ‘0’ WTP. Other respondents reject green-e because they have little understanding of green attributes and/or green consumption patterns. These results indicate the importance of promoting green awareness programs. 3.4. The Mlogit model A regression analysis of WTP and demographic variables is an effective means for assessing the validity and veracity of the CV method. It is also the primary research tool used to investigate market segmentation. Goett et al. (2000) suggest a nonlinear exponential fit of the relationship and the great majority of recent works have focused on the DC technique using a logit or probit model in conducting regression analysis. However, few attempts have been made to study the functional models using the PC technique. To this end, we consider the WTP options as an unordered choice series and use the Mlogit model to address this issue. For convenience, we define J ðj ¼ 1,2,    ,6Þ as those WTP variables, such as RMB 1 yuan, RMB 2 yuan, etc., and x as a vector to reflect respondent characteristics, such as AGE, INC, EDU, etc. (see Table 3 for a complete description). According to the utility theory, the utility function can be described as Eq. (2) when the consumer i ði ¼ 1,2,    ,NÞ prefers to choose j category. U ij ¼ bj xi0 þ eij ,

i ¼ 1,2,    ,N;

j ¼ 1,2,    ,6

0

1þ

ebj xi P5

;

k¼1

ebj xi

,

jak

ð3Þ

The Mlogit model can then be described as Eq. (4). 0

ln

P ij ebj xi ¼ ln b x0 ¼ ðbj bk Þxi0 ¼ bj xi0 Pik e j i

k aj

Following this principle, Eq. (4) can be re-specified as the following Eq. (6)   P ij ¼ bj xi0 ¼ bð0Þj þ bð1Þj ðGEN1 GEN2 Þ ln P ik X5 b ðAGEi AGE3 Þ þ i ¼ 1 ði þ 1Þj X3 b ðOCC i OCC 1 Þ þ i ¼ 1 ði þ 5Þj X3 b ðEDU i EDU 3 Þ þ i ¼ 1 ði þ 8Þj X5 þ b ðINC i INC 3 Þ i ¼ 1 ði þ 11Þj þ b17j ðLOC 1 LOC 2 Þ þ ej

ð6Þ

where GEN2, AGE3, OCC1, EDU3, INC3 and LOC2 are the referential instances. For instance, if we take EDU as an example, the value of EDU3 (college) is zero, and if the educational background of one respondent is elementary, then the value of EDU1 is 1. Furthermore, we can calculate the marginal effect of respondent characteristics on primary choice. That is, as one of the consumer’s characteristics xi (such as AGE) changes one unit, how does the probability of choosing j category as a primary choice react? This situation can be depicted as Eq. (7). !0 ! 0 5 X @P ij ebj xi ¼ ¼ P ij bj  bk P ik ð7Þ 0 @xi 1 þ S5k ¼ 1 ebj xi k¼1

ð2Þ

where bj denotes the coefficient (or estimator) of respondent characteristics when j category (bid amount) is the primary choice and eij is the random error, which obeys normal distribution. According to the hypothesis of discrete choice model, the reason that consumer i prefers to choose j category is because the utility of RMB j yuan is greater than the utility of a reference, such as RMB k yuan. Define Pij as the maximum probability of the utility value in the above situation and yij as the decision variable, and if Uij 4Uik, then yij ¼1; otherwise, yij ¼0. Following a wellestablished procedure of setting bk to zero, we obtain the probability function specified in Eq. (3) (Long, 1997). P ij ðyij ¼ 1Þ ¼ PðU ij 4 U ik Þ ¼

Specifically, if bj 40 and Pij 4Pik, then consumer i tends to choose RMB 5 yuan as his/her WTP rather than RMB k yuan, and this tendency will be gradually strengthened as xi increases. On the other hand, if bj o0, then Pij oPik. It also means that consumer i prefers RMB k yuan as his/her primary choice, and this preference will be more obvious as xi increases. When Eq. (4) is estimated, all explanatory variables in Table 3 are binary. To deal with the mutually exclusive and exhaustive properties of each binary (dummy) characteristic, we adopt a method of restricting the sum of the coefficients to zero for each dummy variable (Briz and Ward, 2009). This method is convenient as each estimated coefficient can be expressed relative to an average respondent rather than to a base set of characteristics. For example, suppose that one of the characteristic has four dummies (D1, D2, D3 and D4), we obtain: X4 X3 X4 d ¼ 0 or d4 ¼  d , so d ðDj Þ j¼1 j j¼1 j j¼1 j X3 ¼ d ðDj D4 Þ ð5Þ j¼1 j

ð4Þ

which reflects the relative probability of consumer i preferring j category (such as RMB 5 yuan) to the reference (RMB k yuan).

