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Will subsidies drive electric vehicle adoption? Measuring consumer preferences in the U.S. and China
... Jansson et al. (2017), Liao (2022), and Zhao et al. (2022) posit a positive correlation, while Qian et al. (2019) contend that elevated household income adversely affects EV adoption intention. Cross-national analyses by Helveston et al. (2015) further highlight the divergent effects of income on adoption intention. Revealed preference literature highlights that plug-in electric vehicle (PEV) buyers tend to be higher-income households, particularly for high-end models like Tesla (Sheldon et al. 2023). ...
... Overall, the findings suggest that a subsidy structure targeted toward low-income households would improve cost-effectiveness and reduce regressive outcomes, ensuring more equitable adoption and better alignment with policy goals. Table 2 exhibits that product attributes such as initial cost, operating and maintenance costs, recharging time, and acceleration time are important factors that have negative relationships with consumers' adoption of EVs (Barth et al. 2016;Helveston et al. 2015;Huang et al. 2021;Noel et al. 2019;Qian et al. 2019;Tarei et al. 2021;Zhao et al. 2022). Tarei et al. (2021) utilize an expert interview to show that high upfront cost is one vital barrier to EV adoption. ...
... Qian et al. (2019) and Huang et al. (2021) use stated choice experiments and show that potential Chinese car buyers consider product attributes such as annual operating and maintenance costs to be more important than the initial cost of the vehicle. Helveston et al. (2015) and Qian et al. (2019) also identify that prospective Chinese EV buyers are willing to pay extra upfront, with a willingness to pay (WTP, hereafter) over RMB10 for each RMB 1 reduction in annual running costs and $3,000 more for every $0.01/mile reduction in operating and maintenance costs, respectively. Lower residents than suburban counterparts (Zhao et al. 2022), as well as a heightened preference for EVs among individuals in less-developed cities compared to their counterparts in more developed locales (Huang et al. 2021). ...
The urgent need for a net-zero future necessitates a fundamental shift in the energy sector, with road transportation responsible for a substantial 37% of global energy-related CO 2 emissions in 2021, emerging as a pivotal focal point in the battle against climate change. Energy consumption in the road sector is expected to surge by 1.26% with a 1% growth in urbanization, concentrated mainly in Asia and Africa by the mid-2030s. Therefore, addressing emissions from the transportation industry is paramount. Electric vehicles (EVs), coupled with a transition to renewable energy, offer a sustainable solution, yet their market share remains at a modest 10% globally and in Asia. With numerous nations committed to achieving net-zero emissions, EV adoption is on the rise, particularly in developing regions with high urbanization and Greenhouse Gas (GHG) emissions. Governments worldwide have initiated policies that provide incentives to promote EVs, but challenges like patent declines and EV battery disposal concerns persist. In this paper, we make an integrative critical review of the existing literature, conduct a SWOT analysis of EVs, and address crucial factors influencing their adoption, thereby contributing to the goal of a more sustainable future in road transportation.
... Literature available on various drivers for global e-bus adoption is reviewed which indicates that faster deployment of e-bus encompasses several enablers; e-bus deployment worldwide has been driven by more than 30 factors. The key factors identified in various studies on the driving forces behind EV adoption include (a) adequate charging infrastructure and favourable government policies, (b) innovations in the electric mobility technology and charging systems, (c) financial and fiscal incentives for EV adoption, and (d) Awareness generation on EVs, environmental consciousness and pride in EV ownership (Aasness & Odeck, 2023;Austmann, 2021;Carley et al., 2013;Choi et al., 2022;Coffman et al., 2017;Ghosh & Bhaduri, 2023;Guno et al., 2021;Hagem et al., 2023;Helveston et al., 2015;Inci et al., 2022;Jenn et al., 2018;Liao et al., 2017;Melander et al., 2022;Sierzchula, 2014;Xiong et al., 2023;Zhang et al., 2016). ...
... • Step 3: statistical analysis of data gathered from the survey to determine the crucial factors, their contributing drivers, and respective contributions for the uptake of pilot e-bus project in Kolkata and large-scale accelerated uptake of e-buses in the city. Gross cost contract mechanism offered to e-bus operators in many countries Hensher, 2021 Solving financial challenges of procuring e-buses Guno et al., 2021 availability of subsidies for e-buses, incentives and subsidies for research and production of e-buses Choi et al., 2022;Helveston et al., 2015;inci et al., 2022;Melander et al., 2022 incentives like waiver of tolls and other municipal incentives, exemption from registration tax and vat ...
... The various types include the delphi Method (Questionnaire Survey), Interview (through long discussions, not formal questionnaires), Metaanalysis (Big data reqd, not suitable in this study), Observation-based (validation required), Optimization techniques (linear programming, the objective of the study is not optimization, but identifying the key enablers), secondary data analysis (literature review basis), Simulation-based (not applicable for this type of analysis). A Survey-based method is robust and most popular for these types of studies (Helveston et al., 2015;Kumar & Alok, 2020). Thereby, after a detailed review, the delphi method is adopted for the Opinion Survey in this analysis. ...
Kolkata’s sustainability and liveability are impaired by uncontrolled emissions from vehicles that rely on traditional fossil fuels. Adoption of environment-friendly energy-efficient technologies like Electric Vehicles (EVs) is urgently required in the city. EV is a suitable option for both public and private transport. However, like other new technologies, EVs are also required to be facilitated by different enablers and drivers. Kolkata executed a pilot project of deploying 80 Electric Buses (e-bus) during 2017–2020 and is considering large-scale uptake of e-buses. The purpose of this study is to investigate the various elements that are crucial to accelerated large-scale e-bus adoption in the city. A survey of two hundred stakeholders from ten relevant entities is carried out through the statistical technique of Principal Component Analysis (PCA). The results indicate five key factors for faster e-bus adoption; (i) Successful e-bus pilot project, (ii) Setting up of an EV Accelerator Cell, (iii) Capital and operational Instruments, (iv) Publicity/Awareness Programs, and (v) Awards and Appreciation for the pilot e-bus project deployed in the city.
... The emphasis on environmental sustainability globally has underscored the role of electric vehicles (EVs) in mitigating climate change, especially in the transportation sector, which is responsible for about 25 % of global CO 2 emissions (Khurana et al., 2020;Xiong et al., 2023). While much of the focus has been on Battery Electric Vehicles (BEVs), Plug-in Hybrid Electric Vehicles (PHEVs) have been understudied despite their potential benefits in various geographical and consumer contexts (Helveston et al., 2015;Huang et al., 2021;Visaria et al., 2022) (detailed discussion in Section 2). This is particularly significant in India, the world's third-largest passenger car market, which has set ambitious targets for EV market shares of 40 % by 2030 and 100 % by 2047 (Bera and Maitra, 2023;Khurana et al., 2020). ...
... The review of EV literature indicates a noticeable geographical disparity in research emphasis, with the majority of the studies conducted in developed nations such as the United States (US) (Jia and Chen, 2023;Tanaka et al., 2014), Italy (Danielis et al., 2020;Giansoldati et al., 2020b), Greece (Mpoi et al., 2023), Denmark (Visaria et al., 2022), South Korea (Lashari et al., 2022), Poland (Kowalska-Pyzalska et al., 2022), Germany (Rommel and Sagebiel, 2021;Hackbarth and Madlener, 2016), Canada (Miele et al., 2020;Higgins et al., 2017), Spain (Rahmani and Loureiro, 2019), Netherlands (Hoen and Koetse, 2014) and countries where EVs have already been adopted as mainstream transportation such as China (Ji and Gan, 2022;Helveston et al., 2015), offering detailed insights into EV adoption factors like vehicle, infrastructure, policy, sociodemographic and trip-related attributes. Conversely, research within developing regions, such as India, appears less comprehensive concerning consumer choice preferences towards EVs in general and PHEVs in particular (Khurana et al., 2020;Navalgund and Nulkar, 2020;Tarei et al., 2021). ...
... The attitudinal statements assessing consumers' environmental and technological attitudes are present in Table 2 and are adapted from past literature (Ewing and Sarigöllü, 2000;Helveston et al., 2015;Nie et al., 2018). This approach, similar to Ewing and Sarigöllü (2000), builds on the well-established relationship between attitudes, preferences, and behaviors to provide a more nuanced understanding of consumer choices. ...
... -Social influence: This holds significant implications in the context of vehicle acceptance and has been empirically demonstrated to exert a substantial influence in predicting the acceptance of automated vehicles [46] and shared autonomous vehicles [47]. It also facilitates the transition to alternative fuel vehicles for climate change mitigation [48], making it a crucial factor for potential policy measures aimed at incentivising the adoption of cleaner vehicle fuel technologies. -Price-value: Price-value has been acknowledged to influence user acceptance of shared automated vehicles and car-sharing systems [49]. ...
... It is also a crucial factor in consumers' electric vehicle purchase decisions [52] and the adoption of car-sharing services [53] or shared autonomous vehicles [54], reflecting their consideration and awareness of environmental issues. -Trust: Trust has been empirically demonstrated to have a significant impact on the acceptance of shared autonomous vehicles [48], shared motorcycles [55], and automated vehicles [56]. ...
This study investigates the factors that drive users to sustain their usage of shared electric scooter (e-scooter) services in Taiwan, distinguishing itself from the conventional focus on predicting consumers’ initial adoption and behavioural intentions. It employs subjective rating questions, incorporating constructs related to user acceptance, attitudes and user experience (UX). Through hierarchical regression analysis of quantitative survey data, the study identifies key factors such as users’ modes of transportation, environmental attitudes, acceptance of shared services, attitudes towards private scooters, UX, total usage instances and age. However, reliance on private scooters as a mode of transportation and frequent usage of shared e-scooters negatively impact the sustained usage of these services. The research further highlights early development challenges in shared vehicle services, including concerns over personal data security, user-unfriendly system designs, lack of convenience, inadequate parking infrastructure and ineffective financial incentives. Based on these findings, the study provides recommendations for service providers and government entities to enhance service design and proactively address these challenges. Implementing these recommendations is expected to mitigate the impact of these challenges and potentially improve user acceptance, UX, and the overall sustainability of shared vehicle services.
... -Distribution of chargers [38,39] -Different charging payment possibilities [40] -Technological support (e.g. mobile applications for checking availability) [41] Legal regulations; -Stimulation and incentives for the purchase of electric and hybrid vehicles [42,43] -Facilitation in the registration of electric and hybrid vehicles [44,45] -Regulation of import, sale and registration of vehicles with internal combustion engine [45] -Integration of electric and hybrid vehicles into the existing legal framework [44,46] Possibilities and performance of electric vehicles -Battery capacities, i.e. distances that can be covered with a single charge [47,48] -Maximum speed, acceleration and torque [48][49][50] -Duration of charging in home conditions and on fast chargers [14,51] -Energy consumption per kilometer [52,53] Attitudes, information, intentions and possibilities of vehicle users -Purchasing power of citizens in relation to the prices of electric and hybrid vehicles -Driver's needs, wishes and habits [54][55][56][57] -Knowledge of the properties and performance of electric and hybrid vehicles [58,59] -Knowledge of the level of development of infrastructure for electric vehicles in the user's country [60] -Knowledge of legal regulations related to the purchase, registration and exploitation of electric vehicles in the user's country [58] -The influence of belonging to a certain stratum of society on the purchase of electric and hybrid vehicles [61,62] Viability of vehicle-to-grid (V2G) service [63] There are several studies dealing with the average daily distances covered by the drivers in different countries. Plötz et al [57] reported summary statistics for Sweden, Germany, Winnipeg (Canada), and Seattle (USA), as follows: ...
