Analyzing public awareness and acceptance of alternative fuel vehicles in China: The case of EV

Energy Policy 39 (2011) 7015–7024

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Analyzing public awareness and acceptance of alternative fuel vehicles in China: The case of EV Yong Zhang a,n, Yifeng Yu b,1, Bai Zou a,2 a b

School of Transportation, Southeast University, Jiangsu, Nanjing 210096, China School of Industrial and Systems Engineering, Georgia Institute of Technology, 755 Ferst Drive, Atlanta, GA, USA

a r t i c l e i n f o

abstract

Article history: Received 6 September 2010 Accepted 27 July 2011 Available online 31 August 2011

The aim of this paper is to analyze consumers’ awareness towards electric vehicle (EV) and examine the factors that are most likely to affect consumers’ choice for EV in China. A comprehensive questionnaire survey has been conducted with 299 respondents from various driving schools in Nanjing. Three binary logistic regression models were used to determine the factors that contribute to consumers’ acceptance of EVs, their purchase time and their purchase price. The results suggest that:

Keywords: Electric vehicle Willingness to pay Logistic regression model

(1) Whether a consumer chooses an EV is significantly influenced by the number of driver’s licenses, number of vehicles, government policies and fuel price. (2) The timing of consumers’ purchases of an EV is influenced by academic degree, annual income, number of vehicles, government policies, the opinion of peers and tax incentives. (3) The acceptance of purchase price of EVs is influenced by age, academic degree, number of family members, number of vehicles, the opinion of peers, maintenance cost and degree of safety. These findings will help understand consumer’s purchase behavior of EVs and have important policy implications related to the promotions of EVs in China. & 2011 Elsevier Ltd. All rights reserved.

1. Introduction 1.1. Development background of alternative fuel vehicles in China In recent years, great concern has been shown regarding the concentration of one major greenhouse gas (GHG)—carbon dioxide (Wang et al., 2007). In 2007, the transportation sector was responsible for 23% of total CO2 emissions worldwide (IEA, 2009a). With six billion tons of CO2 in 2007 (21% of global emissions), emissions in China far surpassed those of the other BRICS countries. In fact, China overtook the US in 2007 as the world’s largest emitter of energy-related CO2 (IEA, 2009b). The transportation sector, a major oil consumer and greenhouse gas (GHG) emitter worldwide, is the most rapidly growing energy sector in China, particularly in terms of oil demand and GHG emissions (Yan and Crookes, 2009a). In China, passenger and freight road transportation have increased by 8 and 15 times, respectively, during the last two decades (Wang et al., 2007). In

n

Corresponding author. Tel./fax: þ 86 25 8379 5384. E-mail addresses: [email protected] (Y. Zhang), [email protected] (Y. Yu), [email protected] (B. Zou). 1 Tel.: þ1 404 660 6857. 2 Tel./fax: þ 86 25 8379 5384. 0301-4215/$ - see front matter & 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.enpol.2011.07.055

particular, the rapid growth of private vehicles has resulted in continuing growth in China’s demand for oil. This growing demand have been widely accepted as a major factor affecting future oil availability and price as well as a major contributor to the increase in China’s GHG emissions (Yan and Crookes, 2009b). In 1990, there were only 0.8 million private vehicles in China, but in 2007, the total number of private vehicles reached 28.8 million; the stock of private vehicles therefore increased approximately 36 times between 1990 and 2007. If the current pattern continues, by the year 2030, there will be 400 million private vehicles in China (Hu et al., 2010). He et al. (2005) predicted that the annual oil demand of China’s road vehicles will reach 363 million tons by 2030. In 2009, the Chinese government promised to reduce carbon dioxide emissions per unit of GDP by between 40% and 45% by 2020 from the 2005 values. The Chinese government is making great efforts to curb petroleum demand and GHG emissions in the road transportation sector by introducing alternative fuel vehicles (AFVs) and regulating vehicle fuel economy. 1.2. Alternative fuel vehicle development in China As the use of private cars is growing rapidly in China in recent years, the government initiated efforts to promote the development

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of AFVs. The Chinese government has taken various actions to improve AFV technology to open markets for this technology. These actions include AFV promotion policies, funding for R&D and market demonstrations of technology (Ou et al., 2010a). The AFV industry, including pure electric vehicles, has developed under the control of Chinese government, which increased its investment in this technology from CNY800 million in the ‘‘10th 5-year plan’’ to CNY5 billion in the ‘‘11th 5-year plan’’, covering over 200 vehicle plants, suppliers and scientific research institutions. China also explicitly stated its short-term goal for AFVs in March 2009 in its announcement of the Adjustment and Revitalization of Auto Industry policy: to produce 500,000 EVs by 2011. In addition, a long-term national strategy for China’s new-energy automobile industry has been formed. The ‘‘Energy Conservation and New-energy Vehicle Development Plan (2011–2020)’’ (hereafter referred to as ‘‘the plan’’) that was charged by the Ministry of Industry and Information Technology has been completed, and the Ministry is seeking advice on the plan from all related ministries and commissions. The plan will be an important policy for the development of China’s new-energy automobile industry. In the draft of the plan, the Ministry of Finance plans to fund as much as CNY100 billion into energy conservation and the development of a new-energy automobile industry chain over the next 10 years. This will make China the world’s leader in new-energy vehicle industrialization and market scale by 2020. The population of new-energy vehicles, including plug-in hybrid electric vehicles, pure electric vehicles, hydrogen fuel cell vehicles and others, will be 5 million, and the sales of hybrid electric vehicles will rank first over the world at 15 million vehicles per year. Among the various types of AFVs, EVs were widely focused recently and represent one of the most effective emission-mitigation measures (Ou et al., 2010a; Safaei et al., 2009). For EVs, GHG emissions from electric driving depend mostly on the fuel type (coal or natural gas) used in generating the electricity for charging, and range between 0 g km  1 (using renewables) and 155 g km  1 (using electricity from an old coal-based plant) (Vliet et al., 2011). When carbon dioxide capture and storage (CCS) technology is employed in power plants, the GHG emissions reductions of electric vehicles (EVs) powered with coal-to-electricity in China can be 60–70% (Ou et al., 2010b). Furthermore, Ou et al. (2010b) indicated that high-efficiency EVs as well as the promotion of coal-based fuels coupled with CCS technology is a feasible option for China’s sustainable transportation energy roadmap, which can reduce fossil energy demand by 21.58% and GHG emissions by 15.61% in 2050. In China, EVs are also proposed as a transportation fuel solution and long-term strategies are being discussed to commercialize EVs (Ouyang et al., 2009). However, currently there are six major barriers to successful AFV implementation, including limited numbers of refueling stations, high refueling costs, onboard fuel-storage issues, safety and liability concerns, technologies and performances improvements in the competition and high initial costs for consumers (Romm, 2006). As a result of these barriers, there is currently a relatively low consumption level of EVs in China, thus posing a great hurdle to the development of the AFV industry. The Chinese government has taken great measures to promote the development of the electric automobile industry and released a series of policies that are favorable to the development of the electric automobile industry since 2009. On January 24th, 2009, China issued the Notice on Experimental Demonstration and Promotion of Energy Saving and Newenergy Automobiles (hereafter referred to as the ‘‘Notice’’). After discussion, Beijing, Shanghai, Chongqing, Changchun, Dalian, Hangzhou, Ji’nan, Wuhan, Shenzhen, Hefei, Changsha, Kunming and Nanchang became the demonstration and promotion cities for energy saving and new-energy automobiles, including the