4. Estimation of WTP and market segmentation 4.1. Estimating WTP Fig. 1 shows the probability distribution of different bid amounts obtained from the 799 questionnaires with positive WTP responses. Of these, 13.52% of the respondents chose the initial bid amount of RMB 1 yuan; this percentage is 7.76% higher than those who chose RMB 2 yuan. However, these results conflict with those obtained using the DC technology (Nomura and Akai, 2004; Farhar, 1999). Thus, a positive WTP does not follow monotonic decreasing distribution on balance; rather, it displays near normal distribution. In this way, the starting point bias, which often occurred when most respondents chose the lower bid amounts (i.e., initial bid amount), can be reduced. Based on the data, 31.29% of the respondents are willing to pay RMB 10 yuan/ month for green-e and approximately 20% are willing to pay RMB 5 yuan/month, while another 20% are willing to pay RMB 20

L. Zhang, Y. Wu / Energy Policy 51 (2012) 514–523

income and city of residence) with the help of the statistical software package, STATE/SE11.0 and the Mlogit model. We then perform the market segmentation.

40.00%

Probability

31.29%

30.00% 20.03%

20.00%

19.40%

13.52%

10.00%

7.76%

8.01%

0.00% 1

519

2 5 10 20 over 25 Willingness To Pay (Unit: yuan)

Fig. 1. Probability distribution of Jiangsu Urban Residents’ WTP for green-e.

4.2.1. Demographic differences across varying WTP Following Eq. (4) and using different WTP as references, we obtain 30 regression equations that provide the critical contributing factors for determining the consumer’s primary choices. For example, as far as the group of 10 versus 1, the regression equation is:   P 10 ln ¼ 2:62210:1767GEN 1 þ0:1006AGE1 þ 0:03688AGE2 P1 þ 0:0324AGE4

yuan/month. The aggregate percentage of those who are willing to pay RMB 5, 10 or 20 yuan is approximately 70%. The expected WTP in the PC questionnaire obtained through discrete variables is expressed by Xn EðWTPÞ ¼ AP ð8Þ i¼1 i i

ð2:57nn Þ ð0:67Þ ð0:75Þ ð0:22Þ ð1:27Þ þ 0:0720AGE5 0:0790OCC 2 0:0759OCC 3 0:1270OCC 4 ð0:74Þ ð0:78Þ ð0:82Þ ð1:15Þ 0:1635EDU 1 0:1022EDU 2 þ 0:0507EDU 4 0:0917INC 1 ð1:12Þ ð0:79Þ ð0:31Þ ð0:87Þ 0:1677INC 2 þ 0:1296INC 4 þ 0:3260INC 5 þ 0:3365INC 6

