Adoption Of Alternative Fuel Vehicles Research Articles

Explainable artificial intelligence for decarbonization: Alternative fuel vehicle adoption in disadvantaged communities

This article explores the adoption of alternative fuel vehicles (AFVs), leading to decarbonization, in disadvantaged communities (DACs) by applying statistical and explainable artificial intelligence (XAI) techniques to understand the factors associated with AFV adoption in these communities. The study harnesses a unique and comprehensive database of surveys and public databases for the Puget Sound region in the United States. The XAI techniques, specifically the Extreme Gradient Boosting algorithm with Shapely Additive Explanations, provide interpretable and understandable explanations of factors associated with AFV adoption in DACs. The study findings provide an understanding of the social and economic factors and challenges of DACs. The results suggest several key factors, especially a lack of access to charging infrastructure, consumer attitudes, and income, play a substantial role in adopting AFVs. As expected, AFV adoption in DACs (12.96%) is lower than non-DACs (15.30%). More public charging stations strongly correlate with AFV adoption in DACs. Tech-oriented households in DACs are more likely to adopt AFVs compared with non-DACs. The findings also point to the significant effects of home charging facilities while adopting AFVs in DACs. The XAI results emphasize the importance of socio-economic factors in AFV adoption programs and provide insights into decision-making in DACs. This research contributes to the literature on AFV adoption and suggests opportunities for improvements in DACs transitioning to AFVs. The study findings can be used to assess the planning-level impacts of refueling or charging infrastructure in DACs while enabling DACs to benefit from infrastructure investments.

Impact of energy production mix on alternative fuel vehicle adoption in Korea

This study examines the environmental impact of alternative fuel vehicle (AFV) based on consumers’ vehicle adoption behavior according to the energy production mix (how fuel for each vehicle technology is produced) of battery electric vehicle (BEV) and hydrogen fuel cell vehicle (HFCV). The discrete choice experiment was utilized to examine the consumers’ vehicle adoption behavior, and our empirical results indicate that consumers prefer BEV with a renewable-oriented power mix the most, while they have the least preference for HFCV with a grey hydrogen-oriented production mix. Compared to the current market share of AFV, an annual market share of BEV and HFCV will increase by about 250% and 194%, respectively, when energy production mixes for AFV become cleaner by 2030. Moreover, making the AFV’s energy production mix cleaner can reduce about 130,000 tons of greenhouse gas from annual sales of vehicles than maintaining the current energy production mix in 2030.

Challenges to Alternative Fuel Vehicle Usage in Trinidad

The Government of the Republic of Trinidad and Tobago has set itself some lofty targets in terms of reducing CO2 emissions from public transportation by 2030. Several initiatives have been launched since 2015 in order to fulfill these targets. One key strategy is the adoption of a higher percentage of alternative fuel vehicles (AFVs) into the overall fleet of vehicles. This article reviews the history and implementation of the various AFV oriented policies and examines the effectiveness of these initiatives. The authors describe the current state of the motorised vehicle fleet in terms of fuel type and highlight where adoption levels have fallen short of the target. The percentage of AVFs per public transit mode ranges from about 2 to 20 percent, where the higher percent represents the CNG buses belonging to the Public Transport Service Corporation (PTSC). In Trinidad, the natural gas supply is already well-established, refuelling stations are in operation and up to 20 original equipment manufacturer CNG vehicles are available on the market, supplemented by the option of vehicle conversion. The opposite is true for electric vehicles. The article further identifies the four key issues affecting the improved adoption of AFVs namely: (i) Fuel Prices (ii) Availability of Alternative Fuels, (iii) Consumer Perception and Resistance, and (iv) Lack of Institutional Support. In addition to presenting the proposals by others for improvements to the local transportation system, the authors recommend six key actions that should be taken to achieve the targets set for 2021 and beyond.

Spatial and unobserved heterogeneity in consumer preferences for adoption of electric and hybrid vehicles: A Bayesian hierarchical modeling approach