implementation of a demonstration project that introduces 1000 EVs in 10 cities. On February 5th, 2009, the country promulgated the Interim Measures on the Administration of the Government Subsidy for the Demonstration and Promotion of Energy Saving and New-energy Automobiles. In May 2009, the Ministry of Finance, the Ministry of Science and Technology, the Ministry of Industry and Information Technology and the National Development and Reform Commission issued the Notice on the Experimental Subsidy for Purchasing Personal New-energy Automobiles, which signaled that the government was planning to develop the EV industry. A subsidy of as much as CNY50,000.00 per vehicle would be provided to plug-in hybrid electric vehicles and a subsidy of as much as CNY60,000.00 would be provided to pure electric vehicles. Some domestic automobile manufacturers (such as Chery, FAW, DFAC, BYD and Jianghuai) launched their own EVs. Vigorous construction of charging facilities has occurred in many cities such as Shenzhen, Shanghai and Wuhan. Compared with other AFV industries in China, China’s EV industry will suffer from various problems related to cell technology, automobile assembly costs, subsidy rate, charging facilities, policies and regulations. Experiences in other countries demonstrate that successful market introduction depends on the actions of many stakeholders such as the car industry, fuel companies and consumers (Janssen et al., 2006). It is now widely recognized that effective communication and demand-side policies for alternative energy require sound knowledge of the preferences and the determinants of demand among the prospective consumers (Roche et al., 2010). Hence, research on China’s electric automobile industry from the consumers’ perspective not only contributes to decision makers’ overall grasp of market demand, but also supports the decisions made by the government and private enterprises. 1.3. Review of the literature on the alternative fuel vehicle market AFVs, which are known for their environmental-friendly attributes, must be systematically analyzed from the viewpoint of consumers. With the development of infrastructure, it is important to evaluate the willingness of consumers to adopt cleaner vehicles (Potoglou and Kanaroglou, 2007). The potential demand for AFVs operated by electricity, compressed natural gas, methanol, or other ‘‘clean’’ fuels can be divided into residential (or personal use) demand and fleet demand (Golob et al., 1997). In recent decades, many studies have focused on the market analysis of AFVs from the consumers’ perspective, including demand preferences, forecasting, and policies. Some key studies were described in Table 1. According to Table 1, most of these previous studies examined AFVs sold in developed countries and regions such as North America and Europe. For example, many researchers have studied the demand markets of California from multiple perspectives. Researchers in California began to examine the demand for AFVs in 1993 (Bunch et al., 1993). A micro-simulation demand forecasting system was designed to produce annual forecasts of new and used vehicle demand by vehicle type and geographic area in California (Brownstone et al., 1996). Golob et al. (1997) investigated fleet demand for AFVs through an analysis of a survey in 1994 of 2000 fleet sites in California. Nesbitt and Sperling (1998) examined seven widely accepted hypotheses regarding the potential fleet market for AFVs and found a large number of misconceptions held by both fleet operators and policymakers that led to distorted expectations of AFV adoption. Brownstone et al. (2000) compared multinomial logit models with mixed logit models of data on California households’ revealed preferences and stated preferences for AFVs. Martin et al. (2009) discussed the behavioral responses to hydrogen fuel cell vehicles and refueling. Axsen and

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Table 1 Some key studies related to the market analysis of AFVs from the consumers’ perspective. Key papers

Method and methodology

Region/respondents/vehicle type

The aim of the paper

Bunch et al. (1993)

SP (stated preference) survey/BNL (binomial logit) model/NMNL (nested multinomial logit) model

Brownstone et al. (1996)

SP survey/MNL (multinomial logit) model

California/700 respondents/cleanfuel vehicle (electric vehicles, unspecified liquid and gaseous fuel vehicles) California/4747 respondents/EV

Parker et al. (1997)

mail questionnaire survey

Golob et al. (1997)

SP survey/binomial probit model/ ordered-response probit model/ MNL model One-to-one interviews

To determine how demand for clean-fuel vehicles and their fuel are likely to vary as a function of attributes that distinguish these vehicles from conventional gasoline vehicles. To construct vehicle choice model for producing annual forecasts of new and used vehicle demand by vehicle type and geographic area. To assess the interest and the future adoption of alternative fuels To analyze commercial fleets’ demand for alternative fuel vehicles.

Nesbitt and Sperling (1998) Brownstone et al. (2000)

US/139 trucking firms/alternative fuel California/a survey in 1994 of 2000 fleet sites/AFV California/over 2700 fleets/AFV

Mixed logit models/MNL model/ SP/RP survey

California/4747 households/AFV

Dagsvik et al. (2002)

SP survey/Random utility models

Norwegian/642 individuals/AFV

Mourato et al. (2004)

Contingent valuation survey/ regression method

London/99 taxi drivers/fuel cell vehicle (FCV)

O’Garra et al. (2005)

SP survey/BNL model

London/420 telephone interviews/hydrogen vehicles

Adamson (2005)

Regression analysis method

Janssen et al. (2006)

Stakeholder analysis/system dynamic modeling techniques/ balanced scorecard approach Review

Germany/2 publicly available databases/FCVs Switzerland/car supply chain/ natural gas cars

Lane and Potter (2007)

UK/reportsand research papers dating from 2000 to 2005,the open university’s (OU) design innovation, group/low carbon cars Berlin, London, Luxembourg and Perth/1358 respondents/ hydrogen (H2) FC buses Canada/426 respondents/clean vehicles

O’Garra et al. (2007)

Contingent valuation survey/ interval regression method

Potoglou and Kanaroglou (2007)

SP survey/NMNL model

Ahn et al. (2008)

Ma et al. (2009)

SP survey/Bayesian approach/ conjoint analysis/a multiple discrete-continuous choice model SP survey/AHP/Logit

Seoul, South Korea/280 respondents/alternative fuel passenger cars China/90 respondents/AFV

Martin et al. (2009)

Pre- and post-clinic surveys

Caulfield et al. (2010)

SP survey/MNL model/NMNL models

California/182 ‘‘ride-and-drive’’ clinic participants/(FCVs) Ireland/168 respondents/HEV or AFV

Erdem et al. (2010)

A web-based survey/ordered probit model

Turkey/1983 participants/hybrids

Mabit and Fosgerau (2011)

SP survey/mixed logit model

Axsen and Kurani (2011)

Observing method/logistic model

Denmark/2146 new-car buyers/ hydrogen, hybrid, bio-diesel, and electric vehicles California/10 households/plug-in hybrid vehicles

Kurani (2011) explored the role of interpersonal influence on car buyer’s perceptions of plug-in hybrid vehicles, and found that interpersonal influence plays an important role in participants’ assessment of plug-in hybrid vehicle technology. In addition, many studies were related to the AFV market research in European countries, such as UK (Mourato et al., 2004; O’Garra et al., 2005; Lane and Potter, 2007; O’Garra et al., 2007), Germany (Adamson, 2005; O’Garra et al., 2007), Norwegian (Dagsvik et al., 2002), Switzerland (Janssen et al., 2006), Turkey (Erdem et al., 2010) and Denmark (Mabit and Fosgerau, 2011).