where Ai is the bid amount, Pi is the frequency of different bid amount options and n is the number of bid amount options (in the present paper, n¼ 6). By calculation, the mean WTP of the urban residents of Jiangsu Province is RMB 10.30 yuan/month (US$ 1.51/ month). It must be noted that this calculation method considers only positive WTP, whereas zero WTP is taken into consideration in the DC models, such as the logit or probit model. To resolve this issue, Kritrom (1997) proposes the Spike model, which is applicable to both open-ended and DC questionnaires. As the methodology for obtaining the mean WTP estimate is similar to that of the PC and open-ended questionnaires, the Spike model is used to refine the results of the mean WTP estimation in this paper. The refined E(WTP)non-negative is obtained by multiplying E(WTP)positive and the percentage of positive WTP to the total WTP, i.e., E(WTP)non-negative ¼10.30  (799/(799þ242))¼RMB 7.91 yuan/ month (US$ 1.15/month). Considering that the stated zero WTP does not necessarily translate to an actual zero value and is actually too nominal to identify, it may well be that E(WTP)nonnegative and E(WTP)positive are the lower and upper ends of the range of WTP estimates, respectively. It can be concluded that the WTP of urban residents in Jiangsu Province range from RMB 7.91 yuan/month to 10.30 yuan/month (US$ 1.15–1.51/month). The total social benefits can be further calculated. Although the survey focuses on individual respondents, the WTP is elicited on a household basis where no other household member is willing to pay a price premium for green-e. In 2009, approximately 13,446,700 urban households (SINJ, 2010) were recorded in Jiangsu Province. Multiplying this by the mean WTP, the total social WTP is RMB 138,501,000 yuan/month (US$ 20,278,330/ month), with an interval ranging from RMB 106,363,400 yuan to 138,501,000 yuan (US$ 15,572,972/month to 20,278,330/month). Compared with foreign literature, the average WTP US$ 1.51 in the Jiangsu urban households is lower than the average WTP recorded in the USA (US$ 6.13/month) and Japan (US$ 17/month) and is generally equal to that of Korea (US$ 1.8/month). However, viewed from the perspective of total social value, the total WTP in Jiangsu Province (US$ 243.6 million per year with 13,446,700 urban households) alone is greater than that of Korea (US$ 157.5 million per year with 7,462,090 households), indicating that the green power industry in China can bring about significant potential social and economic benefits.

ð1:35Þ ð1:68Þ þ 0:3004LOC 1

4.2. Segmentation of the green-e market In the following sections, we explore various contributing factors to WTP (e.g., gender, age, occupation, education, household

ð2:96n Þ

ð3:72nn Þ

ð1:62Þ ð9Þ

We find that only intercepts INC5 and LOC1 are statistically significant at the level of 1% and 5%, respectively, which indicates that the people who choose RMB 10 yuan as their primary choice are obviously different from those who choose RMB 1 yuan with respect to income and location. For clarification and conciseness, the regression results can be rearranged in Table 5. We find that the critical factors are different in different comparisons. For example, in the group of 5 versus 1, only one factor is significant, that is, INC5, whose coefficient’s value is 0.2608, while in the group of 10 versus 1, INC5 and LOC1 are the two critical factors whose coefficient values are 0.3260 and 0.3004, respectively. This finding implies that the factors that determine a consumer’s primary choice are not the same. Furthermore, an interesting phenomenon is that when one bid amount compares with a certain reference, the estimated result is equal to one (written in italics) when the latter compares with the former, though the signs of the coefficients are opposite. For example, in contrast to the group of 10 versus 1, INC5 and LOC1 are still significant, and their coefficients’ values change to  0.3260 and  0.3004 in the group of 1 versus 10. This finding means that the principle behind them is the same. In the above example, as the coefficient of INC5 is positive in the group of 10 versus 1, the consumer prefers RMB 10 to RMB 1, while in the group of 1 versus 10, the consumer tends to choose RMB 10, as the coefficient of INC5 is negative in this group. Thus, we analyze only the estimated results to the right of the dashes. Considering the implication of coefficients described in Eq. (4), we draw the following conclusions. (1) With the exception of WTP of RMB 2 yuan, the choice of WTP that is other than RMB 1 yuan is primarily determined by household income; thus, the higher the bid amount is, the higher the significance level. This finding clearly explains the difference in WTP for green-e between higher and lower household incomes, and it suggests that an increase in household income (denoted by INC¼5 or 6) can promote the WTP for these residents. The geographical location plays an important role as well. If the consumer lives in South Jiangsu (denoted by LOC¼1), he/she tends to choose RMB 10, 20 or over 25 as his/her WTP. A likely reason for the higher WTP in South Jiangsu is that this region is more developed than North Jiangsu in economy, history, culture and environmental protection awareness (see Table 2).