... The main strengths for this transition are related to low fuel costs, reduced noise, and lower environmental impact, while as the main weakness, apart from some uncertainties in the technical and operational performance, high initial purchasing costs of EFVs are identified. The influence of incentives for the purchase of electric and hybrid vehicles in China and America was analyzed in study by Helveston [42]. The amount of incentives was roughly similar in both countries, but the Chinese were significantly more willing to buy electric vehicles than Americans. ...
Electrification of passenger cars is necessity in the transition towards a net zero emission economy. Electric vehicles (EVs) are widely accepted in many developed countries worldwide. According to the 2022 Global EV Outlook by the IEA, there has been an exponential growth in sale of EVs in recent years, but almost all of these vehicles are concentrated in developed countries, while adoption of EVs in the developing countries lags significantly behind this trend. On the other hand, there are very few studies dealing with progress toward sustainable mobility in developing countries. In this paper we analyzed current framework characteristic for the Republic of Serbia, which has almost negligible share of registered EVs (0.007%). In addition, we analyzed attitudes and preferences of representative Serbian population towards EVs using questionnaire survey. The collected information pertains to the most important reasons for buying an electric car in Serbia as well as the main obstacles. The results given in the study are helpful for policy-makers in similar markets on how to potentially accelerate adoption of EVs in target domains. In addition, the results are also useful to manufacturers of electric vehicles to find out which particular features are most attractive to potential buyers in specific markets.
... Furthermore, such policies can also help middle-income countries, where a large part of the population may be considering a car purchase for the first time (Chamon, Mauro and Okawa, 2008[64]), to avoid locking in a car-dependent future. 78 Vehicle electrification should be a priority when alternatives to car may not be feasible. The market penetration of BEVs is on an upward trend, and their stock in 2023 is estimated to be 5 times higher than in 2018 (IEA, 2023 [65]). ...
... Nevertheless, a purchase subsidy remains significantly more effective than an equivalent reduction in user costs. If USD 7000 is provided in the form of subsidised electricity to recharge a BEV, the boost in the BEV share will not exceed 1.8 percentage 78 These policies need to overcome structural barriers, as cars are often considered more comfortable and more convenient than alternative modes of transport. A cultural and social barrier also exists in the form of ingrained beliefs that strongly associate cars with status and independence (Paulssen et al., 2014[105]; Mattioli et al., 2020[106]; Moody and Zhao, 2020 [107]). ...
This paper offers new insights on household choices related to transport, based on data from the third OECD Survey on Environmental Policies and Individual Behaviour Change (EPIC). The analysis explores the role of key factors determining the choice of fuel type in vehicles and the choice of transport mode in trips. The study uses choice experiment data to estimate the importance of key drivers of electric vehicle purchase decisions and to project future adoption rates of electric vehicles. Results show that income, location and environmental awareness play important roles in the choice of whether to own a vehicle, and its fuel type. Convenient access to charging, such as at home or workplace, can significantly increase the likelihood of choosing an electric vehicle.
... vehicle technology, e.g. range, and acceleration (Beck et al., 2017;Helveston et al., 2015;Higgins et al., 2017;Jensen et al., 2020); -charging infrastructure, e.g. home charging and fast charging availability (Buchmann et al., 2021;Chandra, 2022;Jensen et al., 2020); ...
... However, they are cautioned that this is very costly because personal interviews are highly recommended to ensure that interviewers can help the respondents understand the complex task. Further, due to the limited number of observations, it was impossible to distinguish between gasoline and diesel-driven cars as well as between BEVs and PHEVs, although preferences (Helveston et al., 2015) and effects of factors in promoting EVs (Buchmann et al., 2021;Chandra, 2022;Plötz et al., 2017) might differ. Moreover, a distinction by segment class could be considered, since the preference for the drive-train might differ in dependence on the preferred vehicle size (Higgins et al., 2017). ...
A reduction of diesel and petrol vehicles and a shift from conventional to electric vehicles (EV) is part of many governments’ plans towards transport decarbonization. To encourage such a shift, governments need to implement effective policies. In a stated adaptation experiment, 444 respondents were presented with four scenarios with hypothetical pricing strategies concerning EV purchase subsidies, and prices for fuel, electricity, and public transport. The scenarios involved the presentation of values specifically calculated depending on the cost and composition of actual mobility tools in the household, and respondents were asked to adapt their household fleet in response. For example, they could remove current or add new vehicles or public transport (PT) passes. The effect of such cost-related interventions on their decisions was modelled in an integrated choice and latent variable (ICLV) model. Our results suggest that the decision to remove a conventional vehicle and/or replace it with an electric vehicle can be effectively promoted by increasing fuel prices, lowering electricity prices, and lowering PT fares. Providing subsidies for the purchase of EVs was found to be ineffective. An analysis of attitudes revealed that people with greater intention to buy an EV are less affected by any pricing strategies. Incentives for removing a conventional vehicle are only effective for people who are more concerned about the environment.
... Several studies compare the EV purchase prices with the purchase prices of ICE in the market by charging an extra price to the referenced vehicle price or giving a discount to the referenced vehicle price (Hackbarth and Madlener, 2013;Hidrue et al., 2011;Tangnaku, 2016). Some studies use specific prices explicitly designed for their work (Glerum et al., 2014;Helveston et al., 2015;Kim et al., 2019). ...
... Regarding the DCE survey, the early works use either Nested Logit Model, Latent Class Model, or Ranked Order Logit to analyze the data (Hidrue et al., 2011;Potoglou and Kanaroglou, 2007). However, most of the recent studies employ either Multinomial Logit (MNL), Mixed Logit Model (MXL), or Hybrid Choice Model (HCM) as a tool for analyzing the data (Bansal et al., 2021;Hackbarth and Madlener, 2013;Helveston et al., 2015;Kim et al., 2019;Ma et al., 2019;Wang et al., 2017). There is an advantage of MXL over MNL as it does not require a restrictive assumption of independence from irrelevant alternatives (IIA), which are adopted in MNL. ...
This study analyzes consumer preferences for EVs using the discrete choice experiment and explores the attitudes toward possible policies on EV stimulation. The 362 participants with a driving license and living in Bangkok participated in the questionnaire survey. The information on the questionnaire includes their characteristics, car usage behavior, environmental preference, and preference for policies on EV stimulation. The binary logit regression analysis reveals that the number of vehicle possessions, ownership of parking space, the price of EV, and fuel cost per month affect the decision to purchase EVs. On the other hand, being female, income, years of car use, maximum driving range of EV, and coverage area of chargers increase the probability of EV purchase. Environmental preferences have a strong positive correlation with EV purchases. Policies involving personal interest and EV sustainability also positively correlate with EV purchases. However, the extreme ecological perspective has an adverse effect. The analysis of the preferences for policies on EV stimulation reveals that monetary policies are the most preferred choice since the participants prioritize the policies favorable to their benefits.
... Previous research has explored how public policy and consumer preferences affect the adoption of new car technologies (Helveston et al., 2015). For instance, Huang et al. (2021) looked into the desire for electric cars in light of the disparities in age groups and geographical locations. ...
The present study attempts to enhance our understanding of the intention to purchase electric vehicles in India and incorporates price value and environmental concern into the Theory of Planned Behavior model. The study was conducted in three phases. Phase I involved developing and testing the research instruments used to collect data. In Phase II, a pilot study was conducted, employing exploratory factor analysis to confirm the dimensionality of the study constructs. Phase III focused on validating the study model against a larger sample size. The data for Phase III was collected using a combination of online and offline approaches and analyzed using AMOS 24.0. The study findings suggest that environmental concern and price value positively influence the attitude toward electric vehicles. The study also supports the positive influence of Theory of Planned Behavior variables—subjective norms, perceived behavioral control, and attitude—on the intention to purchase electric vehicles. The study offers insights to practitioners to encourage the use of electric vehicles and, hence, contributes to the 2030 Sustainable Development Goals as the use of electric vehicles would help to mitigate climate change, improve human health, and enhance the well-being of society.
... As these factors continue to shift, the elasticity of substitution will likely decrease, driving higher adoption rates of electric vehicles. [54][55][56][57]. ...
The transition towards sustainable transportation is critical for Jakarta, where traditional gas-powered vehicles contribute substantially to air pollution and adverse health effects. Battery- Powered Electric Vehicles (BEVs) represent a viable alternative, backed by technological advancements and government interventions like subsidies. However, despite these initiatives, BEV adoption remains influenced by factors beyond just price. This study explores consumer perceptions and the determinants of BEV demand using Interpretative Phenomenological Analysis (IPA). The qualitative research, conducted through semi-structured interviews with 10 male participants aged 25 to 40, examines key factors impacting BEV adoption, such as price sensitivity, availability of substitutes, production costs, and externalities. Additionally, the study identifies the role of information gaps and government awareness campaigns in shaping consumer preferences. Findings indicate that price disparities, substitute availability, and infrastructure limitations significantly influence BEV adoption. Economic considerations, coupled with consumer awareness of environmental impacts, are pivotal in driving demand. The research suggests that enhancing infrastructure, providing accurate information, and implementing supportive policies could accelerate BEV adoption in Jakarta. This study contributes valuable insights for policymakers and stakeholders seeking to promote sustainable transportation and mitigate negative externalities in Indonesia’s capital.
... In addition to understanding travel behaviors, S&Is are frequently used to gauge public perceptions of emerging transportation technologies, infrastructure projects, and urban policies. For instance, S&Is have been instrumental in assessing public attitudes toward ridesharing services (Krueger et al., 2016), autonomous driving technologies (Bansal and Kockelman, 2018;Harper et al., 2016;Kyriakidis et al., 2015;Madigan et al., 2017), and the adoption of electric vehicles and their associated charging infrastructure (Greene et al., 2020;Helveston et al., 2015). They also provide valuable insights into public sentiment toward hypothetical disruptive events, such as earthquakes or epidemics (Lindell et al., 2020), and policies under consideration, such as congestion pricing (Hess and Börjesson, 2019). ...
Surveys and interviews (structured, semi-structured, or unstructured) are widely used for collecting insights on emerging or hypothetical scenarios. Traditional human-led methods often face challenges related to cost, scalability, and consistency. Recently, various domains have begun to explore the use of conversational agents (chatbots) powered by large language models (LLMs). However, as public investments and policies on infrastructure and services often involve substantial public stakes and environmental risks, there is a need for a rigorous, transparent, privacy-preserving, and cost-efficient development framework tailored for such major decision-making processes. This paper addresses this gap by introducing a modular approach and its resultant parameterized process for designing conversational agents. We detail the system architecture, integrating engineered prompts, specialized knowledge bases, and customizable, goal-oriented conversational logic in the proposed approach. We demonstrate the adaptability, generalizability, and efficacy of our modular approach through three empirical studies: (1) travel preference surveys, highlighting multimodal (voice, text, and image generation) capabilities; (2) public opinion elicitation on a newly constructed, novel infrastructure project, showcasing question customization and multilingual (English and French) capabilities; and (3) transportation expert consultation about future transportation systems, highlighting real-time, clarification request capabilities for open-ended questions, resilience in handling erratic inputs, and efficient transcript post-processing. The results show the effectiveness of this modular approach and how it addresses key ethical, privacy, security, and token consumption concerns, setting the stage for the next-generation surveys and interviews.