To examine seven widely accepted hypotheses regarding the potential feet market for AFVs. To compare multinomial logit models with mixed logit models for data on California households’ revealed preferences and stated preferences for automobiles. To analyze the potential demand for alternative fuel vehicles. To understand the user benefits of fuel cell vehicles and the determinants of demand as well as to study taxi drivers’ WTP for FCV. To investigate the determinants of knowledge and acceptability of hydrogen vehicles among London residents. To model the consumers’ WTP for FCVs. To identify difficulties and chances in the market penetration process of natural gas cars, and to better measure the performance of the implemented strategy. To identify attitudinal barriers inhibiting the adoption of cleaner vehicles in the UK.

To compare public WTP for the air pollution reductions associated with a scenario of large-scale introduction of hydrogen (H2) FC buses in four cities. To examine the factors and incentives that are most likely to influence households’ choice for cleaner vehicles and to study WTP for clean vehicles. To analyze how adding alternative fuel passenger cars to the market will affect patterns indemand for passenger cars To analyze the weight of factors and establish Chinese NEV market share forecasting model. To address questions about consumer reactions to FCVs and explore the stated willingness-to-pay preferences. To examine individual motivations when purchasing vehicles, focusing on what factors would encourage individuals to purchase HEV or AFV. To determine the factors that have an impact on the consumers’ willingness to pay a premium for hybrid automobiles in Turkey. To investigate the potential future of alternative fuel vehicles in Denmark, where the vehicle registration tax is very high and large tax rebates can be given. To explore the role of interpersonal influence on car buyer’s perceptions of plug-in hybrid vehicles.

Compared with US and European Countries, the AFV market in China has been understudied. Although there are many studies related to AFVs concerning industry development, refueling stations, policies and technologies, in China insufficient number of studies have been undertaken to examine the AFV market from the consumer’s perspective. So far, only one paper has conducted forecast for the AFV industry (Ma et al., 2009). In these previous studies, various methods and skills have been used to evaluate and discuss the AFV’s market demand, such as SP survey, MNL, NMNL, BNL, contingent valuation survey,

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regression and so on. The combination of SP survey and various types of logit models were widely applied to describe the consumer’s preference for and adoption of AFVs (Bunch et al., 1993; Brownstone et al., 1996, 2000; Golob et al., 1997; Potoglou and Kanaroglou, 2007; Ma et al., 2009; Caulfield et al., 2010; Mabit and Fosgerau, 2011). Furthermore, it is worth pointing out that the exploration of the factors or incentives that could encourage families to purchase AFVs is important. This exploration will help develop new policies and help automakers gain an understanding of the consumer’s preference for AFVs. Many significant studies have been conducted. For example, Mourato et al. (2004) studied drivers’ preferences for fuel cell taxis and showed that the willingness to participate in a pilot project seems to be driven mostly by drivers’ expectations of personal financial gains and environmental considerations; Adamson (2005) suggested that price, running cost, safety and reliability are important adoption criteria of AFVs for consumers; O’Garra et al. (2007) compared public WTP for the air pollution reductions associated with a scenario of the large-scale introduction of hydrogen (H2) FC buses in four cities: Berlin, London, Luxembourg and Perth. Canadian researchers studied the WTP of consumers in Hamilton (Potoglou and Kanaroglou, 2007) and found that factors such as reduction in the monetary costs, tax incentives and low emission rates might encourage households to adopt a cleaner vehicle. In addition, access to fuel is also an important factor. Researchers in Turkey studied consumers’ WTP for hybrid vehicles (Erdem et al., 2010) and found that consumers with high income, academic degree and more concern about global warming are more willing to pay a premium for hybrid vehicles. Lane and Banks (2010) successfully identified the environmental metrics preferred by consumers and their preferences for how such data should be displayed. Table 2 gives some studies related to the factors influencing consumer decisions.

1.4. Research objective and scope Currently, there are no studies related to the demand pattern of AFVs from the consumers’ perspective in China. It is important to analyze demand characteristics and consumers’ preferences according to the differences among vehicle markets in various countries or regions (Roche et al., 2010). The EVs’ market differences between China and other countries were studied from various aspects such as EVs’ market scales, EVs’ supply chain, incentive policies, market penetration status, consumers’ demand and EVs’ technologies. The authors of

this paper conducted research on consumers’ preferences for EVs in Nanjing. The questionnaire in our research adopted the stated-preference approach and contained many factors that may contribute to consumers’ preference for EVs. The study established three binary logistic regression models to analyze factors contributing to consumers’ preference for EVs, purchase time for EVs and purchase price for EVs. It is expected that the results of this study will help promote the development of the Chinese AFV industry. The paper is structured as follows: Section 2 provides the research methodology, including stated preference, the binary logistic regression model, questionnaire design and data collection. Section 3 establishes the binary logistic regression model, provides descriptive results and discusses the results of the model. Finally, Section 4 provides study conclusions, limitations and prospects for future research.

2. Method 2.1. Questionnaire design With reference to studies conducted in other countries and regions on consumers’ preference for AFVs (Yeh, 2007), the structured questionnaire used in this paper mainly includes consumers’ socio-economic information, consumers’ adoptions of EVs and consumers’ awareness of the development measures pertaining to EVs. In addition, the authors conducted a pilot survey before finalizing the questionnaire. With reference to relevant literatures, the demographic and socio-economic characteristics of respondents were considered in our questionnaire. O’Garra et al. (2005) considered sex, age, highest level of education, work status, annual income and car ownership. Potoglou and Kanaroglou (2007) examined gender, age, education, the number of household vehicles and household income. However, the size of consumer’s family (especially the number of children) was considered in some other literatures (Brownstone et al., 1996; Erdem et al., 2010). In this paper, consumers’ demographic and socio-economic factors include: gender, age, academic degree, annual income, the number of family members, the number of family members with driver’s licenses and the number of vehicles owned by the family. Compared with previous studies, the number of family members with driver’s licenses is emphasized according to our viewpoints. Except for these demographic factors and socio-economic factors, the consumer’s knowledge, experience, environmental awareness and social responsibility are important factors that

Table 2 Studies on Consumers’ WTP for AFVs. Papers

Study content

Main influence factors

Mourato et al. (2004)

Drivers’ preferences for fuel cell taxis and the willingness to participate in a pilot project. To model the consumers’ WTP for FCVs.