The transition to low carbon vehicles known as alternative fuel vehicles (AFVs) is well underway. This transition has been motivated partly by consumer demand and partly by legislation such as the Zero Emission Vehicle mandate, which requires manufacturers to sell a certain percentage of their vehicles as AFVs. While the long-term adoption of AFVs (specifically, electric and hybrids) may take several years, there is a need to understand consumer preferences for AFV adoption and the pathways of AFV adoption from a national perspective. Therefore, this study sought to provide information about consumer preferences regarding AFV ownership while considering spatial and unobserved heterogeneity in consumer preferences, which can potentially impact societal transition to low carbon fueled vehicles. The 2017 National Household Travel Survey was used to calibrate Bayesian logit and hierarchical models. The findings of these models reveal that higher gasoline prices contribute toward the adoption of battery electric vehicles. The results also reveal that the perceived disadvantages of AFVs for long commutes are the key barrier in wider adoption of AFVs. Interestingly, frequent use of the internet by consumers revealed a higher likelihood for purchase of hybrid vehicles. Furthermore, West Coast residents are observed to be a large portion of the early adopters and are more likely to purchase hybrids as compared to battery electric vehicles. The knowledge generated by this study has implications for making better informed decisions about AFV adoption and developing incentives to promote wider adoption of AFVs by overcoming their perceived disadvantages.

Adoption of alternative fuel vehicle fleets – A theoretical framework of barriers and enablers

This paper synthesises and analyses factors affecting firms’ adoption of Alternative Fuel Vehicles (AFVs). Our theoretical framework on Firm Adoption of Sustainable Technologies (FAST) integrates several technology adoption models with the attribute of ‘perceived risk’. On the basis of the FAST theoretical framework, we review and analyse 53 studies on the adoption of AFV fleets. We find that the primary enablers for firms to adopt are linked to efficiency, environmental, economic, and strategic gains. The primary barriers to adoption are limited knowledge, organisational policies, as well as operational and economic factors. Overall, the AFV fleet adoption literature suggests that large firms are more likely to become early adopters, which is largely due to their willingness to accept financial risks in exchange for first mover advantages. We suggest further research avenues particularly on policy and emotions and on the linkages between these two factors.

The use of automotive fleets to support the diffusion of Alternative Fuel Vehicles: A Rapid Evidence Assessment of barriers and decision mechanisms

Automotive fleets in public and private organisations have been identified as offering niche market conditions to support the diffusion of nascent Alternative Fuel Vehicle (AFV) technologies despite technological lock-in. This paper aims to uncover the effectiveness of fleets as early adopters for AFVs by investigating the outcomes of AFV implementations, the barriers hindering AFV adoption, the decision-making mechanisms and the factors contributing to a successful implementation. The evidences from the range of ex-ante and ex-post assessments reviewed point to: i) Adverse operational impacts on fleets due to technical limitations, resulting in degraded service provision; ii) Implementation barriers primarily linked to the refuelling infrastructure scarcity and the high acquisition cost, although lack of information and changing political environment also have a significant influence; iii) Adoption motivations directly related to the types of organisation, with public fleets mostly governed by political mandates, while private fleets adopt AFVs to enhance brand image; iv) Diverse decision-making processes involving numerous stakeholders where vehicle selection may be outside the remit of fleet managers. Consensual findings are that large entities are better positioned to adopt AFVs and that the focus on user experience and particularly on ease of operation is pivotal to ensure the acceptance of the technology during AFV implementation.

Alternative-fuel-vehicle policy interactions increase U.S. greenhouse gas emissions

The transportation sector is currently the largest contributor of greenhouse gas (GHG) emissions in the United States, and light-duty vehicles produce the majority of transportation emissions. Federal standards for fleet-averaged vehicle GHG emission rates and their corresponding corporate average fuel economy standards cap GHG emissions of the US light-duty vehicle fleet. In addition, two key policies aim to encourage a future fleet transition to alternative fuel vehicle (AFV) technologies: (1) incentives that treat AFVs favorably in the federal GHG standard, and (2) state zero-emissions vehicle (ZEV) policy, which mandates AFV sales in some states. While each of these AFV policies can encourage AFV adoption, we show that net GHG emissions increase when both policies are present simultaneously. Specifically, we estimate changes in life cycle GHG emissions and gasoline consumption, relative to a pure federal fleet GHG standard (without AFV incentives or mandates), resulting from the introduction of (1) AFV incentives in federal fleet GHG policy, (2) state ZEV mandates, and (3) the combination of the two. We find that under fairly general conditions the combined AFV policies produce higher GHG emissions than either policy alone. This result is a consequence of state mandates increasing AFV sales in the presence of federal incentives that relax the fleet GHG standard when AFVs are sold. Using AFV sales projections from the Energy Information Administration and the California Air Resources Board, we estimate that the combined policies produce an increase on the order of 100 million tons of CO2 emissions cumulatively for new passenger cars sold from 2012 through 2025 relative to a pure GHG standard. AFV incentives in the GHG standard conflate policy goals by encouraging AFV adoption at the cost of higher fleet GHG emissions, and they permit even higher fleet GHG emissions when other policies, such as the ZEV mandate, increase AFV adoption.