520

L. Zhang, Y. Wu / Energy Policy 51 (2012) 514–523

Table 5 Demographic differences between varying WTP values. Reference

1 versus reference

2 versus reference

5 versus reference

10 versus reference

20 versus reference

Over 25 versus reference

1

—

nil

INC5 0.2608n

INC5 0.4671nn LOC1 0.3054n

2

nil

—

nil

INC5 0.3260nn LOC1 0.3004n EDU4 0.8379nn

5

INC5  0.2608n

nil

—

EDU4 0.7085nn

10

INC5  0.3260nn LOC1  0.3004n INC5  0.4671nn LOC1  0.3054n INC6  0.6359nn LOC1  0.3597n

EDU4  0.8379nn

GEN1 0.7622nn EDU4  0.6324nn EDU4  0.7085nn

GEN1  0.7622nn EDU4 0.6324nn —

AGE2  0.5477nn

INC6 0.6359nn LOC1 0.3597n EDU4 0.7056n INC5 0.4150n GEN1  0.7161n INC6 0.0375nn INC6 0.3098n

AGE2 0.5477nn

—

nil

nil

—

20 Over 25

EDU4  0.9140nn n

n

EDU4  0.7056 INC5  0.4150n

EDU4 0.9140nn

n

GEN1 0.7161 INC6  0.0375nn

INC6  0.3098

Notes: The critical factors are listed with their coefficients’ values in the table, and statistically insignificant variables are neglected. — represents the bid amount compared with itself. n

denotes a statistical significance level of 5%. denotes a statistical significance level of 1%. ‘nil’ indicates that no statistically significant variables exist.

nn

Table 6 Marginal effects of demographic factors with the same WTP. Bid amount

Gender

1 2 5 10 20 Over 25

0.0014 0.0151 0.0831  0.1142  0.0382  0.0237

Age (0.06) (0.84) (2.80nn) (  3.29nn) (  1.31) (  1.23)

0.0056 0.1103 0.0223  0.0459  0.0356  0.0023

(0.43) (2.64n) (0.83) (  0.96) (  1.22) (  0.65)

Occupation

Education

Household income

0.0070 0.0154 0.0006  0.0039  0.0112  0.0066

 0.0189  0.0425  0.0815 0.0747 0.0607 0.0076

 0.0406  0.0066  0.0110 0.0035 0.0300 0.0247

(0.63) (1.83) (0.05) ( 0.25) ( 0.84) ( 0.76)

( 0.86) ( 2.73nn) ( 2.99nn) (2.33n) (2.22n) (0.42)

( 3.43nn) ( 0.74) ( 0.76) (0.21) (2.14n) (2.76nn)

Geographical location  0.0305  0.0165  0.0034 0.0205 0.0135 0.0094

( 2.35n) ( 1.73) ( 0.21) (1.10) (0.86) (0.91)

Notes: The values in brackets are the z-test values of the corresponding coefficients. n

denotes a statistical significance level of 5%. denotes a statistical significance at the 1% level.

nn

(2) If RMB 2 yuan is taken as a reference and compared with a WTP of RMB 1 or 5 yuan, there is no substantial variation among the contributing factors. However, if RMB 2 yuan is compared with a WTP that is greater than RMB 10 yuan, the education factor is significant with a positive coefficient, which implies that higher-educated respondents have a higher WTP. A likely reason for the higher WTP among educated people is that education translates into a heightened sense of social responsibility, widespread environment awareness and greater perception of the non-use value of green-e. Meanwhile, household income remains an important contributing factor with respect to the highest bid amount. (3) When using RMB 5 yuan as a reference, we find the gender factor plays a significant role in RMB 10 or over 25 yuan with a negative signal, i.e., female (denoted by GEN¼1) respondents exhibit a preference for RMB 5 yuan and their WTP diminishes progressively as the amount of the payment increases. A likely reason for the lower WTP among women is that females often pay the household’s energy bills and care more about the price than males. Meanwhile, the non-use value attribute undermines or degrades the utility of green-e (Farhar and Coburn, 2000), which may lead females to refuse to pay a higher price premium for green-e. Moreover, the education of the respondents who choose RMB 10 and 20 yuan bid amounts is significantly different from those who are willing to pay only RMB 5 yuan. As this segment constitutes 50.69% of the WTP and the coefficients are positive, we can say that, at present, the majority of respondents with a high WTP are likely higher-educated males. Furthermore, we also find that household income is an important contributing factor to the highest WTP bid amount.

(4) In the upper range of RMB 10, 20 and over 25 yuan, age is a significant factor with a negative coefficient when we compare RMB 20 yuan with RMB 10 yuan. This result suggests that as age decreases (denoted by AGE¼2), the respondents are inclined to choose the higher WTP as their primary choice. The reasonable explanation is that green-e is a relatively new product that greatly appeals to youth who have higher levels of education and an increased concern for their health and the environment (Rogers, 2003). This result also echoes the findings of Rowlands et al., 2003. Moreover, household income is still significant when RMB 25 yuan is compared with RMB 10 yuan, and no variables are significant when we compare RMB 25 yuan with RMB 20 yuan.