Personal financial gains, environmental considerations.

Adamson (2005) O’Garra et al. (2007)

Potoglou and Kanaroglou (2007) Erdem et al. (2010)

Compared public WTP for the air pollution reductions associated with a scenario of the largescale introduction of hydrogen (H2) FC buses in four cities: Berlin, London, Luxembourg and Perth. Factors that encourage households to adopt a cleaner vehicle. Consumers’ WTP for hybrid vehicles in Turkey.

Fuel economy, running costs, emissions, having an automatic gearbox. Income, age, gender, education, attitude, knowledge, bus-use frequency, environmental awareness.

Reduction in the monetary costs, tax incentives, low emission rates, access to fuel. Income, awareness of hybrid cars, the number of automobiles that a family has, the awareness of environmental pollution and environmental protection, level of social responsibility, the importance of high performance of vehicle, academic degree, age, risk attitudes, gender, the number of children, average annual distance in kilometers, concern about global warming.

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may affect the consumer’s adoption of AFV (Mourato et al., 2004; O’Garra et al., 2005; Erdem et al., 2010). Vehicle attributes were described as purchase price, annual fuel and maintenance costs, fuel availability, incentives for purchasing a hybrid or an alternative fuel vehicle and acceleration (Potoglou and Kanaroglou, 2007). In our questionnaire, factors such as knowledge, experience, environmental awareness, significant attributes of vehicles were included. Furthermore, interpersonal influence was considered in this study. In this paper, consumers’ awareness and attitude towards EVs includes: consumers’ awareness of EVs, consumers’ motivation for purchasing EVs, the influence of vehicle attributes on preference and purchasing behavior, as well as consumers’ purchasing behavior in the future (including the vehicle type, purchase time for EVs, and purchase price for EVs). These factors are listed in Table 3. The paper also wants to investigate consumers’ awareness of EV development by asking consumers what measures could promote the development of EVs. 2.2. Data collection When researchers conduct similar studies in other regions of the world, they often mail their questionnaire to the respondents and the respondents mail it back after filling it; however, this method has a potential problem, which is the low response rate (usually below 40%) (Skipper, 2007). Alternatively, researchers sometimes conduct surveys using the Internet or in person. However, these methods also have limitations. A web-based survey is infeasible for this study because assistance is required from various institutions and because respondents’ clarifying questions about the survey cannot be answered when it is conducted online (Potoglou and Kanaroglou, 2007). In-person survey method requires a number of trained interviewers, and such training is time-consuming (Ahn et al., 2008). Due to these limitations, the authors use the questionnaire survey that takes place face to face in this research. In this way, the distributors of questionnaires can also answer respondents’ clarifying questions immediately. The research is conducted among trainees in driving schools, and the purpose is to identify the factors that impact consumers’ preference for AFVs. Trainees at driving schools belong to two different consumer groups. One major group is young employed persons. This group is characterized by a high academic degree and a relatively high income, so these individuals are likely to purchase an AFV in the near future. The other major group is university students, who are characterized by a high academic degree. University students are not likely to purchase an EV in the near future. But in the long-term, most members of this group will earn a relatively high income, and therefore this group is likely to become the major consumer group for AFVs. Because of the different characteristics of the two consumer groups, a survey conducted in driving schools is an effective way to gage the preferences and demands of different consumer groups for AFVs. The authors distributed a total of 400 copies of the questionnaire in Shilin driving school and in Zhongshan driving school in Nanjing between March 20th, 2010 and April 10th, 2010. The authors collected 299 valid copies of the questionnaire after they were filled out. The number of valid copies accounts for 74.8% of the total number of the copies distributed. Table 3 provides descriptive information about the driving school trainees who successfully completed the questionnaire. Table 3 shows that most of the trainees at the two driving schools are young (trainees under 35 account for 69.9%) and have acquired high academic degrees (undergraduate degree and graduate degree account for 85.9%). More than half of the trainees

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have a relatively high income (trainees with an income above CNY50,000 account for 52.2%) and a smaller proportion (31.5%) has a relatively low income (belongs to the group with an income below CNY30,000). This indicates that most of the two driving schools’ trainees are young people with high academic degrees. Most of them are young employees or university students. According to the studies conducted in other regions of the world, compared with other consumer groups, AFVs are more attractive to the group that contains relatively young, educated and wealthy consumers. Therefore, most of the trainees at driving schools are either currently in or likely to join in the consumer groups that will purchase AFVs in the future. 2.3. Stated preference and binary logistic regression model Forecasting the demand for new products or transportation innovations requires information about consumers’ preferences for products or services that do not exist in the current marketplace (Brownstone et al., 2000). Stated preference (SP) experiments can be designed to measure consumers’ preferences for hypothetical alternatives, including new products. Customers’ preferences for AFVs can be inferred by stated preference techniques (Roche et al., 2010). Compared with the conventional revealed preference method, there are several advantages of the SP method (Dagsvik and Liu, 2009). Firstly, the SP method can be used to obtain several (hypothetical) choice observations from each respondent. Secondly, the SP survey also has advantage over the revealed preference survey in that one can design the experiments in the SP survey with independent, widely varying conditions, and explanatory variables across respondents as well as across experiments for each individual. As a result, the SP method has been widely applied to analyze consumers’ preferences for AFVs. For further discussion on this issue: Brownstone et al. (1996), Hickson et al. (2007), Potoglou and Kanaroglou (2007), Ahn et al. (2008), Caulfield et al. (2010) and Mabit and Fosgerau (2011). This paper also contains a number of stated-preference experiments that were designed to examine what factors are likely to influence consumers’ adoption of EVs in Nanjing. There are several other reasons for using the SP method in our survey in addition to the advantages stated above. Firstly, it is difficult to collect market’s micro-data on EV demand because the current market share of EV in Nanjing is close to zero. Secondly, the SP method is cost-effective because a relatively small sample is required to provide the required information. When respondents stated their preferences for the hypothetical alternatives, their responses should be further analyzed using statistical models. In this study, the dependent variable (or outcome variable) can only be 0 or 1, and a number of independent variables may have an effect on the dependent variable. In this case, classical linear regression methods are inappropriate because they produce problematic error term distributions and unlikely probability estimates (O’Garra et al., 2005). It is feasible to use maximum likelihood estimation methods, such as logit models, for this type of analysis. Usually people use ‘‘logistic regression’’, ‘‘logistic model’’, ‘‘logistic regression model’’ or ‘‘logit model’’ to name the same model (Wang and Guo, 2001). Researchers have used various types of logit model to analyze the consumers’ preferences : logit model (Collantes, 2007; O’Garra et al., 2007; Beggs et al., 1981; Train, 1980; Calfee, 1985; Bunch et al., 1993; Brownstone et al., 2000; Caulfield et al., 2010; Mabit and Fosgerau, 2011); logistic regression (Savvanidou et al., 2010). The only difference between logistic regression and logit model is: logistic regression directly estimates the probability, while logit model does a logit probability of conversion (Wang and Guo, 2001). In our study, we used

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Table 3 The independent variables in the binary logistic regression models. Independent variables

Level

The percentage of respondents (%)

Mean (sd. deviation)

Gender

1: Male 2: Female

54.3 45.7

1.46(0.499)

Age

1: 2: 3: 4: 5:

o 25 25–34 35–44 45–54 Z 55

35.5 34.4 13.0 13.4 3.7

2.15(1.154)

Education level

1: 2: 3: 4: 5:

Junior middle school or lower Senior middle school or equivalent Associate Bachelor Master or Ph.D.