Analysis of Alternative Fuel Vehicle (AFV) Adoption Utilizing Different Machine Learning Methods: A Case Study of 2017 NHTS

Alternative fuel vehicles (AFVs) are considered as the one of policies towards the sustainable transportation with low fossil fuel consumption and greenhouse gas emission. However, the demand for AFV is still not clear, due to the complex interaction between influencing factors. This paper aimed to investigate the AFV adoption behavior in order to estimate the AFV penetration in specific regions. 2017 National Household Travel Survey (NHTS), providing the detailed information of AFV users, was utilized to conduct the factor analysis and establish the prediction model. Three groups of variables, including vehicle-related, household-related and individual-related variables, were considered. Additionally, the AFV users are only 4.2% of all the respondents, which is a typical imbalanced distribution. Thus, this paper also introduced the methods to deal with the imbalanced dataset for AFV users and conventional vehicle (CV) users, which was rarely investigated in existing studies. To construct the prediction model, five machine learning methods, Logistic Regression (LR), Naive Bayes (NB), Random Forest (RF), Support Vector Machine (SVM) and Decision Tree (DT), were employed and compared. The performance analysis indicates that the RF model has the best prediction capability among the models. For validation, the RF prediction model was used to develop a AFV penetration map for U.S. by state. This study is believed to have a wide application in government policy making and vehicle manufacturing.

How do companies decide? Emotional triggers and drivers of investment in natural gas and biogas vehicles

Management research recognizes that companies’ investment decisions are driven not only by objective techno-economic models but also by emotion. Alternative fuel vehicle (AFV) investments offer an appealing context in which to study this interaction in energy source decisions, as they are influenced by a diverse set of analytical and emotional elements ranging from fuel costs to environmental values. This study examines companies’ vehicle investment decision making in Finland and focuses on the interaction between objective facts and subjective feelings along with the process of choosing the vehicles’ energy source.This article is based on an interventionist case study. Switching path analysis technique (SPAT) was used to investigate companies’ switches from conventional fuels to natural gas or biogas. This article contributes to the discussion of AFV adoption by unveiling companies’ decision-making triggers, investment criteria and the use of information sources in the decision-making processes. The findings regarding the decision-making dynamics between these factors are transferable to other energy investments. However, the content of the triggers and drivers varies across contexts and may change over time, because vehicles as investment objects have different meanings for different organizational actors depending on their organizational role, investment use context, and personal preferences.

Alternative-Fuel Vehicle Adoption in Service Fleets: Impact Evaluation Through Optimization Modeling

This paper introduces a new rich vehicle routing problem faced by companies that consider alternative-fuel vehicle (AFV) adoption into a service fleet consisting of gasoline or diesel vehicles. The service operation addressed here differs from delivery operations in that a vehicle has to stop for extended periods of time while its driver serves customers. We discuss measuring the impact of AFV adoption on fleet operations from multiple perspectives and formulate four objective functions to represent the defined performance metrics in a generalized mixed-integer linear programming model. The model can accommodate various AFV types with respect to driving range, refueling time, and availability of refueling stations. We develop a variable neighborhood search heuristic to solve large-scale problems efficiently. Results from the research show that the classical vehicle routing objective of minimizing total vehicle miles traveled does not work well in this emerging problem; instead, an objective such as minimizing carbon emissions or fuel costs provides more desirable solutions. The results also show that in service fleets, refueling time has lesser impact on fleet performance compared to service station availability or vehicle range. From a managerial standpoint, this indicates that investment in range extension or establishing service stations is more important than investment in faster refueling capability.

Multi-criteria alternative-fuel technology selection using interval-valued intuitionistic fuzzy sets

Fleet operators periodically update and upgrade their vehicles in order to take opportunity of new technologies. Alternative-fuel vehicles provide opportunities to lower fuel costs as well as to reduce environmental footprint. Decision on alternative-fuel vehicle adoption is a challenging problem for fleet operators in today’s technological and market development level. This paper focuses on the alternative-fuel technology selection problem of a utility company in the USA. The paper develops a multi-expert, multi-criteria decision making (MCDM) method based on interval-valued intuitionistic fuzzy sets (IVIFS) with linguistic data, and finds that the best fit for the utility company is an extended-range natural gas vehicle.