4.2.2. Differences in the marginal effect of demographic factors with the same WTP To study the changes in consumption behaviors of respondents under varied demographic factors, we differentiated regression equations with respect to these factors according to Eq. (7). The results are presented in Table 6. We find that occupational factor is never statistically significant for all bid amounts and that other factors are not significant for certain bid amounts; however, there is a substantial difference across respondent segments between lower WTP below RMB 5 yuan and higher WTP above 10 yuan. For those respondent segments interested in lower payments, their WTP increases with respect to gender and age; that is, females and increased age result in higher WTP values (marginal utility coefficient is positive). Furthermore, their (females and increased age) WTP for small amounts does not increase with improved household

L. Zhang, Y. Wu / Energy Policy 51 (2012) 514–523

income, higher levels of education or better social and economic conditions, a finding that does not agree with previous survey results. Conversely, the marginal utility coefficient is negative, indicating a diminished WTP. In the case of higher bid amounts, the WTP decreases with respect to gender; that is, the marginal utility coefficient is negative for females. Meanwhile, the WTP amount increases with improved household income and higher levels of education (marginal utility coefficient is positive). Female, middle aged and elderly people tend to opt for relatively smaller bid amounts when gender and age are concerned, while males and young people are willing to pay higher bid amounts with respect to the same factors. These results are consistent with those of other studies (Rowlands et al., 2003; Zarnikau, 2003; Batley et al., 2001). However, the demographic variables reflecting social status, such as education level, household income and city of residence, show no consistency in terms of the choice for low or high bid amounts. Conversely, with improvements in these factors, those segments of the population who opt for lower bid amounts decrease their WTP (marginal utility coefficient is negative), while those who opt for higher bid amounts increase their WTP (marginal utility coefficient is positive). This is markedly different from the results of previous studies’, i.e., the WTP increases with improvements in household income and education level (Rowlands et al., 2003; Zarnikau, 2003; Batley et al., 2001; Yoo and Kwak, 2009). This case implies

521

substantial variations across the two population segments in terms of the aforementioned demographic variables. The proportions of the WTP for different categories of education and household income are illustrated in Figs. 2 and 3, respectively. Interpreting the figures in the scenario for the WTP below RMB 5 yuan, the percentage values of the population segment with lower levels of education and income are greater in relation to those with higher levels of education and income. This finding is in contrast with the scenario of the WTP above RMB 10 yuan. For example, with respect to RMB 1 yuan, the percentages of EDU3 and EDU4 are 10.2% and 12.6%, respectively, while the percentages for EDU1 and EDU2 are 25.0% and 17.8%, respectively. Meanwhile, the percentages for INC4, INC5 and INC6 are 10.5%, 10.6% and 0.0%, respectively, while those for INC1, INC2 and INC3 are 35.7%, 19.7% and 13.1%, respectively. The results for RMB 20 yuan is different than those for RMB 1 yuan, as the percentages for EDU3 and EDU4 are 31.8% and 39.3%, respectively, while those for EDU1 and EDU2 only account for 0.0% and 24.8%, respectively. Meanwhile, the percentages for INC4, INC5, and INC6 are 32.1%, 36.1% and 25.9%, respectively, while those for INC1, INC2 and INC3 only account for 14.2%, 30.3% and 30.9%, respectively. Thus, it can be inferred that the population segment with lower education levels and lower income levels tend to opt for a smaller bid amount, while those with higher education levels and higher income levels are willing to pay a higher bid amount.

70.00% Prim ary school 60.00%

High school University

Probability

50.00%

Graduate

40.00% 30.00% 20.00% 10.00% 0.00%

1

2

5 10 Willingness To Pay (Unit: yuan)

20

over 25 WTP

Fig. 2. Percentage of WTP for different education levels.

40.00% below 800

Probability

30.00%

800-1600

1600-3000

20.00% 3000-5000

10.00%

5000-10000

10000 above

0.00% 1

2

5 10 20 Willingness To Pay (Unit: yuan)

25

Fig. 3. Percentage of WTP for different household income levels.