0 3.7 10.4 38.1 47.8

4.30(0.800)

Annual income (Yuan)

1: 2: 3: 4: 5:

o 30,000 30,000–49,999 50,000–69,999 70,000–99,999 Z 100,000

31.5 16.3 16.3 23.2 12.7

2.69(1.441)

Family size

1: 2: 3: 4: 5:

1 2 3 4 Z5

2.7 11.0 50.0 24.3 12.0

3.34(0.888)

The number of family members with driver’s licenses

1: 2: 3: 4: 5:

0 1 2 3 Z4

31.2 34.4 29.4 4.7 0.3

2.09(0.904)

The number of vehicles in a family

1: 2: 3: 4:

0 1 2 Z3

47.1 37.5 13.4 2.0

1.70(0.774)

Whether the respondent has an understanding of AFVs

1: 2: 3: 4: 5:

Definitely do not know Do not know Neutral Know Clearly know

5.0 31.2 51.5 11.0 1.3

2.73(0.776)

Whether the respondent has commuted by AFVs or has driven AFVs

1: Have 2: Do not have

28.1 71.9

1.72(0.450)

Vehicle performance

1: 2: 3: 4:

Definitely would not consider Would not consider Would consider Definitely would consider

1.3 1.0 52.2 45.5

3.42(0.599)

Government policy

1: 2: 3: 4:

Definitely would not consider Would not consider Would consider Definitely would consider

0.7 13.4 64.9 21.0

3.07(0.615)

Environmental requirement

1: 2: 3: 4:

Definitely would not consider Would not consider Would consider Definitely would consider

1.3 7.7 63.6 27.4

3.18(0.635)

The opinion of peers

1: 2: 3: 4:

Definitely would not consider Would not consider Would consider Definitely would consider

3.0 18.1 62.9 16.0

2.93(0.691)

Vehicle price

1: 2: 3: 4: 5:

Not at all important Not important Neutral Important Very important

1.3 0.3 15.7 60.5 22.2

4.02(0.716)

Tax reduction

1: 2: 3: 4: 5:

Not at all important Not important Neutral Important Very important

0.3 1.7 24.1 53.5 20.4

3.92(0.733)

Fuel price

1: Not at all important 2: Not important

0.0 1.0

4.31(0.651)

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Table 3 (continued ) Independent variables

Level

The percentage of respondents (%)

Mean (sd. deviation)

3: Neutral 4: Important 5: Very important

7.4 50.8 40.8

Fuel availability

1: 2: 3: 4: 5:

Not at all important Not important Neutral Important Very important

0.3 1.0 9.7 52.2 36.8

4.24(0.692)

Maintenance cost

1: 2: 3: 4: 5:

Not at all important Not important Neutral Important Very important

0.0 1.3 8.0 55.5 35.2

4.24(0.653)

Vehicle safety

1: 2: 3: 4: 5:

Not at all important Not important Neutral Important Very important

0.0 1.3 8.0 55.5 35.2

4.55(0.619)

Table 4 Statistical analysis of the consumers’ awareness of EVs. Questions

Strongly disagree (%)

Disagree (%)

Neutral (%)

Agree (%)

Strongly agree (%)

Mean/sd. deviation

Rank

a

Do EVs function well? Is the safety of EVs good? Is EVs’ maintenance cost lower than that of other types of vehicles? Do EVs bring environmental benefits? Is EVs’ fuel cost low? Is EVs’ price cheaper than that of fossil vehicles? Is EVs’ technology mature? Is EVs’ duration distance short?

0.7 1.0 0.3 0.3 0.7 4.3 1.7 5.0

7.4 3.3 14.4 0.7 9.7 17.7 7.0 12.7

44.1 37.1 39.5 9.0 25.4 32.8 40.5 31.8

35.5 42.5 29.1 43.1 38.8 28.1 29.8 31.8

12.4 16.1 16.7 46.8 25.4 17.1 21.1 18.7

3.52(0.829) 3.69(0.814) 3.47(0.946) 4.52(1.012) 3.79(0.956) 3.36(1.091) 3.62(0.950) 3.46(1.087)

5 3 6 1 2 8 4 7

0.604

three binary logistic regressions to determine the variables that affect respondents’ preferences for EVs, referring to the research (O’Garra et al., 2005). The binary logistic regression model is set as follows (Li and Luo, 2005): P ¼ expððb0 þ b1 x1 þ b2 x2 þ    þ bm xn Þ=ð1þ expðb0 þ b1 x1 þ b2 x2 þ    þ bm xn ÞÞÞ

ð1Þ

where b0 is a constant term that is irrelevant to xi, and b1, b2, y, bm are regression coefficients that represent the contribution of xi to P. Consumers’ preferences for and purchasing behaviors of EVs are outcomes that are likely to be affected by various factors. So in our binary logistic regression model, consumers’ WTP for EVs, purchase time for EVs and purchase price for EVs are considered as dependent variables. The independent variable remains to be determined for the model. Table 3 assigns values for independent variables in the binary logistic regression model.

maintenance cost, short charging interval and better economic results. Reliability analysis (Cronbach’s a ¼0.604) shows that the reliability of the consumers’ perceptions of these properties is acceptable. From this result, it can be seen clearly that currently most of the consumers only have a limited acquaintance with EVs. Most of the consumers know that EVs use alternative fuel as power, so these vehicles are environmental friendly. However, they do not know much about the performance, the maintenance cost, or the charging interval of EVs. Therefore, it is necessary to enhance consumers’ awareness of EVs in order to promote the development of EV industry. 3.2. Binary logistic regression models SPSS16.0 is used to set up the models in our study. The independent variables, their significance level and other statistical indicators are listed in Table 5.