Moving beyond alternative fuel hype to decarbonize transportation

In the past three decades, government, industry and other stakeholders have repeatedly been swept up with the ‘fuel du jour’, claiming that a particular alternative fuel vehicle (AFV) technology can succeed in replacing conventional gasoline-powered vehicles. However, AFV technologies have experienced relatively little success, with fossil fuels still accounting for about 95% of global transport energy use. Here, using the US as a case study, we conduct a media analysis to show how society’s attention has skipped among AFV types between 1980 and 2013, including methanol, natural gas, plug-in electric, hybrid electric, hydrogen and biofuels. Although our results provide no indication as to whether hype ultimately has a net positive or negative impact on AFV innovation, we offer several recommendations that governments can follow to move past hype to support significant AFV adoption and displace fossil fuel use in the transportation sector. Alternative fuel technologies are crucial to decarbonize transport, but attention has shifted among options over time. This study presents an analysis of media, innovation and funding data for these different options and recommends actions to help move beyond hype to support technology adoption.

Optimizing layouts of initial AFV refueling stations targeting different drivers, and experiments with agent-based simulations

The number of refuelling stations for AFVs (alternative fuel vehicles) is limited during the early stages of the diffusion of AFVs. Different layouts of these initial stations will result in different degrees of driver concern regarding refueling and will therefore influence individuals’ decisions to adopt AFVs. The question becomes “what is an optimal layout for these initial stations? Should it target all drivers or just a portion of them, and if so, which portion?” Further, how does the number of initial AFV refueling stations influence the adoption of AFVs? This paper explores these questions with agent-based simulations. Using Shanghai as the basis of computational experiments, this paper first generates different optimal layouts using a genetic algorithm to minimize the total concern of different targeted drivers and then conducts agent-based simulations on the diffusion of AFVs with these layouts. The main findings of this study are that (1) targeting drivers in the city center can induce the fastest diffusion of AFVs if AFV technologies are mature and (2) it is possible that a larger number of initial AFV refueling stations may result in slower diffusion of AFVs because these initial stations may not have sufficient customers to survive. The simulations can provide some insights for cities that are trying to promote the diffusion of AFVs.

Innovative alternatives take action – Investigating determinants of alternative fuel vehicle adoption

Alternative fuel vehicles (AFVs) as environmentally friendly alternatives to conventional internal combustion engines have gained increasing attention in general public. While empirical studies have begun to explore product-specific factors that drive consumer adoption of AFVs, an integrative framework of a comprehensive set of AFV adoption factors and its theoretical foundation as well as empirical validation is still missing. By drawing on theory of innovation adoption and theory of reasoned action we show that consumers’ perceptions of AFV attributes lead to a general attitude formation towards AFV. In conjunction with consumers’ subjective and personal norm, this in turn determines AFV adoption behavior. Concerning AFV attributes, compatibility, design, and relative advantage of AFVs exhibit the strongest influence on consumers’ attitude formation toward AFV. We derive implications for future research and policy makers. The latter include suggestions on how to develop and communicate AFV in order to stimulate AFV adoption.

An Agent-Based Training System for Optimizing the Layout of AFVs' Initial Filling Stations

The availability of refuelling locations for alternative fuel vehicles (AFVs) is an important factor that drivers consider before adopting an AFV; thus, the layout of initial filling stations for AFVs will influence the adoption of AFVs. This paper presents a training system for optimising the layout of initial filling stations for AFVs by linking an agent-based model of the adoption of AFVs with a real city/area's road network, as well as the city/area's social and economic background. In the agent-based model, two types of agents (driver agents and station owner agents) interact with each other in a city/area's road network, stored in a GIS (Geographic Information System). With simulation scenario analyses and a genetic algorithm, the training system presented in this paper can help decision makers determine close-to-optimal layouts for initial AFV filling stations. This paper also presents a case study of the application of the training system that analyses the layout of fast-charging or battery-changing stations for the promotion of electric vehicles adoption in Shanghai.

Consumer valuation of changes in driving range: A meta-analysis

We perform a meta-analysis of studies investigating consumer preferences for electric and other alternative fuel vehicles (AFVs) to provide insights into the way driving range is traded off for capital costs. We find that consumers are willing to pay, on average, between 66 and 75 US$ for a 1-mile increase in driving range. Ceteris paribus, 100-mile-range cars have to be priced about 60% less than their conventional counterparts to become competitive. In line with intuition, but in contrast to most specifications employed in primary studies, we find that consumers’ marginal willingness to pay (WTP) decreases at a diminishing rate with increases in driving range. The variation in the WTP and compensating variation estimates among examined studies can be attributed to differences in the levels of driving range considered, in other elements of the study design and in the country of study. Our findings support stated preference literature’s conclusion that short driving range has been a major limitation to the large-scale adoption of battery electric vehicles (BEVs) and other AFVs, and that technological developments permitting longer driving ranges will, to some extent, facilitate their market penetration. We further propose that consumer valuation of driving range should not be examined in isolation from other attributes related to refuelling activities, such as refuelling duration and the coverage of refuelling infrastructure.