... Meanwhile, customers in rural areas will be more concerned about cost efficiency and reliability, given the limited availability of charging infrastructure in these areas and the lower level of public understanding and awareness of the benefits of EVs [68]. Socio-economic background influences perceptions of affordability, with higher-income consumers more likely to adopt EVs due to environmental concerns and social status, while lower-income consumers are deterred by high upfront costs and uncertainty about long-term savings [69]. ...
Electric vehicles (EVs) emerged as a help for Indonesia as a pathway to address environmental challenges related to air pollution and greenhouse gas emissions from the transportation sector. Despite governmental efforts, including Presidential Regulation No. 55/2019, EV adoption rates in Indonesia remain low, although sales are increasing annually due to limited charging infrastructure, high upfront costs, and consumer perception. This study distinguishes itself from previous research by moving beyond a singular focus on policy, adoption factors, barriers, or economic opportunities. Instead, it integrates these dimensions into a cohesive analysis while placing particular emphasis on government policies. By adopting this multidimensional approach, the study presents a nuanced understanding of EV adoption in Indonesia, exploring not only the drivers, challenges, and economic potential but also the tangible benefits of EV manufacturing and usage for both producers and consumers within the current regulatory framework. It highlights the transformative impacts of EV adoption on key areas such as job creation, GDP expansion, and energy security, offering strategic insights for policymakers, industry leaders, and stakeholders. Future research could explore rural infrastructure development, local battery production impacts, and long-term economic implications of EV in Indonesia’s ecosystem.
... Research has also focused on the global nature of EV adoption, with studies examining regional differences in adoption rates. Helveston et al. (2015) conducted a comparative study of EV adoption in China and the United States, revealing that while both countries exhibited rapid growth in EV sales, the underlying drivers were markedly different. In China, government mandates and subsidies played a more dominant role, whereas in the United States, consumer preferences and environmental awareness were more influential. ...
As the world moves towards sustainable transportation, the adoption of electric vehicles (EVs) plays a pivotal role in reducing carbon emissions and dependence on fossil fuels. This study provides a comprehensive analysis of global EV adoption trends, drawing on historical data that tracks EV sales and stock across various regions from 2011 onward. By examining the evolution of market shares and the distribution of different powertrain types, such as battery electric vehicles (BEVs), this research uncovers significant regional disparities in EV adoption. The findings highlight key drivers and barriers influencing market growth, providing insights into the varied pace of electrification across the globe. This analysis underscores the need for region-specific policy measures to overcome challenges and accelerate the transition to electric mobility. Ultimately, this study contributes to the broader discourse on achieving an eco-friendly transportation future by offering a nuanced understanding of the global progress towards widespread EV adoption.
... The monthly cost savings of choosing an EV is the effect of the fuel savings or environmental benefits of driving an EV, and is a proxy for the superior affordability of EVs. In general, affordability is the most important factor consumers consider when choosing a vehicle (Li et al. 2020;Liao et al. 2017;Helveston et al. 2015). Each question presents EVs as more economical than internal combustion vehicles. ...
To address climate change, achieving carbon neutrality is imperative. Electric vehicles (EVs) stand out as the primary solution for achieving carbon neutrality in the transportation sector. However, the inconvenience associated with charging impedes widespread adoption of EVs. Dynamic wireless charging system (DWCS) present a promising solution to enhance charging convenience due to their greater accessibility compared to traditional charging infrastructure, along with reduced charging durations. Consequently, DWCS holds significant potential for promoting EV adoption and ensuring sustainability, yet this potential remains underexplored. In this study, a discrete choice model was developed to analyze the impact of charging convenience on vehicle choice through stated preference surveys and to evaluate the effect of enhancing charging convenience through the introduction of DWCS on the activation of EVs. Furthermore, it investigated the spatial distribution of EV activation effects concerning DWCS accessibility and estimated environmental benefits, such as reduced air pollutant emissions and associated economic gains. The introduction of DWCS in Seoul could potentially increase the market share of EVs by approximately 14% and result in air pollution reduction benefits amounting to 140.82 B KRW ($106.06 M) over a period of 10 years. These results underscore the significant potential of DWCS in promoting EV adoption and sustainability, hinting at its pivotal role in advancing toward widespread EV adoption.
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... Moreover, California residents pay considerably more than in other US states [79]. Another comparative study reported that Chinese people are ready to pay more than Americans for using EVs 13 [129]. Appropriate policy (e.g., suspension of purchase and driving restrictions, access to bus lanes, exemption from sales tax and road tolls) incentivize Chinese people to pay more. ...
This comprehensive state-of-the-art literature review investigates the status of electric vehicle (EV) market share and the key factors that affect EV adoption. Investigating the current
scenarios of EV, this study observes a rapid increase in the number of EVs and charging stations in different parts of the world. It reports that people’s socio-economic features (e.g., age, gender, income, education, vehicle ownership, home ownership, political affiliation) significantly influence EV adoption. Moreover, factors such as high driving range, fuel economy, safety technology, financial incentives, availability of free charging stations, and the capacity of EVs to contribute to
decarbonization emerge as key motivators for EV purchases. The literature also indicates that EVs are predominantly used for short-distance travel and users commonly charge their vehicles at home. Most users prefer fast chargers and maintain a high state of charge (SoC) to avoid unforeseen situations. Despite the emergent trend, there is a disparity in charging infrastructure supply compared to the growing demand. Thus, there is a pressing need for more public charging stations to meet the surging charging demand. The integration of smart charging stations equipped with advanced technologies to optimize charging patterns based on energy demand, grid capacity, and
people’s demand can help policymakers to leverage smart city movement. This paper makes valuable contributions to the literature by presenting a conceptual framework articulating the
factors of EV adoption, outlying their role in achieving smart cities, and proposing suggestions for future research directions.
... BEVs have excellent GHG reduction potential as power grids shift to a lower carbon electricity generation mix. However, perceptions about vehicle range, charging infrastructure availability, and charging time [7][8][9], as well as cost and resource material requirements due to larger-sized batteries [10][11][12][13], pose challenges to widespread adoption of BEVs in the short term. Hydrogen-powered vehicles share similar traits to BEVs in that they have no tailpipe emissions and future potential for excellent GHG reduction despite most of the present-day commercially available hydrogen coming from GHG-emitting sources, such as natural gas reforming [14]. ...
Plug-in hybrid electric vehicles (PHEVs) are designed to enable the electrification of a large portion of the distance vehicles travel while utilizing relatively small batteries via taking advantage of the fact that long-distance travel days tend to be infrequent for many vehicle owners. PHEVs also relieve range anxiety through seamless switching to hybrid driving—an efficient mode of fuel-powered operation—whenever the battery reaches a low state of charge. Stemming from the perception that PHEVs are a well-rounded solution to reducing greenhouse gas (GHG) emissions, various metrics exist to infer the effectiveness of GHG reduction, with utility factor (UF) being prominent among such metrics. Recently, articles in the literature have called into question whether the theoretical values of UF agree with the real-world performance of PHEVs, while also suggesting that infrequent charging was the likely cause for observed deviations. However, it is understood that other reasons could also be responsible for UF mismatch. This work proposes an approach that combines theoretical modeling of UF under progressively relaxed assumptions (including the statistical distribution of daily traveled distance, charging behavior, and attainable electric range), along with vehicle data logs, to quantitatively infer the contributions of various real-world factors towards the observed mismatch between theoretical and real-world UF. A demonstration of the proposed approach using data from three real-world vehicles shows that all contributing factors could be significant. Although the presented results (via the small sample of vehicles) are not representative of the population, the proposed approach can be scaled to larger datasets.
... Do Paço and Reis, 2012; Dua et al., 2021;Graham-Rowe et al., 2012;Guo et al., 2022;He et al., 2018;Helveston et al., 2015;Ianole-Călin et al., 2020;Kandasamy et al., 2020;Lee and Lovellette, 2011;Sovacool et al., 2019;Tu and Yang, 2019;Westaby and Braithwaite, 2003;Garcia et al., 2007. ...
... Recent trends in the EV industry include several important aspects that contribute to the technological development and adoption of EVs. Policy plays a key role in driving the growth of EVs with various incentives, regulations, and emission reduction targets set by governments in various countries [17,22,23]. With these various factors, the trend of EVs is increasing and is expected to continue to grow in the future. ...
Electric vehicles (EVs) have seen significant growth due to the increasing awareness about environmental concerns and the negative impacts of internal combustion engine vehicles (ICEVs). The electric vehicle landscape is rapidly evolving, with EV policies, battery, and charging infrastructure and electric vehicle-to-everything (V2X) at its forefront. This review study used a bibliometric analysis of the Scopus database to investigate the development of EV technology. This bibliometric study specifically focuses on analyzing electric vehicle trends, policy implications, lithium-ion batteries, EV battery management systems, charging infrastructure, EV smart charging technologies, and V2X. Through this detailed bibliometric analysis discussion, we aim to provide a better understanding of holistic EV technology and inspire further research in electric vehicles. The analysis covers the period from 1990 to 2022. This bibliometric analysis underscores the interplay of electric vehicle policies, technology, and infrastructure, specifically focusing on developments in battery management and the possibility of V2X technology. In addition, this bibliometric analysis suggests the synchronization of international electric vehicle policy, advancement of battery technology, and promotion of the use of EV smart charging and V2X systems. This bibliometric analysis emphasizes that the expansion of EVs and sustainable mobility relies on a comprehensive strategy that encompasses policy, technology, and infrastructure. This bibliometric analysis recommends fostering collaboration between different sectors to drive innovation and advancements in electric vehicle technology.
... The development of NEV in China has also attracted worldwide attention (Cao et al., 2022a;Chen, 2022). Many scholars have carried out related research on China's NEV industry in terms of policy analysis such as government subsidies and charging facilities (Xiao et al., 2020), consumer behavior (Helveston et al., 2015), market diffusion (He et al., 2020) and its impact. Although China has a huge market for NEV, insufficient R&D capability, weak technical force and lack of key core technologies are still bottlenecks hindering the further development of NEV (Cao et al., 2022a;Han et al., 2022;Yuen, 2016). ...
The purpose of this paper is to consider the joint action of structural, cognitive and relational social capital, and to explore the formation mechanism of the innovation network of new energy vehicles (NEV). The research data come from China's NEV cooperative invention patent applications from 2001 to 2019. This paper uses the exponential random graph model (ERGM) to study the impact of different dimensions of social capital on the NEV industry-university-research (I-U-R) innovation network. The results show that from the perspective of structural capital, the closed network structure has a positive impact on the formation of NEV I-U-R innovation network. From the perspective of cognitive capital, the homogeneity of knowledge base has a positive effect on the formation of the NEV I-U-R innovation network, and the innovation subjects with the same knowledge base breadth and the same knowledge base depth are more inclined to form a cooperative relationship. For relational capital, institutional environment similarity and organizational structure similarity are important factors affecting the formation of NEV I-U-R innovation network to a similar extent. The findings of this study provide scientific references for promoting the sustainable development of I-U-R innovation network in NEV industry.