3. Results and discussion 3.1. Public awareness of EVs Table 4 shows consumers’ awareness of EVs. From Table 4, it can be seen that the consumers’ perceptions of the properties of AFVs are ranked as follows: environmental benefits, low fuel cost, high degree of safety, high quality, good performance, low

3.2.1. Binary logistic regression model 1 (willingness to choose an EV) Table 5 shows that the independent variables of the model of consumers’ purchase willingness for EVs include: the number of family members with driver’s licenses, the number of vehicles owned by the family, government policies and fuel price. The number of family members with driver’s licenses and the number

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Table 5 Estimated results of Models 1, 2 and 3. (Do not show estimated results for parameters not in the model.) Parameters

Age Education level Annual income Family size The number of driver’s license in a family The number of vehicles in a family Government policy The opinion of peers Tax reduction Fuel price Maintenance cost Vehicle safety Constant Nagelkerke R2  2 log likelihood Chi-square Overall predictive accuracy (%)

Model 1 (willingness to choose an EV) (n ¼27)

Model 2 (time period to purchase an EV) (n ¼27)

Model 3 (acceptability of purchase price of an EV) (n¼27)

B

B

Sig

B

Sig

–  1.987 0.331 – –  1.131  0.733 0.938 0.414  0.759 – – 11.374

– 0.000 0.098 – – 0.001 0.091 0.013 0.189 0.047 – – 0.000

0.351 0.441 – 0.254 – 1.036 – 0.420 – –  0.844 0.751  6.006

0.049 0.024 – 0.111 – 0.000 – 0.072 – – 0.002 0.005 0.001

– – – – 0.468  0.515  0.395 – – 0.399 – –  1.529

Sig – – – – 0.025 0.038 0.090 – – 0.076 – – 0.159 0.057 322.843 11.248 70.3

0.399 126.411 43.905 72.6

of vehicles owned by the family have good significance level (Sigo0.05), while government policies and fuel price have poor significance level (Sig40.05). The interpretive validity of the model is somewhat low (0.057). One possible reason for the low validity is that currently the consumers in Nanjing are still unfamiliar with EVs. The likelihood ratio is relatively high. The chi-square value of the model is good and the overall predictive accuracy is above 50%. Therefore, the binary logistic regression model that takes consumers’ purchasing willingness for EVs as its dependent variable is expressed as follows: P ¼ expð1:529 þ 0:46x1 0:515x2 0:395x3 þ0:399x4 Þ =ð1þ expð1:529 þ 0:468x1 0:515x2 0:395x3 þ 0:399x4 ÞÞ

ð2Þ

where x1, x2, y, and x4, respectively, represent four independent variables in the model, i.e. the number of family members with driver’s licenses, the number of vehicles owned by the family, government policies, and fuel price. Given the specific values from x1 to x4, it is possible to calculate the probability that a consumer is willing to purchase an EV. 3.2.2. Binary logistic regression model 2 (time period to purchase an EV) In model 2, we set the value of 1 as ‘‘purchase within 5 years’’ and the value of 0 as ‘‘not purchase within 5 years’’. Independent variables that are introduced into model 2 include: education level, annual income, the number of vehicles owned by the family, government policies, the opinion of peers, tax incentives to purchase the vehicle and fuel price. These variables are expressed as x1, x2, y, and x7, respectively. Moreover, x1, x3, x5 and x7 have good significance level (Sigo0.05), while x2, x4 and x6 have poor significance level (Sig40.05). The interpretive validity of the model is 0.399, which is within the acceptable range. The likelihood ratio and chi-square value of the model are ideal, and overall predictive accuracy is far above 50%. Therefore, the binary logistic regression model that takes the consumers’ purchase time for EVs as its dependent variable is expressed as follows: P ¼ expð11:3741:987x1 þ 0:331x2 1:131x3 þ 0:733x4 þ 0:938x5 þ 0:414x6 0:759x7 Þ=ð1þ expð11:3741:987x1 þ 0:331x2 1:131x3 0:733x4 þ 0:938x5 þ 0:414x6 0:759x7 ÞÞ ð3Þ Given the specific values from x1 to x7, it is possible to calculate the probability that a consumer will purchase an EV within 5 years.

0.390 285.595 95.267 72.1

3.2.3. Binary logistic regression model 3 (acceptability of purchase price of an EV) Our questionnaire survey results reveal that 46.2% of the respondents choose the EVs that cost them no more than CNY150,000, while the rest of the respondents choose the EVs that cost them more than CNY150,000. Based on the survey results and relevant literatures in other regions (Potoglou and Kanaroglou, 2007), we set the value of 1 as an acceptable price of more than CNY150,000 (current car price) and the value of 0 as an acceptable price of no more than CNY150,000 (current car price) in model 3 in order to enhance the feasibility of this model. When the binary logistic regression model takes purchase price as its dependent variable, the factors related to vehicle price should be excluded from the independent variables. Therefore, after excluding ‘‘vehicle price’’ and ‘‘tax incentives for purchasing the vehicle’’ in Table 3, we obtained the independent variables and their coefficients, as well as other statistical results of model 3. From Table 5, it can be seen that the independent variables of model 3 include: age, education level, family size, the number of vehicles owned by the family, the opinion of peers, maintenance cost and the degree of safety. These variables are expressed as x1, x2, y, and x7, respectively. Furthermore, x1, x2, x4, x6 and x7 have good significance level (Sigo0.05), while x3, and x5 have poor significance level (Sig40.05). The interpretive validity of the model is 0.390, which is within the acceptable range. The likelihood ratio as well as chi-square value of the model are ideal, and overall predictive accuracy is 72.1%. Therefore, the binary logistic regression model that takes purchase price as the dependent variable is expressed as follows: P ¼ expð6:006 þ0:351x1 þ 0:441x2 þ 0:254x3 þ 1:036x4 þ 0:420x5 0:844x6 þ 0:751x7 Þ=ð1þ expð6:006 þ 0:351x1 þ 0:441x2 þ 0:254x3 þ 1:036x4 þ0:420x5 0:844x6 þ0:751x7 ÞÞ ð4Þ Given the specific values from x1 to x7 in model 3, it is possible to calculate the probability that a consumer will purchase an EV that is more than CNY150,000. 3.3. Discussion In model 1, the coefficients of the number of family members with driver’s licenses and fuel price are positive, whereas the