522

L. Zhang, Y. Wu / Energy Policy 51 (2012) 514–523

A likely interpretation of this scenario can be attributed to the fact that, at present, green-e is considered a luxury and the Veblen effect exists, i.e., consumers display a greater preference for higher bid values (Leibenstein, 1950). Because there is no appreciable distinction between green-e and conventional power, such as thermal power, in terms of their usage characteristics, green power can be substituted by the latter at a more competitive price. Therefore, with respect to low income households, they consider green-e to be a common good and therefore prefer a low bid amount. Meanwhile, marginal gains from an increase in income increase if the increase is devoted to thermal power or daily necessities; thus, the marginal income effect of green-e is negative for this segment of the population. For those households with high education and high income levels, using green-e represents a fashionable and eco-friendly lifestyle, thus serving as a manifestation of high social status. Moreover, the higher the price of green-e is, the higher the degree of manifestation, which further translates into a higher WTP, thus favoring greater bid amounts. As household incomes increase, they are more willing to purchase green-e at high bid amounts; thus, the marginal income effect is positive. This gives rise to the phenomenon whereby the demand for green-e of the former population segment decreases with the decline in the price of green-e. Moreover, the demand for green-e of the latter population segment increases as the price of green-e increases. Green-e appears to be a luxury product with the Veblen effect in particular population segments in China. Drawing upon these findings allows the conduct of market segmentation for green-e and the identification of corresponding WTP as shown in Table 7. It can be found that in the group of RMB 1 yuan and RMB 2 yuan, the characters of respondents are the same. They are likely to be female and elderly and earn a low salary as their education is low, occupation is unstable and location is undeveloped. However, for the group of RMB 20 yuan and over 25 yuan, the situation is different. This market segment can be defined as welleducated young men who own stable jobs and high salaries, and most of them are from developed regions. The group of RMB 5 yuan and RMB 10 yuan can be seen in the buffer zone of the above two segments. The younger stable-job men from undeveloped regions prefer RMB 5 yuan as do those with a poor education and low incomes, and the elder unstable-job women from developed regions opt for RMB 10 yuan as do those who are well educated and have high incomes. These results indicate that the segmentation relies more on education, income and geographical location than on gender, age and occupation. We can also conclude that the respondents who choose RMB 20 yuan or over 25 yuan constitute the seed customers of green-e.

5. Summary and conclusion Although green-e offers environmental benefits, the actual sources of electricity delivered to the houses cannot be distinguished; thus, green-e is known for its non-use value, which can be estimated by the CV method. Different from previous studies,

this paper uses the payment card technology to determine the WTP estimate for green-e in China. Furthermore, the characteristics of a population segment with different bid amounts and market segmentation are explored using the unordered Mlogit model. The survey results suggest that the majority of respondents are willing to pay a monthly premium to support green-e, and the mean premium is RMB 10.30 yuan. Considering ‘protest zeroes’ WTP as refined by the Spike model, the mean WTP decreases to RMB 7.91 yuan, which is approximately equal to that of Korea and lower than those in the USA and Japan. Meanwhile, there is a statistically significant difference across the population segments with respect to education, household income and geographical location between those who choose RMB 10 yuan plus and those who choose minus RMB 5 yuan. Particularly, as household incomes improve, the former population segment is expected to show an increased WTP at a higher bid value, while the latter is expected to not increase their WTP even at a lower bid value. This finding suggests that green-e is still a luxury product and that the Veblen effect exists in particular population segments in China. While the survey results suggest that conditions for establishing a widespread, deregulated green-e market are readily available in China, at present, the deregulation of green-e markets is limited to developed countries, and the possibility of deregulating the green-e market in developing countries has yet to be evaluated. The survey results indicate that the majority of the Chinese people surveyed in the present study demonstrate positive environmental behaviors and are willing to provide important support to develop green-e. In Jiangsu Province, green-e suppliers obtained subsidies totaling RMB 787.12 million yuan in 2009 (SERC, 2010), according to existing subsidy policies. We report a positive WTP for green-e among Jiangsu residents ranging from RMB 1276.36 to 1662.01 million yuan, suggesting that the subsidy can be compensated by a deregulated green-e operation program. Thus, the green-e industry in China has the potential to achieve large-scale development. The survey also draws important implications for government participation in developing the green-e market. Apart from financial factors, ‘protest zeroes’ in WTP arise due to an absence of green-e awareness. A lack of knowledge about the green-e also contributes to ‘protest zeroes’. Moreover, misconceptions about green-e require comprehensive programs that can increase consumer awareness throughout China. As the consumer’s level of education has a significant impact on higher WTP among residents, the government should take more initiatives to improve public education on green-e and environmental awareness. The survey provides important recommendations for the operation of market-oriented green-e for suppliers. The survey results indicate the substantial variations in the current demand for green-e; therefore, green marketing companies should focus on market segmentation and introduce a flexible and multistep electricity price strategy (that is, for people in different income, education or other demographic brackets, green-e company can charge different prices) for different consumers because of the