A Study of the Diffusion of Alternative Fuel Vehicles: An Agent‐Based Modeling Approach*

This paper demonstrates the use of an agent‐based model (ABM) to investigate factors that can speed the diffusion of eco‐innovations, namely alternative fuel vehicles (AFVs). The ABM provides the opportunity to consider the interdependencies inherent between key participants in the automotive industry: manufacturers, consumers, and governmental agencies. Agent‐based models allow for tackling these interdependencies in a very elegant way. Because ABMs allow convenient modeling of the interactions between multiple agents, each with unique optimization goals, it provides a method for understanding consumer and manufacturer response to diverse environmental changes. Grounding the agent‐based model on empirical data further improves the validity of the model results. We use choice‐based conjoint data of more than 7,000 respondents to elicit heterogeneous consumer preferences. Further, information about manufacturers' cost structure is available from an established vehicle design tool, AVCEM. In three experiments, mechanisms are considered for speeding the adoption of AFVs: technology push, market pull, and regulatory push. Simulation results support the idea that technology push can be an important mechanism for speeding the diffusion of AFVs. Market pull, that is, word of mouth, also has a positive impact on the diffusion of AFVs and increases the social good by decreasing the preference for fuel‐inefficient vehicles. Furthermore, word of mouth leads to a higher willingness to pay for AFVs, which indicates that the perceived value of AFVs increases with word of mouth. In contrast, a governmental push that focuses on the manufacturers (fuel economy mandates) leads to a decrease in the social good (air pollution improvement) because market share for fuel‐inefficient vehicles increases. This article provides insights into how to set up an ABM to evaluate factors that influence the diffusion of alternative fuel vehicles. These insights can be applied to other types of eco‐innovations.

Green consumer behavior: determinants of curtailment and eco‐innovation adoption

PurposeKnowledge of green consumer behavior is important for environmental and business reasons. The purpose of this study is to examine the determinants of green curtailment behaviors and consumer adoption of innovations marketed as green (eco‐innovations), and to analyze factors explaining these two types of green behaviors.Design/methodology/approachThe results from a survey on adopters and non‐adopters (n=1,832) of alternative fuel vehicles (AFVs) are reported. Regression analysis on willingness to curtail car use and willingness to adopt a so‐called environmentally friendly car are used to identify significant determinants across the behavioral categories.FindingsThe results show that values, beliefs, norms, and habit strength determine willingness to curtail and willingness for eco‐innovation adoption. Personal norms have a strong positive influence on willingness for the behaviors and habit strength has a negative influence. The other determinants have varying influence depending on type of behavior.Research limitations/implicationsA limitation of this study concerns the focus on only one eco‐innovation. However, since the adoption of AFVs is a high involvement behavior, the results carry implications for other high involvement products as well.Practical implicationsAttitudinal factors and habits in combination prove to be effective determinants for curtailment behaviors and willingness to adopt eco‐innovations. In addition, previous adoption is found to be a strong determinant of future willingness to adopt.Originality/valueThe contribution of the paper is the two‐sided approach on green consumer behavior and the result that values, beliefs and norms not only predict low involvement post‐purchase behaviors but also adoption of high involvement eco‐innovations.

Identifying Challenges for Sustained Adoption of Alternative Fuel Vehicles and Infrastructure

This paper develops a dynamic, behavioral model with an explicit spatial structure to explore the co-evolutionary dynamics between infrastructure supply and vehicle demand. Vehicles and fueling infrastructure are complementarities and their chicken-egg dynamics are fundamental to the emergence of a self-sustaining alternative fuel vehicle market, but they are not well understood. The paper explores in-depth the dynamics resulting from local demand-supply interactions with strategically locating fuel-station entrants. The dynamics of vehicle and fuel infrastructure are examined under heterogeneous socio-economic/ demographic conditions. The research reveals the formation of urban adoption clusters as an important mechanism for early market formation. However, while locally speeding diffusion, these same micro-mechanisms can obstruct the emergence of a large, self-sustaining market. Other feedbacks that significantly influence dynamics, such as endogenous topping-off behavior, are discussed. This model can be applied to develop targeted entrance strategies for alternative fuels in transportation. The roles of other powerful positive feedbacks arising from scale and scope economies, R&D, learning by doing, driver experience, and word of mouth are discussed.