Y. Zhang et al. / Energy Policy 39 (2011) 7015–7024

coefficients of the number of vehicles owned by the family and government policies are negative. According to our model, the more family members can drive, the more likely that the consumer is willing to purchase an EV. This is a new finding in our research. One possible explanation for this finding is that maybe these consumers have more personal choices and more environmental concerns. In contrast, the more vehicles a family have, the less likely that a consumer in this family is willing to purchase an EV. In the relevant studies in other nations, the coefficient of government policies is generally positive. The possible explanations for the negative coefficient of government policies are: the current promotion policies are still somehow limited (mainly on subsidies), the consumers are still unaware of these promotion policies, and the consumers are clear about the disadvantages of domestic electric vehicles. Since the coefficient of government policies is 0.395, it is obvious that its absolute value is small. Thus, we can figure out that government policies make little difference to consumers’ purchasing willingness. In model 2, the coefficients of education level, the number of vehicles owned by the family, government policies and fuel price are negative, while the coefficients of annual income, tax incentives for purchasing the vehicle and the opinion of peers are positive. Keeping all other conditions constant, consumers are more likely to purchase EVs when fuel price increases. It is also easy to understand why the coefficients of the opinion of peers, annual income and tax incentives for purchasing the vehicle are positive. According to the study of consumer behavior (Sun et al., 2009), the opinion of peers tends to have a positive effect on consumer behavior. The study (Axsen and Kurani, 2011) also revealed the effect of interpersonal influence on consumers’ acceptance for AFVs. The more a consumer earns, the more likely that he or she is willing to purchase an EV. Keeping all other conditions constant, the higher the tax incentives are (provided that the consumers are aware of government policies and the government offers tax incentives for EVs), the more likely that a consumer is willing to purchase an EV (Potoglou and Kanaroglou, 2007). In the model for purchase time, the coefficients of education level and government policies are negative. However, in similar studies conducted in other regions of the world, the coefficients of these two factors are positive (O’Garra et al., 2005). Generally speaking, the well-educated consumers are more likely to be familiar with AFVs. Therefore, one possible explanation for this difference is that the EV industry is more developed and mature in developed countries. Therefore, consumers with high academic degrees in these countries are more willing to purchase EVs. In China, however, the EV industry is still a developing sector, and consumers with high academic degrees are familiar with the disadvantages of domestic EVs. As a result, they are unwilling to purchase EVs in the short term. Another possible explanation is that the respondents of our survey have a similar education level and the majority of the respondents are university students (revealed by annual income), so they are unable to purchase EVs in the near future. The explanation for why the coefficient of government policies is negative is similar to the explanation for that in the purchase willingness model. In model 3, the coefficients of age, education level, family size, the number of vehicles owned by the family, the opinion of peers and the degree of vehicle safety are positive, while the coefficient of maintenance cost is negative. According to the relevant studies conducted in other regions of the world, people with higher academic degrees tend to have stronger purchase power and are able to afford higher price. In this model, the more members there are in a consumer’s family (especially children), the consumer is likely to be more willing to purchase an EV. This finding is similar to

7023

that in the study in California (Brownstone et al., 1996). But the study in Turkey (Erdem et al., 2010) indicated that the number of children in a family is not important for consumer’s adoption of HEV. Model 1 shows that the number of vehicles owned by the family exerts a negative influence on consumers’ WTP. However, the more vehicles a family owns, the more likely that the family is wealthy. Therefore, the wealthy family can afford more expensive EVs. This is why the coefficient of the number of vehicles owned by the family is positive. The explanation for the positive coefficient of the opinion of peers in model 3 is similar to that in model 2. Keeping all other conditions constant, the consumer is more likely to purchase an EV at a higher price when the degree of vehicle safety is higher and the maintenance cost is lower. In this model, the coefficient of age is positive, while it is negative in studies conducted in other regions of the world (Mourato et al., 2004). Probably this difference results from the fact that the respondents in our study are relatively young. As is shown in Table 1, 69.9% of the respondents are below 35 years old. It is likely that older consumers are able to afford a more expensive EV, thus leading to the positive coefficient of age. The number of vehicles owned by the family is a factor that influences the purchase willingness, purchase time and purchase price. The more vehicles a family owns, the consumer in this family is less likely to purchase an EV in the short term. However, the more vehicles a family owns, the more likely that this family is relatively wealthy. Thus the consumer in this family can afford higher price for an EV. The results of our study show that the factors such as previous experience with EVs, environmental awareness, gender, and fuel availability have no obvious effect on consumer’s adoption of EVs. This finding is different from that of previous studies in several aspects. One possible explanation for this difference is that the sample size in our questionnaire survey is not large enough and the respondents’ demands for EVs have no obvious difference. Another possible explanation is that respondents in Nanjing have various preferences for EVs compared with the respondents in other studies. The results also indicate that it is necessary to conduct a number of in-depth surveys in order to obtain a better understanding of the EV market in China.

4. Conclusions Based on the current status of the EV market in China and the studies on consumers’ preferences for AFVs conducted in other regions of the world, we studied consumers’ preferences for AFVs in Nanjing. We conducted a questionnaire survey and then used binary logistic regression models to analyze consumers’ purchase willingness, purchase time and purchase price for EVs. This research offers some insights into the factors that affect consumer’s decision to purchase an EV. This research suggests that there is the potential for different influences on consumer behavior in China towards EVs compared to other world regions. This could be due to the varying stages of development of the electric vehicle industry in the different regions or to the use of different approaches (including choice of respondents) in the studies. However, it is also recognized that these results are from one study in Nanjing and application of the results to China as a whole is not necessarily valid. Therefore further research is required. Firstly, to see if the results from this study are replicable elsewhere in China, this would also include widening the sample to include different consumer groups. Secondly, if the results are replicable, more in-depth, qualitative, face-to-face interviews to ensure better understanding of the factors should be undertaken. Finally, and based on the outcomes of 1 and 2 there is a need to test and understand the applicability of these results to other regions and vice versa—whether research

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results from other regions could be applicable to China in the longer term.

Acknowledgments The authors gratefully acknowledge support from the National Natural Science Foundation of China (70902029) and the Ph.D. Program Foundation of the Ministry of Education of China (20090092120045). The authors also thank Wang Juebin and Zhao Beibei for their help in improving our English writing. References Adamson, K.A., 2005. Calculating the price trajectory of adoption of fuel cell vehicles. International Journal of Hydrogen Energy 30 (4), 341–350. Ahn, J., Jeong, G., Kim, Y., 2008. A forecast of household ownership and use of alternative fuel vehicles: a multiple discrete-continuous choice approach. Energy Economics 30 (5), 2091–2104. Axsen, J., Kurani, K.S., 2011. Interpersonal influence in the early plug-in hybrid market: observing social interactions with an exploratory multi-method approach. Transportation Research Part D: Transport and Environment 16 (2), 150–159. Beggs, S., Cardell, S., Hausman, J., 1981. Assessing the potential demand for electric cars. Journal of Econometrics 16, 1–19. Brownstone, D., Bunch, D.S., Train, K., 2000. Joint mixed logit models of stated and revealed preferences for alternative-fuel vehicles. Transportation Research Part B: Methodological 34 (5), 315–338. Brownstone, D., Bunch, D.S., Golob, T.F., Ren, W., 1996. A transactions choice model for forecasting demand for alternative-fuel vehicles. Research in Transportation Economics 4, 87–129. Bunch, D.S., Bradley, M., Golob, T.F., Kitamura, R., Occhiuzzo, G.P., 1993. Demand for alternative-fuel vehicles in California: a discrete-choice stated preference pilot project. Transportation Research 27A, 237–253. Bunch, D.S., Bradley, M., Golob, T.F., Kitamura, R., Occhiuzzo, G.P., 1993. Demand for clean-fuel vehicles in California: a discrete-choice stated preference survey. Transportation Research A: Policy and Practice 27 (3), 237–253. Calfee, J.E., 1985. Estimating the demand for electric automobiles using fully disaggregated probabilistic choice analysis. Transportation Research 19B, 287–302. Caulfield, B., Farrell, S., McMahon, B., 2010. Examining individuals preferences for hybrid electric and alternatively fuelled vehicles. Transport Policy 17 (6), 381–387. Collantes, G.O., 2007. Incorporating stakeholders’ perspectives into models of new technology diffusion: the case of fuel-cell vehicles. Technological Forecasting and Social Change 74 (3), 267–280. Dagsvik, J.K., Liu, G., 2009. A framework for analyzing rank-ordered data with application to automobile demand. Transportation Research Part A: Policy and Practice 43 (1), 1–12. Dagsvik, J.K., Wennemo, T., Wetterwald, D.G., Aaberge, R., 2002. Potential demand for alternative fuel vehicles. Transportation Research Part B: Methodological 36 (4), 361–384. ¨ Erdem, C., S- enturk, I., S- ims-ek, T., 2010. Identifying the factors affecting the willingness to pay for fuel-efficient vehicles in Turkey: a case of hybrids. Energy Policy 38 (6), 3038–3043. Golob, T.F., Torous, J., Bradley, M., Brownstone, D., Crane, S.S., Bunch, D.S., 1997. Commercial fleet demand for alternative-fuel vehicles in California. Transportation Research Part A: Policy and Practice 31 (3), 219–233. He, K., Huo, H., Zhang, Q., He, D., Wang, F., Walsh, M., Michael, P., 2005. Oil consumption and CO2 emissions in China’s road transport: current status, future trends, and policy implications. Energy Policy 33 (12), 1499–1507. Hickson, A., Phillips, A., Morales, G., 2007. Public perception related to a hydrogen hybrid internal combustion engine transit bus demonstration and hydrogen fuel. Energy Policy 35 (4), 2249–2255. Hu, X., Chang, S., Li, J., Qin, Y., 2010. Energy for sustainable road transportation in China: challenges, initiatives and policy implications. Energy 35 (11), 4289–4301. IEA, 2009a. CO2 Emissions from Fuel Combustion Highlights. IEA. IEA, 2009b. Key World Energy Statistics. IEA. Janssen, A., Lienin, S.F., Gassmann, F., Wokaun, A., 2006. Model aided policy development for the market penetration of natural gas vehicles in Switzerland. Transportation Research Part A 40 (4), 316–333.