This study assesses the worldwide research trajectories of the Scopus-indexed literature on electric vehicle adoption spanning three decades. Employing the five-phase framework proposed by Zupic and Carter (2015) and utilizing the Vosviewer and Biblioshiny software, the research analyses the intellectual and social networks within the literature. The analysis of international collaboration reveals a strong regionalization in the domain, evidencing the limited collaboration between developed and developing countries. The thematic map illustrates the emerging focus on the key themes, including battery charging, environmental concerns, and transportation infrastructure in the realm of electric vehicles. Co-citation analysis further illuminated four clusters, shedding light on the prominence of monetary incentives, consumer behavioral factors and the necessity of charging infrastructure for speedy electric vehicle uptake. The study exhibits novelty by illustrating theme-based cluster analysis, distinguishing trends before and after the introduction of UN sustainable development goals. The study provides a roadmap for future researchers by identifying the prominent, emerging and niche themes, especially post-2015, which can be explored in greater depth. The findings of the study offer valuable insights for the policymakers and marketers aiming to accelerate the electric vehicle uptake by revealing the crucial role of monetary incentives and charging infrastructure.
This comprehensive state-of-the-art literature review investigates the status of the electric vehicle (EV) market share and the key factors that affect EV adoption with a focus on the shared vision of vehicle electrification and the smart city movement. Investigating the current scenarios of EVs, this study observes a rapid increase in the number of EVs and charging stations in different parts of the world. It reports that people's socioeconomic features (e.g., age, gender, income, education, vehicle ownership, home ownership, and political affiliation) significantly influence EV adoption. Moreover, factors such as high driving range, fuel economy, safety technology, financial incentives, availability of free charging stations, and the capacity of EVs to contribute to decarbonization emerge as key motivators for EV purchases. The literature also indicates that EVs are predominantly used for short-distance travel and users commonly charge their vehicles at home. Most users prefer fast chargers and maintain a high state of charge (SOC) to avoid unforeseen situations. Despite the emergent trend, there is a disparity in charging infrastructure supply compared to the growing demand. Thus, there is a pressing need for more public charging stations to meet the surging charging demand. The integration of smart charging stations equipped with advanced technologies to optimize charging patterns based on energy demand, grid capacity, and people's demand can help policymakers leverage the smart city movement. This paper makes valuable contributions to the literature by presenting a conceptual framework articulating the factors of EV adoption, outlying their role in achieving smart cities, suggesting policy recommendations to integrate EVs into smart cities, and proposing suggestions for future research directions.
In this article, we propose an assessment framework for zero-emission vehicles (ZEVs) in Germany using economic and customer-relevant criteria, with a focus on the mobility needs of individuals. Developing this framework required data obtained from four different sources: (1) literature, (2) semi-structured interviews, (3) a survey, and (4) market research. First, we derived the criteria relevant to assessing ZEVs from the literature and from semi-structured interviews. These interviews were conducted with individuals who have driving experience with both battery and fuel cell electric vehicles. Seven criteria were found to be particularly relevant for assessing ZEVs: greenhouse gas emissions, infrastructure availability, charging/refueling time, range, spaciousness, total costs, and driving dynamics (in descending order of importance). Second, we conducted a survey among 569 ZEV drivers and ZEV-interested individuals in order to weight these seven criteria. This survey was based on the Analytic Hierarchy Process approach. We then used market research to assign value scores to each criterion, representing the extent to which a particular ZEV meets a given criterion. Finally, we combined the value scores with the criteria weights to create the assessment framework. This framework allows for a transparent assessment of different ZEVs from the perspective of (potential) customers, without the need to repeatedly involve the surveyed participants. Our study is primarily useful for mobility planners, policymakers, and car manufacturers to improve ZEV infrastructure and support transportation systems’ transition towards low-carbon mobility.
Most net-zero emissions targets require electrification of the entire light-duty vehicle fleet, and before that the electrification of all new vehicle sales. In this paper, we review literature on demand-side issues related to achieving 100% zero-emissions vehicle (ZEV) sales, focusing on plug-in electric vehicles (PEVs). We discuss potential demand-side challenges to increasing PEV sales and related research gaps, including consumer factors (perceptions, knowledge, and consumer characterises), demand-focused policy (incentives), infrastructure, and energy prices. While global PEV sales have substantially increased in recent years, several challenges remain: some demographic groups are currently underrepresented among PEV buyers (e.g. renters, lower income buyers), some car drivers are resistant to PEVs, incentives are influential but have predominantly benefited higher-income new-car buyers and are being phased out, infrastructure is not sufficiently developed or equally distributed, infrastructure is not user friendly, and some households lack charging access. Some issues we identify may be related to the early stage of the PEV market, though will need to be addressed to reach higher PEV sales and PEV fleet shares. Finally, we outline areas where more research is needed to understand and guide the PEV transition.
The transition towards electrifying the transport sector in China presents several new opportunities and solutions to accomplish the requirements, which are essential to address critical issues of energy resilience, air purity, and the development of sustainable energy systems. Accomplishing sustainability objectives faces significant challenges attributed to several adoption barriers of electric vehicles (EVs), therefore, a variety of different incentives have been considered, or being executed to handle these barriers effectively. The Chinese government is actively paying attention to augment the market penetration and consumer adoption of EVs through the implementation of numerous demonstration programs with favorable transportation policies. This study provides a comprehensive review of the key factors influencing adoption barriers and the effectiveness of EV policies. The primary objective is to extensively describe significant adoption issues, technical considerations, plans, and policies aimed at facilitating for massive deployment of EVs through an integrative review approach. This study also aims to highlight how government policies are systematically linked with each other to offer support, speedy progress, and achieve notable growth in the adoption of EVs in China. The final aim is to develop a comprehensive conceptualized research framework through extension in the Theory of Planned Behavior based on substantial barriers and policies for extensive adoption of EVs. The primary policy recommendations for different segments offer diverse insights for stakeholders to envision the future of electrified transportation.
The demand for electric cars has significantly increased in recent years, but it represents only a small fraction of the total new vehicles sold globally. This study aims to analyze the factors influencing consumer purchase intentions towards electric cars in the Greater Jakarta area (Jabodetabek). Using a survey method, data were collected from individuals who are considered capable of purchasing electric cars, either through installment plans or cash payments, and analyzed using partial least squares structural equation modeling (PLS-SEM) to test the research hypotheses. The results indicate that government incentives, ease of access to charging infrastructure, perception of resale value, product diversification, social influence, environmental concern, and consumer attitudes significantly and positively influence purchase intentions towards electric cars. However, the perception of purchase price did not show a significant effect. The findings suggest that strategic initiatives by policymakers and manufacturers, such as continuous government incentives, expansion of charging infrastructure, and effective marketing strategies leveraging social influence and environmental benefits, are crucial for promoting electric car adoption in Jabodetabek. This study provides valuable insights for understanding consumer behavior towards electric cars in emerging markets and offers useful information for enhancing market penetration.
Decarbonizing private transport is crucial in the highly polluted cities of low- and middle-income countries, such as Vietnam. In motorcycle-dominated megacities, this means gradually converting the conventional motorcycle fleet into e-motos. In this study set in Hanoi, we examined the factors that explain existing e-moto riders' attachment to this mode. For this purpose, we built a novel theoretical framework called VEMM (Values and Expectations in Mobility Model), which combines the constructs of the Expectation Confirmation Model (ECM) and the Value-Based Adoption Model (VBAM). Survey data collected from 481 e-moto users in Hanoi were analysed by applying a Partial Least Squares Structural Equation Model (PLS-SEM). We found that the enduring intention to use e-motos is positively related to perceived value, user satisfaction, and perceived usefulness. We controlled for riders' gender, age, education, and living area but only education was statistically significant. Overall, we found that the perceived value of e-motos owed more to the perceived benefits (usefulness, enjoyment, and eco-friendliness) than the perceived costs (risk, price, technicality, habit, and stigma) of this mode. Based on these findings, we recommend that manufacturers consider the critical role of values when designing and promoting e-motos.
The Indian automative industry shifts towards the transition from gasoline- powered vehicles to Electric Vehicles (EVs) emphasizing the understanding of key drivers for the EVs adoption. The study investigated the factors affecting EVs adoption with moderating effects of Technology Readiness and Social Influence. The study investigated how TR and SI moderated the relationship between key variables like Perceived Benefits (PB), Environmental Attitude (EA), Government Incentives (GI) and Perceived Barriers (PBA). The quantitative research approach was used to collect data from existing and non-existing EV owners. Technology Readiness (TR) was found to moderate the relationship between Perceived Benefits (PB) and Intentions to Purchase (IP) indicating technology ready people can translate their perceived benefits into purchase decision. Social Influence (SI) positively moderated the relationship between Perceived Benefits (PB) and Intentions to Purchase (IP) as well as Perceived Barriers (PBA) and Intentions to Purchase (IP) indicating individuals are highly affected by social influence to overcome their perceived barriers to purchase EVs. Technology Readiness (TR) was not found to be moderated on Perceived Barriers (PBA) and Government Incentives (GI). Social Influence (SI) was also not found to moderate on Environmental Attitude (EA).
New energy vehicles (NEVs), owing to their low carbon emission, have gained immense importance to achieve the net-zero emission target. The global NEVs market has grown significantly over the last decade. China, the United States (US), and Europe are the leading markets for NEVs. This study systematically and critically reviews NEV literature on consumer behavior pertaining to NEV adoption. An attempt is made to uncover the current research trends, research settings, theoretical perspectives, and key factors influencing consumer behavior towards NEVs. These factors are further categorized into five broad factors: (a) economic factors, (b) policy and regulatory factors, (c) psychological factors, (d) infrastructural and technological factors, and (e) demographic factors. Through a critical analysis of existing theories, this study delineates the complex phenomenon of consumer behavior towards NEV adoption, offering a holistic understanding of the key factors influencing consumer behavior. This review suggests that purchasing price, charging infrastructures, consumers’ attitudes towards the environment, and government policies are decisive to NEV adoption. This study contributes to the NEV adoption literature by proposing an integrated theoretical framework. Further, it outlines the managerial and policy implications for transitioning towards NEVs to achieve net-zero emission targets.
Autonomous vehicles (AVs) can potentially revolutionize the transportation system, but the extent of their impact may depend on users’ attitude and AV-related policies. This paper seeks to provide a holistic view of the impacts of policy, attitudinal, and sociodemographic factors on AV adoption intention. An extension to the original Technology Acceptance Model is proposed by incorporating perceived enjoyment (i.e., how enjoyable respondents think using an AV will be) and policy factors. Four policy factors include the availability of financial incentives, awareness campaigns, traffic policies, and legislative measures. Using 1,831 survey responses in China, multiple linear regression models were estimated to quantify the direct impacts of the proposed policy and attitudinal factors on AV adoption intention. They also illustrate the moderating effects of these policies on the relationships between attitudinal factors and AV adoption intention. The study findings may be used to design future policy measures to facilitate a smooth transition to an era of AVs.