Table 7 The characteristics of market segmentation for green-e. Characters

1

2

5

10

20

Over 25

Gender Age Occupation Education Household income Geographical location

Female Elderly Insecure Low Low Undeveloped

Female Elderly Insecure Low Low Undeveloped

Male Young Secure Low Low Undeveloped

Female Elderly Insecure High High Developed

Male Young Secure High High Developed

Male Young Secure High High Developed

L. Zhang, Y. Wu / Energy Policy 51 (2012) 514–523

Veblen effect existing in the green-e market. Specifically, more attention should be devoted to the seed consumers. A marketskimming pricing strategy (that is, the price is higher when green-e is first introduced to the market to gain profit, and then the price is gradually reduced) can also be considered because households are not sensitive to price change and still show a high WTP at a relatively high price level. With the current high cost of green-e, the participation of seed consumers plays an exceptionally important role in successfully launching the green-e market in China. Finally, it should be noted that some respondents may overstate their WTP in the survey. Once the green-e price is implemented, they may not pay as much for it, according to the practices in some developed countries. This consideration shows the direction for future study.

Acknowledgments This work is supported by the Natural Science Foundation of China (No. 71003097) and the Social Science Project of the Education Ministry (No. 09YJC630218). References Batley, S.L., Colbourne, D., Fleming, P.D., Urwin, P., 2001. Citizen versus consumer: challenges in the UK green power market. Energy Policy 29, 479–487. Bennett, J., Blamey, R., 2001. The Choice Modeling Approach to Environmental Valuation. Edward Elgar Publishing Inc, Massachusetts. Bergstrom, T., Blume, L., Clotfelter, C.T., 1986. On the Private Provision of Public Goods. Journal of Public Economics 87, 25–49. Blaine, T.W., Lichtkoppler, F.R., Jones, K.R., Zondag, R.H., 2005. An assessment of household willingness to pay for curbside recycling: a comparison of payment card and referendum approaches. Journal of Environmental Management 76, 15–22. Briz, T., Ward, R.W., 2009. Consumer awareness of organic products in Spain: an application of multinominal logit models. Food Policy 34, 295–304. Clark, C.F., Kotchen, M.J., Moore, M.R., 2003. Internal and external influences on pro-environmental behavior: participation in a Green Electricity Program. Journal of Environmental Psychology 23, 237–246. Ek, K., 2005. Public and private attitude towards Green Electricity: the case of Swedish Wind Power. Energy Policy 33 (13), 1677–1679. Ek, K., Patrik, S., 2008. Norms and economic motivation in the Swedish Green Electricity Market. Ecological Economics 68, 169–182. Farhar, B.C., 1999. Willingness to Pay for Electricity from Renewable Resources: A Review of Utility Market Research. NREL/TP.550.26148. National Renewable Energy Laboratory: Golden, CO. Farhar, B.C., Coburn, T.C., 2000. A Market Assessment of Residential Grid-Tied PV Systems in Colorado. NREL/TP-550-25283. National Renewable Energy Laboratory: Golden, CO. Fuchs, D.A., Arentsen, M.J., 2002. Green Electricity in the market place: the policy challenge. Energy Policy 30, 525–538. George, R.P., Allison, M.B., Joshua, M.D., 2007. Does willingness to pay for green energy differ by source? Energy Policy 35 (6), 3327–3334.