Lane, B., Banks, N., 2010. Low CVP Car Buyer Survey: Improved Environmental Information for Consumers. Ecolane and Sustain on behalf of the Low Carbon Vehicle Partnership. Lane, B., Potter, S., 2007. The adoption of cleaner vehicles in the UK: exploring the consumer attitude-action gap. Journal of Cleaner Production 15 (11–12), 1085–1092. Li, Z.H., Luo, P., 2005. second ed. The Analysis of SPSS for Windows, 261. Electronic Industry Press, Beijing, CN. Ma, J., Wang, N., Kong, D., 2009. Market forecasting modeling study for new energy vehicle based on AHP and logit regression. Journal of Tongji University (Nature of Science) 37 (8), 1079–1084. Mabit, S.L., Fosgerau, M., 2011. Demand for alternative-fuel vehicles when registration taxes are high. Transportation Research Part D: Transport and Environment 16 (3), 225–231. Martin, E., Shaheen, S.A., Lipman, T.E., Lidicker, J.R., 2009. Behavioral response to hydrogen fuel cell vehicles and refueling: results of California drive clinics. International Journal of Hydrogen Energy 34 (20), 8670–8680. Mourato, S., Saynor, B., Hart, D., 2004. Greening London’s black cabs: a study of driver’s preferences for fuel cell taxis. Energy Policy 32 (5), 685–695. Nesbitt, K., Sperling, D., 1998. Myths regarding alternative fuel vehicle by lightduty vehicle fleets. Transport and Environment 3 (4), 259–269. O’Garra, T., Mourato, S., Garrity, L., Schmidt, P., Beerenwinkel, A., Altmann, M., Hart, D., Graesel, C., Whitehouse, S., 2007. Is the public willing to pay for hydrogen buses? A comparative study of preferences in four cities. Energy Policy 35 (7), 3630–3642. O’Garra, T., Mourato, S., Pearson, P., 2005. Analyzing awareness and acceptability of hydrogen vehicles: a London case study. International Journal of Hydrogen Energy 30 (6), 649–659. Ou, X., Zhang, X., Chang, S., 2010a. Scenario analysis on alternative fuel/vehicle for China’s future road transport: life-cycle energy demand and GHG emissions. Energy Policy 38 (8), 3943–3956. Ou, X., Yan, X., Zhang, X., 2010b. Using coal for transportation in China: life cycle GHG of coal-based fuel and electric vehicle, and policy implications. International Journal of Greenhouse Gas Control 4, 878–887. Ouyang, M.G., Wang, H.W., Hao, H., 2009. China Vehicle Energy Demand Study. Technique Report. China Automotive Energy Research Center, Tsinghua University, Beijing, China. Parker, R.S., Fletchall, H., Pettijohn, C.E., 1997. Truck operators’ perspectives on use of alternative fuels. Transportation Research Part E: Logistics and Transportation Review 33 (1), 73–78. Potoglou, D., Kanaroglou, P.S., 2007. Household demand and willingness to pay for clean vehicles. Transportation Research Part D: Transport and Environment 12 (4), 264–274. Roche, M.Y., Mourato, S., Fischedick, M., Pietzner, K., Viebahn, P., 2010. Public attitudes towards and demand for hydrogen and fuel cell vehicles: a review of the evidence and methodological implications. Energy Policy 38 (10), 5301–5310. Romm, J., 2006. The car and fuel of the future. Energy Policy 34 (17), 2609–2614. Safaei, M.H., Tichkowsky, G., Kumar, A., 2009. Development of a multi-criteria assessment model for ranking of renewable and non-renewable transportation fuel vehicles. Energy 34 (1), 112–125. Savvanidou, E., Zervas, E., Tsagarakis, K.P., 2010. Public acceptance of biofuels. Energy Policy 38 (7), 3482–3488. Sun, L., Bian, Z., Zhao, D., 2009. Customer Behavior. Dongbei University of Finance and Economics Press, Dalian. Skipper, D.H., 2007. Consumer Attitudes Regarding Biofuels. University of Arkansas, USA. Train, K., 1980. The potential demand for electric vehicles. Transportation Research 14A, 405–414. Vliet, O., Brouwer, A.S., Kuramochi, T., Broek, M., Faaij, A., 2011. Energy use, cost and CO2 emissions of electric cars. Journal of Power Sources 196, 2298–2310. Wang, C., Cai, W., Lu, X., Chen, J., 2007. CO2 mitigation scenarios in China’s road transport sector. Energy Conversion and Management 48 (7), 2110–2118. Wang, J., Guo, Z., 2001. Logistic Regression Model—Methods and Applications. Higher Education Press, Beijing. Yan, X., Crookes, Roy J., 2009a. Life cycle analysis of energy use and greenhouse gas emissions for road transportation fuels in China. Renewable and Sustainable Energy Reviews 13 (9), 2505–2514. Yan, X., Crookes, Roy J., 2009b. Reduction potentials of energy demand and GHG emissions in China’s road transport sector. Energy Policy 37 (2), 658–668. Yeh, S., 2007. An empirical analysis on the adoption of alternative fuel vehicles: the case of natural gas vehicles. Energy Policy 35 (11), 5865–5875.