This study systematically examines the literature of electric vehicle (EV) purchase intention and consumer behavior using a bibliometric method to unveil three main research questions: 1) identifying influential publications, authors, and journals; 2) analyzing the thematic evolution of research over time; and 3) identifying emerging research directions. The main objective is to provide a comprehensive understanding of the current state of knowledge and to guide future research in this evolving field. A comprehensive bibliometric analysis was conducted, using Scopus statistics analysis, R-Studio Biblioshiny and VOSviewer, comprising 687 publications authored by 1743 researchers representing 34 different countries with the dataset sourced from the Scopus database from 2010 to 2023. To achieve a nuanced understanding of the research landscape, a multifaceted approach was adopted, including detailed citation analysis, author co-citation analysis, keyword analysis, and thematic mapping. Through meticulous analysis, this study identifies the most influential publications, authors, and journals in the domain of EV purchase intentions and consumer behaviors. It also traces the evolution of themes over time and identifies emerging research directions, providing valuable insights into the trajectory and future avenues of inquiry within this field. The findings contribute to a deeper understanding of the dynamics shaping research in the realm of EVs. The insights gained contribute significantly to advancing knowledge in this crucial domain, offering theoretical insights and practical implications for policymakers, businesses, manufacturers, and academics.
Policies play a pivotal role in fostering the growth of new energy vehicles. This paper delves into the benefits and risks associated with the development of new energy vehicles under varying policy landscapes. Firstly, the intricate relationship between the competition and growth of new energy vehicles and internal combustion engine vehicles is captured through the application of the extended Lotka-Volterra model. Secondly, a simulation flow diagram is constructed, taking into account the actual mediums of policy impact and drawing insights from diverse research methodologies. This flow diagram enables a comprehensive evaluation of environmental benefits, health risks, and traffic pressure. Finally, a critical evaluation of the actual impact of policies on new energy vehicles is conducted, along with an exploration of the root causes of potential policy paradoxes. The findings reveal that a standalone infrastructure support policy, research and development support policy, or carbon tax policy can, in certain periods, lead to an increase in carbon emissions and traffic pressure. Although the driving restriction policy does not exert negative impacts on the environment, health, and traffic at the national level, it can result in a surge in the total vehicle population. Therefore, it is imperative to adopt a combination of policies to mitigate the potential risks associated with any single policy.
The aim of the study is to explore factors influencing prospect's willingness to purchase electric vehicles and examine the influence of those factors. The scope of the study confined to examine factors influencing Andhra Pradesh Capital Region (Amaravathi) prospects' willingness to purchase electric vehicles. The approach of the study is qualitative but the method is quantitative, exploratory and descriptive in nature. The data collected using well-structured questionnaire from randomly selected 1200 prospects of electric vehicles. The collected information/data transcribed/analysed using descriptive statistical tools and regression. The results revealed that performance attributes are strongly associates with willingness to buy electric vehicles. The results, statistical analysis regression value 'r' exhibited that there is lot of scope to purchase electric vehicles when the performance attributes effectively assorted as per the requirements of the customer. Finally, this paper provides implications to manufacturers and scope for further research.
Current research aims to investigate the experts' opinions on the key factors (KFs) influencing the intention to use smart hybrid electric vehicles (SHEV). The objective is to systematically compile the psychological, social, and cultural attributes and present a comprehensive taxonomy of these key factors. The study employs a content analysis approach to compile experts' opinions addressing key factors related to the intention to use SHEV in an oil-rich economy. Through an in-depth analysis of the identified key factors in interviews, a taxonomy comprising 40 key codes is formulated. These codes are classified into ten main themes. The significance of this study lies in the development of a taxonomy that organizes knowledge, provides a common language, and offers researchers a conceptual framework for clear navigation and easier comprehension when categorizing key factors and establishing relationships between these categories.
Electric vehicles (EVs) are eco-friendly automobile innovation and is evolving as a promising solution to global challenges such as energy scarcity and environmental pollution. However, adoption of EVs in Ghana remains limited. Hence, the study examined the underlying factors affecting EVs adoption in Ghana using web-based survey involving 902 participants across Ghana. The study employed Innovation Diffusion Theory and Binary Logistic Regression to address the objective. The result shows that gender, driving license, driving experience, vehicle ownership, source of information on EVs, and knowledge of EVs were significant demographic factors related to the adoption of EVs. Apparently, being male, middle age, driving experience ranging from 6 to 20 years, higher-income earners, private car owners, familiarity with EVs, and those with knowledge of EVs from
sources like internet and school were more inclined to adopt EVs. Moreover, factors such as economic benefits, availability of charging infrastructure, environmental concerns, and self-image are more likely to increase EVs adoption. Categorically, the perception that EVs: save fuel, provide economic advantages, reduced air pollution, produced low carbon emissions, protect the environment, are easy to maintain and availability of repair resources are more likely to motivate the adoption of EVs. Furthermore, charging stations availability, personal image enhancement, and peer influence are closely related to the adoption of EVs. However, not opting to adopt EVs is closely related to concerns about tax incentives associated with owning an EV, the distance traveled before recharging, and the belief that EVs have low noise levels. Policy implications were also discussed.
The critical nexus of sustainable manufacturing and logistics within the supply chain is pivotal for safeguarding the environment and navigating uncertain market landscapes. This study meticulously explores the intricate interplay between ecological commitments and the adaptability of risk coefficients, scrutinizing their impact on operational efficacy, production dynamics, and value chain optimization. Firstly, we have designed a dual-channel supply chain model that includes the supplier, green manufacturer, and sustainable retailer. Pricing and greening decisions across the multi-layered supply chain have been optimized meticulously by incorporating risk aversion effects into the utility function. Market demand is uncertain and highly sensitive to product price and level of green attributes. Secondly, in order to compare the optimal decision, the effectiveness of the supply chain has been analyzed under different business perspectives, which are centralized, decentralized, and rewards contracts with target green level with the help of the leader-follower Stackelberg game approach. The analytical results delve into the impact of supply chain contracts, revealing the way the retailer’s incentives shape manufacturer’s green initiatives and members’ risk aversion preferences. The numerical findings illustrate the manufacturer’s advancement in green manufacturing, achieving peak green product levels and higher profitability, contrasting centralized and decentralized marketing scenarios in the dual-channel model. Finally, some managerial insights with a sensitivity of major elasticity parameters have been addressed to achieve the performance of the supply chain.
The phenomenon of rise and fall in the ocean waters, called tides, is due to the attractive forces between the celestial bodies; Sun, Earth and the Moon. When the ocean water rises to a maximum extent, it is called spring tide and when they fall off to the lowest possible extent, it is called neap tide. With progress in technology, the usage of electric and electronic devices is exponentially increasing and there is a need to produce extra power other than the existing, in order to meet the future demands. Tidal energy can be considered as one of the best existing source of renewable energies. Unlike the wind, solar, thermal energy etc., tidal energy is something that has a long term perspective and it can be forecasted more accurately. Tidal energy is clean and not depleting. Because of these features it is unique and suitable to use it as a power generating source in the future. There are various types of tidal power plants across the world with varying tidal elevation. Also, the method of conversion of tidal energy into electrical energy is site specific. But generally, the method followed for extracting energy from tides is similar to the conventional hydroelectric power plants. In this paper, the tides at some locations across the world and along the Indian coast, tidal power plants across the world, resource allocation of tidal power plants, advantages and disadvantages of tidal power will be reviewed from the literature.
Keywords: Tidal energy, tidal barrage, tidal stream, electrical energy.
The promotion of hybrid and electric vehicles (EVs) has been proposed as one promising solution for reducing transport energy consumption and mitigating vehicular emissions in China. In this study, the energy and environmental impacts of hybrid and EVs during 2010-2020 were evaluated through an energy conversion analysis and a life cycle assessment (LCA), and the per-kilometer energy consumptions of gasoline, coal, natural gas (NG), oil, biomass, garbage and electricity for EVs and HEVs were estimated. Results show that the EVs and HEVs can reduce the energy consumption of vehicles by national average ratios of 17%-19% and 30%-33%, respectively. The study also calculated the detailed emission factors of SO2, NOX, VOC, CO, NN3, PM10M, PM2.5, OC, EC, CO2, N2O, CH4, Pb and Hg. It is indicated that the HEVs can bring significant reductions of NOX, VOC and CO emissions and lesser decreases of SO2 and CO2 for a single vehicle. The EVs could decrease many of the VOC, NN3, CO and CO2 emissions, but increase the SO2, NOX and particles by 10.8-13.0, 2.7-2.9 and 3.6-11.5 times, respectively. In addition, the electricity sources had significant influence on energy consumption (EC) and emissions. A high proportion of coal-fired energy resulted in large ECs and emission factors. The total energy consumption and pollutants emission changes in 2015 and 2020 were also calculated. Based on the energy use and emission analysis of HEVs and EVs, it is suggested that EVs should be promoted in the regions with higher proportions of hydropower, natural gas-fired power and clean energy power, while HEVs can be widely adopted in the regions with high coal-fired power ratios. This is to achieve a higher energy consumption reduction and pollutant emission mitigation. Moreover, the results can also provide scientific support for the total amount control of regional air pollutants in China.
This book describes the new generation of discrete choice methods, focusing on the many advances that are made possible by simulation. Researchers use these statistical methods to examine the choices that consumers, households, firms, and other agents make. Each of the major models is covered: logit, generalized extreme value, or GEV (including nested and cross-nested logits), probit, and mixed logit, plus a variety of specifications that build on these basics. Simulation-assisted estimation procedures are investigated and compared, including maximum simulated likelihood, method of simulated moments, and method of simulated scores. Procedures for drawing from densities are described, including variance reduction techniques such as anithetics and Halton draws. Recent advances in Bayesian procedures are explored, including the use of the Metropolis-Hastings algorithm and its variant Gibbs sampling. No other book incorporates all these fields, which have arisen in the past 20 years. The procedures are applicable in many fields, including energy, transportation, environmental studies, health, labor, and marketing.
The scaling approach is a statistical estimation method which allows for differences in the amount of unexplained variation in different types of data which can then be used together in analysis. In recent years, this approach has been tested and recommended in the context of combining Stated Preference and Revealed Preference data. The paper provides a description of the approach and a historical overview. The scaling approach can also be used to identify systematic differences in the variance of choices within a single Stated Preference data set due to the way in which the hypothetical choice situations are presented or the responses are obtained. The paper presents the results of two case studies — one looking at rank order effect and the other at fatigue effect. Scale effects appear to exist in both cases: the amount of unexplained variance is shown to increase as rankings become lower, and as the number of pairwise choices completed becomes greater. The implications of these findings for the use of SP ranking tasks and repeated pairwise choice tasks are discussed.
In models with unobserved taste heterogeneity, distributional assumptions can be placed in two ways: (1) by specifying the
distribution of coefficients in the utility function and deriving the distribution of willingness to pay (WTP), or (2) by specifying the distribution of WTP and deriving the distribution of coefficients. In general the two approaches are equivalent, in that any mutually compatible
distributions for coefficients and WTP can be represented in either way. However, in practice, convenient distributions, such as normal or log-normal, are usually
specified, and these convenient distributions have different implications when placed on WTP’s than on coefficients. We compare models that use normal and log-normal distributions for coefficients (called models in
preference space) with models using these distributions for WTP (called models in WTP space). We find that the models in preference space fit the data better but provide less reasonable distributions of WTP than the models in WTP space. Our findings suggests that further work is needed to identify distributions that either fit better when applied in
WTP space or imply more reasonable distributions of WTP when applied in preference space.