523

Goett, A.A., Hudson, K., Train, K.E., 2000. Customers’ choice among retail energy suppliers: the willingness-to-pay for service attributes. The Energy Journal 21, 1–28. Hanemann, W.M., 1989. Welfare evaluations in contingent valuation experiments with discrete response data: reply. American Journal of Agricultural Economics 71 (4), 1057–1061. ¨ ¨ Jalas, M., 2004 Kuluttajat Ymparist ojohtamisen Kohteina ja Osapuolina.In: Heis¨ kanen, E. (Ed.), Ymparist o¨ ja liiketoiminta. Gaudea-mus, Helsinki, Finland, pp. 211–226. Kritrom, B., 1997. Spike Models in Contingent Valuation. American Journal of Agricultural Economics 79 (4), 1013–1023. Leibenstein, H., 1950. Bandwagon, snob, and veblen effects in the theory of consumers’ demand. Quarterly Journal of Economics 64 (2), 183–207. Li, J.F., 2011. China Wind Power Outlook. China Environment Science Press, Beijing. Lipp, J., 2000. Policy considerations for a sprouting UK Green Electricity Market. Energy Policy 24 (1), 31–44. Long, S.J., 1997. Regression models for categorical and limited dependent variables. Sage publications, Thousand Oaks, CA. Menges, R., Schroeder, C., Traub, S., 2005. Altruism, warm glow and the willing-todonate for Green Electricity: an artefactual field experiment. Environmental & Resource Economics 31, 431–458. Mitchell, R.C., Carson, R.T., 1989. Using Surveys to Value Public Good: The Contingent Valuation Method. Resources for the Future, Washington, D. C. NBSC, 2010. China Statistics Yearbook. /http://www.stats.gov.cn/tjsj/ndsj/2010/ indexch.htmS. NOAA, 1993. Natural Resource Damage Assessments Under the Oil Pollution Act of 1990. Fed. Reg, 58, (10), 4601–4614. Nomura, N., Akai, M., 2004. Willingness to pay for Green Electricity in Japan as estimated through contingent valuation method. Applied Energy 78, 453–463. Roe, B., Teisl, M.F., Levy, A., Russell, M., 2001. US consumers’ willing to pay for Green Electricity. Energy Policy 29, 917–925. Rogers, E., 2003. Diffusion of Innovations, 5th ed. The Free Press, New York. Rowe, R.D., Schulze, W.D., Breffle, W.S., 1996. A test for payment card biases. Journal of Environmental Economics and Management 31, 178–185. Rowlands, I.H., Scott, D., Parker, P., 2003. Consumers and Green Electricity: profile potential purchasers. Business Strategy and the Environment 12, 36–48. Ryan, M., Scott, D.A., Donaldson, C., 2004. Valuing health care using willingness to pay: a comparison of the payment card and dichotomous choice methods. Journal of Health Economics 23, 237–258. SINJ, 2010. Statistics Yearbook of Jiangsu. /http://www.jssb.gov.cn/tjxxgk/tjsj/ tjnq/2011/index_212.htmlS. Salmela, S., Varho, V., 2006. Consumers in the Green Electricity Market in Finland. Energy Policy 34, 3669–3683. SERC, 2010. Regulatory Reporting on Electricity Tariff Enforcement and Settlement in 2009. /http://www.serc.gov.cn/zwgk/jggg/201009/t20100913_13544.htmS. Scheaffer, R.L., Mandenhall, W., 1990. Elementary Survey Sampling, fourth ed. Duxbrey Press. Sina.com, 2010. Wind Power’s Quota System: Investors Applaud While Power Companies Keep Silence. /http://finance.sina.com.cn/roll/20100524/ 00117987779S.shtml, 2010. ¨ el. Sveriges Elleverantorer, ¨ Swedenergy, 1999. Vad Tycker Elkunderna om Gron Stockholm, 3. Wood, L.L., Kenyon, A.E., Desvousges, W.H., Morander, L.K., 1995. How much are customers willing to pay for improvements in health and environmental quality? The Electricity Journal 8 (4), 70–77. Yoo, S.H., Kwak, S.Y., 2009. Willingness to pay for Green Electricity in Korea: a contingent valuation study. Energy Policy 37, 5408–5416. Zarnikau, J., 2003. Consumer demand for green power and energy efficiency. Energy Policy 31, 1661–1672.