Discrete choice models estimated using hypothetical choices made in a survey setting (i.e., choice experiments) are widely used to estimate the importance of product attributes in order to make product design and marketing mix decisions. Choice experiments allow the researcher to estimate preferences for product features that do not yet exist in the market. However, parameters estimated from experimental data often show marked inconsistencies with those inferred from the market, reducing their usefulness in forecasting and decision making. We propose an approach for combining choice-based conjoint data with individual-level purchase data to produce estimates that are more consistent with the market. Unlike prior approaches for calibrating conjoint models so that they correctly predict aggregate market shares for a "baseline" market, the proposed approach is designed to produce parameters that are more consistent with those that can be inferred from individual-level market data. The proposed method relies on a new general framework for combining two or more sources of individual-level choice data to estimate a hierarchical discrete choice model. Past approaches to combining choice data assume that the population mean for the parameters is the same across both data sets and require that data sets are sampled from the same population. In contrast, we incorporate in the model individual characteristic variables, and assert only that the mapping between individuals' characteristics and their preferences is the same across the data sets. This allows the model to be applied even if the sample of individuals observed in each data set is not representative of the population as a whole, so long as appropriate product-use variables are collected that can explain the systematic deviations between them. The framework also explicitly incorporates a model for the individual characteristics, which allows us to use Bayesian missing-data techniques to handle the situation where each data set contains different demographic variables. This makes the method useful in practice for a wide range of existing market and conjoint data sets. We apply the method to a set of conjoint and market data for minivan choice and find that the proposed method predicts holdout market choices better than a model estimated from conjoint data alone or a model that does not include demographic variables.
We assess the economic value of life-cycle air emissions and oil consumption from conventional vehicles, hybrid-electric vehicles (HEVs), plug-in hybrid-electric vehicles (PHEVs), and battery electric vehicles in the US. We find that plug-in vehicles may reduce or increase externality costs relative to grid-independent HEVs, depending largely on greenhouse gas and SO(2) emissions produced during vehicle charging and battery manufacturing. However, even if future marginal damages from emissions of battery and electricity production drop dramatically, the damage reduction potential of plug-in vehicles remains small compared to ownership cost. As such, to offer a socially efficient approach to emissions and oil consumption reduction, lifetime cost of plug-in vehicles must be competitive with HEVs. Current subsidies intended to encourage sales of plug-in vehicles with large capacity battery packs exceed our externality estimates considerably, and taxes that optimally correct for externality damages would not close the gap in ownership cost. In contrast, HEVs and PHEVs with small battery packs reduce externality damages at low (or no) additional cost over their lifetime. Although large battery packs allow vehicles to travel longer distances using electricity instead of gasoline, large packs are more expensive, heavier, and more emissions intensive to produce, with lower utilization factors, greater charging infrastructure requirements, and life-cycle implications that are more sensitive to uncertain, time-sensitive, and location-specific factors. To reduce air emission and oil dependency impacts from passenger vehicles, strategies to promote adoption of HEVs and PHEVs with small battery packs offer more social benefits per dollar spent.
Vehicles that can run on both electricity and gasoline—so-called plug-in hybrid electric vehicles (PHEVs)—are proposed as both a near-term technology to achieve energy and environmental goals and a transitional step toward viable all-electric vehicles addressing many of the same goals. Whether PHEVs meet any of their goals depends not only on their design and performance on standardized drive cycles, but also on drivers’ travel and refueling/recharging behaviors. To replace assumptions with observations of potential PHEV drivers’ behavior in market and impact analyses, we conducted an internet-based survey of 2,373 new car-buying households in the United States. The instrument was implemented in three separate pieces, requiring multiple days for households to answer questions, conduct a review of their own driving and parking patterns, and then complete a sequence of PHEV design exercises. In this paper, we draw five conclusions from the resulting data. First, most new vehicle buyers are unaware of PHEVs in particular and are confused about electric-drive terminology commonly used by experts. Second, at least half of our target population is already equipped for at-home vehicle recharging, but currently have little opportunity for recharging at their workplace or other locations. Third, we observed widely varied interests in four possible PHEV attributes—fuel economy in both charge-depleting (CD) and charge sustaining (CS) operation, blended vs. all-electric operation, the distance over which the vehicle is in CD mode, and recharging speed. Still, the appeal of increased fuel economy appears to be highest and that of faster recharging to be lowest. Further, there is little interest in all-electric operation. Fourth, given the previous two points, we estimate that about a third of the target population has both the infrastructure to recharge a PHEV and interest in a vehicle with plug-in capabilities. Fifth, our recharge scenarios de
This paper develops new techniques for empirically analyzing demand and supply in differentiated products markets and then applies these techniques to analyze equilibrium in the U.S. automobile industry. Our primary goal is to present a framework which enables one to obtain estimates of demand and cost parameters for a broad class of oligopolistic differentiated products markets. These estimates can be obtained using only widely available product-level and aggregate consumer-level data, and they are consistent with a structural model of equilibrium in an oligopolistic industry. When we apply the techniques developed here to the U.S. automobile market. we obtain cost and demand parameters for (essentially) all models marketed over a twenty year period.
The 2002 Annual Energy Review published by Japan Energy Society consists of 81 articles covering the following subjects: present situation in energy supply and demand, including trends in energy supply and demand, trends in businesses related to energy supply and demand, energy conversion (petroleum refining, electric power generation, city gas, LPG, and cokes), energy consuming industries (metallurgy, petrochemistry, transportation, cement manufacture, and paper pulp production); trends in development and study of methods of using energy resources, such as petroleum, coal, natural gas, atomic power, natural energy (hydro-, solar, geothermal, wind, ocean, and biomass), energy from waste materials, and secondary energy sources, such as hydrogen and alcohol; trends in development and studies of energy conversion techniques, including combustion theory (solid, liquid, and gas combustion), trends in boilers (boilers for steam power generation, general industrial boilers, and marine boilers), trends in industrial furnaces, trends in engines, trends in heat energy systems ( co-generation and heat pump), and trends in highly efficient electric power generation, including MHD generator, fuel cells (phosphoric acid fuel cells, high temperature fuel cells, solid high-molecular fuel cells), fluidized bed combustion, gas turbine power generation, super-critical pressure power generation, electric power transmission and storage; environment, including environmental problems trends in global and national scales and trends in developing environment protection techniques; energy related standards (JIS and ISO); and list of national meetings related to energy and environment.
The global locus of manufacturing has been changing dramatically over the last three decades, driven by industrializing nations, most prominently China. Classical economics suggests that global productivity gains achieved by shifting the location of manufacturing will outweigh the losses ( 1 ). But shifts in the global locus of manufacturing may affect not just production costs, but the nature and pace of technological change.
We assess existing and potential charging infrastructure for plug-in vehicles in US households using data from the American Housing Survey and the Residential Energy Consumption Survey. We estimate that less than half of US vehicles have reliable access to a dedicated off-street parking space at an owned residence where charging infrastructure could be installed. Specifically, while approximately 79% households have off-street parking for at least some of their vehicles, only an estimated 56% of vehicles have a dedicated off-street parking space – and only 47% at an owned residence. Approximately 22% vehicles currently have access to a dedicated home parking space within reach of an outlet sufficient to recharge a small plug-in vehicle battery pack overnight. Access to faster charging, required for vehicles with longer electric range, will usually require infrastructure investment ranging from several hundred to several thousand dollars, depending on panel and construction requirements. We discuss sensitivity of results to uncertain factors and implications for the potential of mainstream penetration of plug-in vehicles.
Recent Chinese economic and energy policies recognize the transportation sector as a key element in the nation's effort to meet its energy and air quality goals. The development of alternative fuel vehicle (AFV) has been considered as a particularly promising strategy. AFV-related policies can be traced back to the eighth Five-Year Plan period (i.e., 1991–1995). All the work during the last twenty years has cumulatively prompted the transition of AFV development from policy-making to actual implementation and from research and development (R&D) to mass production. The year of 2009 is significant for the AFV industry in China in that the central government announced the “Plan on Shaping and Revitalizing the Auto Industry”. This Plan launched a demonstration program of electric vehicle (EV) deployment in 13 Chinese cities and set the national goal of manufacturing 0.5 million AFVs in three years. To better understand the current status, problems and uncertainties existed in the EV deployment in China, this paper reviewed the relevant policies and reported a survey with the pilot cities during the summer of 2009. Based on the survey findings, this paper developed a number of recommendations to help address the issues found in the demonstration program to date.
Previous models of auto-type choice have not been able to disentangle very much of the structure of the household's auto-choice decision: the models assumed that very few auto characteristics affect choice, and often these few parameters were estimated with low precision. Hence the models had only limited use in forecasting the effects of government policies to influence transportation energy consumption. The present paper introduces a multinomial logit model for the type of car that households will choose to buy. The model includes a large variety of auto characteristics as explanatory variables, as well as a large number of characteristics of the household and the driving environment. The model fits the data quite well, and all of the variables enter with the correct signs and plausible magnitudes.
In this paper, we develop a methodology for estimating marginal emissions of electricity demand that vary by location and time of day across the United States. The approach takes account of the generation mix within interconnected electricity markets and shifting load profiles throughout the day. Using data available for 2007 through 2009, with a focus on carbon dioxide (CO2), we find substantial variation among locations and times of day. Marginal emission rates are more than three times as large in the upper Midwest compared to the western United States, and within regions, rates for some hours of the day are more than twice those for others. We apply our results to an evaluation of plug-in electric vehicles (PEVs). The CO2 emissions per mile from driving PEVs are less than those from driving a hybrid car in the western United States and Texas. In the upper Midwest, however, charging during the recommended hours at night implies that PEVs generate more emissions per mile than the average car currently on the road. Underlying many of our results is a fundamental tension between electricity load management and environmental goals: the hours when electricity is the least expensive to produce tend to be the hours with the greatest emissions. In addition to PEVs, we show how our estimates are useful for evaluating the heterogeneous effects of other policies and initiatives, such as distributed solar, energy efficiency, and real-time pricing.
The Transportation Energy Data Book: Edition 30 is a statistical compendium prepared and published by Oak Ridge National Laboratory (ORNL) under contract with the U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy, Vehicle Technologies Program. Designed for use as a desk-top reference, the Data Book represents an assembly and display of statistics and information that characterize transportation activity, and presents data on other factors that influence transportation energy use. The purpose of this document is to present relevant statistical data in the form of tables and graphs. The latest edition of the Data Book is available to a larger audience via the Internet (cta.ornl.gov/data). This edition of the Data Book has 12 chapters which focus on various aspects of the transportation industry. Chapter 1 focuses on petroleum; Chapter 2 energy; Chapter 3 highway vehicles; Chapter 4 light vehicles; Chapter 5 heavy vehicles; Chapter 6 alternative fuel vehicles; Chapter 7 fleet vehicles; Chapter 8 household vehicles; Chapter 9 nonhighway modes; Chapter 10 transportation and the economy; Chapter 11 greenhouse gas emissions; and Chapter 12 criteria pollutant emissions. The sources used represent the latest available data. There are also three appendices which include detailed source information for some tables, measures of conversion, and the definition of Census divisions and regions. A glossary of terms and a title index are also included for the reader s convenience.
This study investigates how the introduction of electric vehicles may influence the usage of existing cars. A survey of 250 households in South Korea, Korea is used to analyze a future automobile market that includes electric vehicles taking into account the heterogeneity of consumer preferences and usage patterns. Based on consumer preferences, the future market share of various vehicles is estimated and the impact of promoting the usage of electric vehicles by government subsidization and tax incentives is analyzed.
Previous conjoint choice design construction procedures have produced a single homogeneous design that is administered to all study participants. In contrast, this article proposes to construct a limited set of different designs. The principle of heterogeneous designs is applicable to a variety of types of models. This article illustrates this principle for Bayesian designs, taking into account prior uncertainty about the parameter values, and for mixed logit designs that accommodate respondent heterogeneity. The authors develop and investigate a computational procedure that enables quick and easy implementation. Although the number of different designs in the optimal set is small, the authors use a Monte Carlo study to demonstrate that their heterogeneous design achieves substantial gains in efficiency compared with homogeneous designs.
Plug-in hybrid electric vehicles (PHEVs) are being developed for mass production by the automotive industry. PHEVs have been touted for their potential to reduce the US transportation sector's dependence on petroleum and cut greenhouse gas (GHG) emissions by (1) using off-peak excess electric generation capacity and (2) increasing vehicles energy efficiency. A well-to-wheels (WTW) analysis - which examines energy use and emissions from primary energy source through vehicle operation - can help researchers better understand the impact of the upstream mix of electricity generation technologies for PHEV recharging, as well as the powertrain technology and fuel sources for PHEVs. For the WTW analysis, Argonne National Laboratory researchers used the Greenhouse gases, Regulated Emissions, and Energy use in Transportation (GREET) model developed by Argonne to compare the WTW energy use and GHG emissions associated with various transportation technologies to those associated with PHEVs. Argonne researchers estimated the fuel economy and electricity use of PHEVs and alternative fuel/vehicle systems by using the Powertrain System Analysis Toolkit (PSAT) model. They examined two PHEV designs: the power-split configuration and the series configuration. The first is a parallel hybrid configuration in which the engine and the electric motor are connected to a single mechanical transmission that incorporates a power-split device that allows for parallel power paths - mechanical and electrical - from the engine to the wheels, allowing the engine and the electric motor to share the power during acceleration. In the second configuration, the engine powers a generator, which charges a battery that is used by the electric motor to propel the vehicle; thus, the engine never directly powers the vehicle's transmission. The power-split configuration was adopted for PHEVs with a 10- and 20-mile electric range because they require frequent use of the engine for acceleration and to provide energy when the battery is depleted, while the series configuration was adopted for PHEVs with a 30- and 40-mile electric range because they rely mostly on electrical power for propulsion. Argonne researchers calculated the equivalent on-road (real-world) fuel economy on the basis of U.S. Environmental Protection Agency miles per gallon (mpg)-based formulas. The reduction in fuel economy attributable to the on-road adjustment formula was capped at 30% for advanced vehicle systems (e.g., PHEVs, fuel cell vehicles [FCVs], hybrid electric vehicles [HEVs], and battery-powered electric vehicles [BEVs]). Simulations for calendar year 2020 with model year 2015 mid-size vehicles were chosen for this analysis to address the implications of PHEVs within a reasonable timeframe after their likely introduction over the next few years. For the WTW analysis, Argonne assumed a PHEV market penetration of 10% by 2020 in order to examine the impact of significant PHEV loading on the utility power sector. Technological improvement with medium uncertainty for each vehicle was also assumed for the analysis. Argonne employed detailed dispatch models to simulate the electric power systems in four major regions of the US: the New England Independent System Operator, the New York Independent System Operator, the State of Illinois, and the Western Electric Coordinating Council. Argonne also evaluated the US average generation mix and renewable generation of electricity for PHEV and BEV recharging scenarios to show the effects of these generation mixes on PHEV WTW results. Argonne's GREET model was designed to examine the WTW energy use and GHG emissions for PHEVs and BEVs, as well as FCVs, regular HEVs, and conventional gasoline internal combustion engine vehicles (ICEVs). WTW results are reported for charge-depleting (CD) operation of PHEVs under different recharging scenarios. The combined WTW results of CD and charge-sustaining (CS) PHEV operations (using the utility factor method) were also examined and reported. According to the utility factor method, the share of vehicle miles traveled during CD operation is 25% for PHEV10 and 51% for PHEV40. Argonne's WTW analysis of PHEVs revealed that the following factors significantly impact the energy use and GHG emissions results for PHEVs and BEVs compared with baseline gasoline vehicle technologies: (1) the regional electricity generation mix for battery recharging and (2) the adjustment of fuel economy and electricity consumption to reflect real-world driving conditions. Although the analysis predicted the marginal electricity generation mixes for major regions in the United States, these mixes should be evaluated as possible scenarios for recharging PHEVs because significant uncertainties are associated with the assumed market penetration for these vehicles. Thus, the reported WTW results for PHEVs should be directly correlated with the underlying generation mix, rather than with the region linked to that mix.
Stated choice surveys are used extensively in the study of choice behaviour across many different areas of research, notably in transport. One of their main characteristics in comparison with most types of revealed preference (RP) surveys is the ability to capture behaviour by the same respondent under varying choice scenarios. While this ability to capture multiple choices is generally seen as an advantage, there is a certain amount of unease about survey length. The precise definition about what constitutes a large number of choice tasks however varies across disciplines, and it is not uncommon to see surveys with up to twenty tasks per respondent in some areas. The argument against this practice has always been one of reducing respondent engagement, which could be interpreted as a result of fatigue or boredom, with frequent reference to the findings of Bradley & Daly (1994) who showed a significant drop in utility scale, i.e. an increase in error, as a respondent moved from one choice experiment to the next, an effect they related to respondent fatigue. While the work by Bradley & Daly has become a standard reference in this context, it should be recognised that not only was the fatigue part of the work based on a single dataset, but the state-of-the-art and the state-of-practice in stated choice survey design and implementation has moved on significantly since their study. In this paper, we review other literature and present a more comprehensive study investigating evidence of respondent fatigue across a larger number of different surveys. Using a comprehensive testing framework employing both Logit and mixed Logit structures, we provide strong evidence that the concerns about fatigue in the literature are possibly overstated, with no clear decreasing trend in scale across choice tasks in any of our studies. For the data sets tested, we find that accommodating any scale heterogeneity has little or no impact on substantive model results, that the role of constants generally decreases as the survey progresses, and that there is evidence of significant attribute level (as opposed to scale) heterogeneity across choice tasks.
This paper empirically examines the determinants of the demand for alternative energy sources and propulsion technologies in vehicles. The data stem from a stated preference discrete choice experiment with 598 potential car buyers. In order to simulate a realistic automobile purchase situation, seven alternatives were incorporated in each of the six choice sets, i.e. hybrid, gas, biofuel, hydrogen, and electric as well as the common fuels gasoline and diesel. The vehicle types were additionally characterized by a set of attributes, such as purchase price or motor power. Besides these vehicle attributes, our study particularly considers a multitude of individual characteristics, such as socio-demographic and vehicle purchase variables. The econometric analysis with multinomial probit models identifies some population groups with a higher propensity for alternative energy sources or propulsion technologies in vehicles, which can be focused by policy and automobile firms. For example, younger people and people who usually purchase environment-friendly products have a higher stated preference to purchase biofuel, hydrogen, and electric automobiles than other population groups. Methodologically, our study highlights the importance of the inclusion of taste persistence across the choice sets. Furthermore, it suggests a high number of random draws in the Geweke-Hajivassiliou-Keane simulator, which is incorporated in the simulated maximum likelihood estimation and the simulated testing of statistical hypotheses.
This paper examines the effects of the Corporate Average Fuel Efficiency standards (CAFE) on the automobile product mix, prices and fuel consumption First a discrete choice model of automobile demand and a continuous model of vehicle use are estimated using micro data from the Consumer Expenditure Survey for 1984-1990. Next, the demand side model is combined with a model of oligopoly and product differentiation on the supply side. After estimating the demand and supply parameters, the effects of the CAFE regulation are assessed through simulations and compared to the effects of alternative policy instruments such as a powerful gas guzzler tax and an increase in the gasoline tax. Our results are as follows: Vehicle use is in the short run unresponsive to fuel cost changes; vehicle purchases, however, respond to both car prices and fuel cost. These results taken together imply that (1) contrary to the CAFE opponents' claims, higher fleet fuel efficiency is not neutralized by increased driving, and (2) policies to reduce fuel consumption by shifting the composition of the car fleet towards more fuel efficient vehicles are more promising than policies that target utilization. Policies with compositional effects operate through two channels: changes in vehicle prices and in operating costs. Contrary to environmental groups' claims, our results do not indicate the existence of consumer myopia. Still, we find the gasoline tax increase necessary to achieve fuel consumption reductions equivalent to the ones currently achieved through CAFE is 780%; whether an increase of this size is politically feasible is questionable. Our results indicate that the CAFE regulation reduced fuel consumption but shifts in the classification of products as domestic vs. imports weakened the effectiveness of the standards.
We develop a consumer-level model of vehicle choice to shed light on the erosion of the U.S. automobile manufacturers' market share during the past decade. We examine the influence of vehicle attributes, brand loyalty, product line characteristics, and dealerships. We find that nearly all of the loss in market share for U.S. manufacturers can be explained by changes in basic vehicle attributes, namely: price, size, power, operating cost, transmission type, reliability, and body type. U.S. manufacturers have improved their vehicles' attributes but not as much as Japanese and European manufacturers have improved the attributes of their vehicles.
There is growing interest in reducing emissions from electricity generation in the United States (U.S.). Renewable energy, energy efficiency, and energy conservation are all commonly suggested solutions. Both supply- and demand-side interventions will displace energy-and emissions-from conventional generators. Marginal emissions factors (MEFs) give a consistent metric for assessing the avoided emissions resulting from such interventions. This paper presents the first systematic calculation of MEFs for the U.S. electricity system. Using regressions of hourly generation and emissions data from 2006 through 2011, we estimate regional MEFs for CO(2), NO(x), and SO(2), as well as the share of marginal generation from coal-, gas-, and oil-fired generators. Trends in MEFs with respect to system load, time of day, and month are explored. We compare marginal and average emissions factors (AEFs), finding that AEFs may grossly misestimate the avoided emissions resulting from an intervention. We find significant regional differences in the emissions benefits of avoiding one megawatt-hour of electricity: compared to the West, an equivalent energy efficiency measure in the Midwest is expected to avoid roughly 70% more CO(2), 12 times more SO(2), and 3 times more NO(x) emissions.
In this paper, we develop a general random utility framework for analyzing data on individuals’ rank-orderings. Specifically, we show that in the case with three alternatives one can express the probability of a particular rank-ordering as a simple function of first choice probabilities. This framework is applied to specify and estimate models of household demand for conventional gasoline cars and alternative fuel vehicles in Shanghai based on rank-ordered data obtained from a stated preference survey. Subsequently, the framework is extended to allow for random effects in the utility specification to allow for intrapersonal correlation in tastes across stated preference questions. The preferred model is then used to calculate demand probabilities and elasticities and the distribution of willingness-to-pay for alternative fuel vehicles.
Using coal to produce transportation fuels could improve the energy security of the United States by replacing some of the demand for imported petroleum. Because of concerns regarding climate change and the high greenhouse gas (GHG) emissions associated with conventional coal use, policies to encourage pathways that utilize coal for transportation should seek to reduce GHGs compared to petroleum fuels. This paper compares the GHG emissions of coal-to-liquid (CTL) fuels to the emissions of plug-in hybrid electric vehicles (PHEV) powered with coal-based electricity, and to the emissions of a fuel cell vehicle (FCV) that uses coal-based hydrogen. A life cycle approach is used to account for fuel cycle and use-phase emissions, as well as vehicle cycle and battery manufacturing emissions. This analysis allows policymakers to better identify benefits or disadvantages of an energy future that includes coal as a transportation fuel. We find that PHEVs could reduce vehicle life cycle GHG emissions by up to about one-half when coal with carbon capture and sequestration is used to generate the electricity used by the vehicles. On the other hand, CTL fuels and coal-based hydrogen would likely lead to significantly increased emissions compared to PHEVs and conventional vehicles using petroleum-based fuels.
We compare multinomial logit and mixed logit models for data on California households' revealed and stated preferences for automobiles. The stated preference (SP) data elicited households' preferences among gasoline, electric, methanol, and compressed natural gas vehicles with various attributes. The mixed logit models provide improved fits over logit that are highly significant, and show large heterogeneity in respondents' preferences for alternative-fuel vehicles. The effects of including this heterogeneity are demonstrated in forecasting exercises. The alternative-fuel vehicle models presented here also highlight the advantages of merging SP and revealed preference (RP) data. RP data appear to be critical for obtaining realistic body-type choice and scaling information, but they are plagued by multicollinearity and difficulties with measuring vehicle attributes. SP data are critical for obtaining information about attributes not available in the marketplace, but pure SP models with these data give implausible forecasts.
We describe and apply choice models, including generalizations of logit called "mixed logits, " that do not exhibit the restrictive "independence from irrelevant alternatives" property and can approximate any substitution pattern. The models are estimated on data from a stated-preference survey that elicited customers ’ preferences among gas, electric, methanol, and CNG vehicles with various attributes. ACKNOWLEDGEMENTS: David Bunch and Tom Gollob collected the data and conducted preliminary analyses upon which our analysis relies. We are grateful to them for allowing us to use the data. They are not, of course, responsible for any errors or representations that we make
This paper brings together several research streams and concepts that have been evolving in random utility choice theory: (1) it reviews the literature on stated preference (SP) elicitation methods and introduces the concept of testing data generation process invariance across SP and revealed preference (RP) choice data sources; (2) it describes the evolution of discrete choice models within the random utility family, where progressively more behavioural realism is being achieved by relaxing strong assumptions on the role of the variance structure (specifically, heteroscedasticity) of the unobserved effects, a topic central to the issue of combining multiple data sources; (3) particular choice model formulations incorporating heteroscedastic effects are presented, discussed and applied to data. The rich insights possible from modelling heteroscedasticity in choice processes are illustrated in the empirical application, highlighting its relevance to issues of data combination and taste heterogeneity.
Fleet demand for alternative-fuel vehicles (‘AFVs’ operating on fuels such as electricity, compressed natural gas, or methanol) is investigated through an analysis of a 1994 survey of 2000 fleet sites in California. This survey gathered information on site characteristics, awareness of mandates and incentives for AFV operation, and AFV purchase intentions. The survey also contained stated preference tasks in which fleet decision makers simulated fleet-replacement purchases by indicating how they would allocate their choices across a ‘selector list’ of hypothetical future vehicles. A discrete choice model was estimated to obtain preference tradeoffs for fuel types and other vehicle attributes. The overall tradeoff between vehicle range and vehicle capital cost in the sample was $80/mile of range, but with some variation by fleet sector. The availability (density) of off-site alternative fuel stations was important to fleet operators, indicating that fleets are willing to trade off more fuel infrastructure for changes in other attributes, e.g. increased capital or operating costs, or more limited vehicle range. Public fleets (local and county government) were the most sensitive to the capital cost of new vehicles. Along with schools, they are the only fleet sector where reduced tailpipe emission levels are a significant predictor of vehicle choice. Fleet operators in the private sector base their vehicle selection less on environmental concerns than on practical operational needs.
According to intuition and theories of diffusion, consumer preferences develop along with technological change. However, most economic models designed for policy simulation unrealistically assume static preferences. To improve the behavioral realism of an energy–economy policy model, this study investigates the “neighbor effect,” where a new technology becomes more desirable as its adoption becomes more widespread in the market. We measure this effect as a change in aggregated willingness to pay under different levels of technology penetration. Focusing on hybrid-electric vehicles (HEVs), an online survey experiment collected stated preference (SP) data from 535 Canadian and 408 Californian vehicle owners under different hypothetical market conditions.Revealed preference (RP) data was collected from the same respondents by eliciting the year, make and model of recent vehicle purchases from regions with different degrees of HEV popularity: Canada with 0.17% new market share, and California with 3.0% new market share. We compare choice models estimated from RP data only with three joint SP–RP estimation techniques, each assigning a different weight to the influence of SP and RP data in coefficient estimates. Statistically, models allowing more RP influence outperform SP influenced models. However, results suggest that because the RP data in this study is afflicted by multicollinearity, techniques that allow more SP influence in the beta estimates while maintaining RP data for calibrating vehicle class constraints produce more realistic estimates of willingness to pay. Furthermore, SP influenced coefficient estimates also translate to more realistic behavioral parameters for CIMS, allowing more sensitivity to policy simulations.
This research is designed to help researchers and policy makers ground their work in the reality of how US consumers are thinking and behaving with respect to automotive fuel economy. Our data are from semi-structured interviews with 57 households across nine lifestyle “sectors.” We found no household that analyzed their fuel costs in a systematic way in their automobile or gasoline purchases. Almost none of these households track gasoline costs over time or consider them explicitly in household budgets. These households may know the cost of their last tank of gasoline and the unit price of gasoline on that day, but this accurate information is rapidly forgotten and replaced by typical information. One effect of this lack of knowledge and information is that when consumers buy a vehicle, they do not have the basic building blocks of knowledge assumed by the model of economically rational decision-making, and they make large errors estimating gasoline costs and savings over time.Moreover, we find that consumer value for fuel economy is not only about private cost savings. Fuel economy can be a symbolic value as well, for example among drivers who view resource conservation or thrift as important values to communicate. Consumers also assign non-monetary meaning to fuel prices, for example seeing rising prices as evidence of conspiracy. This research suggests that consumer responses to fuel economy technology and changes in fuel prices are more complex than economic assumptions suggest.The US Department of Energy and the Energy Foundation supported this research. The authors are solely responsible for the content and conclusions presented.
Both federal Corporate Average Fuel Economy (CAFE) regulations and market forces resulting from sharply higher gasoline prices are forcing auto manufacturers to dramatically improve the fuel economy of cars built for sale in the United States. Because these fuel economy improvements are being accompanied by significant changes in other car attributes that are believed to be important determinants of consumer choice among car models, there is currently considerable uncertainty about the likely effects on the automotive market. This paper analyzes market demand for automobile attributes, including those that are likely to be affected by fuel economy improvements, and presents estimates of dollar market valuations of key attributes. Market share effects of various assumed changes in car prices are analyzed in order to gain insight into competition among car models and the sensitivity of new car fleet average fuel economy to possible manufacturer or size-class-specific pricing strategies. Effects of gasoline price changes are also analyzed. These analyses make use of the hedonic demand model (also known as the “random coefficients logit model”), an extension of the multinomial logit probability choice model which explicitly incorporates variations in consumer tastes across the car-buying population. A data base measuring attributes and observed market shares of some 150 car models and submodels for the 1977 and 1978 model years is used.
E-bikes in China are the single largest adoption of alternative fuel vehicles in history, with more than 100 million e-bikes purchased in the past decade and vehicle ownership about 2× larger for e-bikes as for conventional cars; e-car sales, too, are rapidly growing. We compare emissions (CO(2), PM(2.5), NO(X), HC) and environmental health impacts (primary PM(2.5)) from the use of conventional vehicles (CVs) and electric vehicles (EVs) in 34 major cities in China. CO(2) emissions (g km(-1)) vary and are an order of magnitude greater for e-cars (135-274) and CVs (150-180) than for e-bikes (14-27). PM(2.5) emission factors generally are lower for CVs (gasoline or diesel) than comparable EVs. However, intake fraction is often greater for CVs than for EVs because combustion emissions are generally closer to population centers for CVs (tailpipe emissions) than for EVs (power plant emissions). For most cities, the net result is that primary PM(2.5) environmental health impacts per passenger-km are greater for e-cars than for gasoline cars (3.6× on average), lower than for diesel cars (2.5× on average), and equal to diesel buses. In contrast, e-bikes yield lower environmental health impacts per passenger-km than the three CVs investigated: gasoline cars (2×), diesel cars (10×), and diesel buses (5×). Our findings highlight the importance of considering exposures, and especially the proximity of emissions to people, when evaluating environmental health impacts for EVs.
Plug-in hybrid electric vehicles (PHEVs), which use electricity from the grid to power a portion of travel, could play a role in reducing greenhouse gas (GHG) emissions from the transport sector. However, meaningful GHG emissions reductions with PHEVs are conditional on low-carbon electricity sources. We assess life cycle GHG emissions from PHEVs and find that they reduce GHG emissions by 32% compared to conventional vehicles, but have small reductions compared to traditional hybrids. Batteries are an important component of PHEVs, and GHGs associated with lithium-ion battery materials and production account for 2-5% of life cycle emissions from PHEVs. We consider cellulosic ethanol use and various carbon intensities of electricity. The reduced liquid fuel requirements of PHEVs could leverage limited cellulosic ethanol resources. Electricity generation infrastructure is long-lived, and technology decisions within the next decade about electricity supplies in the power sector will affectthe potential for large GHG emissions reductions with PHEVs for several decades.
Hybrid electric vehicles (HEVs) have image, or symbolic benefits, in addition to their functionality. This study examines the images that ten HEV-owning households saw in their vehicles, and the impact symbolic benefits had on these consumers' decisions to purchase HEVs. In general, all of the HEV owners perceived some image in their vehicles, although these images varied in their strength and significance. The majority of households saw their HEVs projecting images that were linked to larger values, including social awareness, responsibility, and concern for others; others connected their HEVs to images of frugality and intelligent consumerism. HEVs served as communication mechanisms in all households, either by projecting their images or by stimulating owner evangelism. In addition, for a handful of participants, the symbolic benefits of their HEVs were significant enough to justify substantial functional compromises.
President Obama signed the 400 through their paychecks over the course of the year, this will encourage spending, since it will be a smaller amount returned each week. The Alternative Minimum Tax will be "patched." Some post-secondary education plans will be implemented and Hope Credits will be expanded. A first time home buyer credit that went into effect in 2007 has been changed, increasing the dollar limit and waiving the payback requirement unless the home is sold within 36 months of purchase.