







































     Business Management Research and Applications: A Cross Disciplinary Journal 

 

 

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Driving Change: Motivations and Barriers to 

Electric Vehicle Adoption 
 

 

Rebecca L. Bergh | University of Washington, Seattle, Washington, USA 

Maya Q. Tang | University of Washington, Seattle, Washington, USA 

Lily Tang | University of Washington, Seattle, Washington, USA 

Jennifer Chen | University of Washington, Seattle, Washington, USA 

Rajeshwari R. Sabadra | University of Washington, Seattle, Washington, USA 

Ryan Rucker, Ed.D. | Columbia Southern University, Orange Beach, Alabama, USA 

 

Contact: Dr. Ryan Rucker, 803-730-6714, ryan.rucker@columbiasouthern.edu 

 

Abstract 

 

Washington state aims to have 100% of passenger vehicles be zero-emission vehicles (ZEVs) on 

the road by 2035, yet consumer adoption remains slow, and public confidence in this goal is low. 

This research explored how residents make vehicle-purchase decisions, their sentiment toward 

EVs, and the adequacy of current EV infrastructure. A statewide survey revealed weak 

correlations between individual demographics and the likelihood of EV adoption, while 

highlighting key barriers, including high upfront costs, limited charging infrastructure, and 

concerns about total ownership costs and environmental impacts. Existing infrastructure data 

indicates significant expansion is required to support projected EV growth. Based on quantitative 

and qualitative findings, three recommendation areas emerged: policy measures to incentivize 

adoption or reassess the ZEV timeline; environmental strategies focused on battery recycling and 

grid modernization; and technological improvements in charging access, vehicle capabilities, and 

infrastructure resilience. These efforts must be implemented in parallel to meaningfully 

accelerate EV adoption and move toward Washington’s 2035 ZEV target. 

 

Keywords: Electric vehicle, consumer behavior, infrastructure, policy, technology 

  



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Introduction/Background 
 

In 10 years, all 2.83 million passenger vehicles driving on Washington roads are expected 

to be powered purely by electricity — if the state’s top leadership achieves its goal of 100% 

ZEVs by 2035. However, the state has a long way to go. Only about 2.8% of all passenger 

vehicles on the road in Washington fit into this category (Data.gov, 2024). Additionally, these 

battery-electric vehicles (EVs) accounted for only 10.8% of all new passenger vehicle 

registrations in the state in 2024 (Data.gov, 2024). To meet its goal, the state must accelerate 

ZEV adoption among drivers by providing robust charging infrastructure and other incentives. 

Understanding how consumers make these big purchases is a good starting point for identifying 

possible pathways to the 2035 goal.  

 

Literature Review 
 

Research on electric vehicle adoption has highlighted multiple factors influencing 

consumer behavior, including cost, range anxiety, incentives, and infrastructure availability. 

Prior studies emphasize that while financial incentives and environmental awareness encourage 

adoption, gaps in charging infrastructure and policy inconsistencies remain significant barriers. 

Multiple studies have identified cost, range anxiety, and charging accessibility as the primary 

determinants of EV adoption. Liao et al. (2017) found that while environmental benefits play a 

role in purchase decisions, total cost of ownership, charging convenience, and perceived 

technological risks remain the most influential factors. Their research emphasized that financial 

incentives, such as tax rebates, significantly increase EV adoption rates only when coupled with 

strong charging infrastructure development. 

Similarly, Rezvani et al. (2014) analyzed psychological and social influences on EV 

adoption, noting that while environmental consciousness and social influence contribute 

positively, concerns about battery life, resale value, and charging reliability hinder widespread 

adoption. Their findings suggest that consumer education and policy addressing these concerns 

could improve adoption rates. This aligns with recent Washington state efforts to enhance EV 

incentives and awareness campaigns. A 2021 report from the International Council on Clean 

Transportation (ICCT) further supports these findings, stating that while EV purchase intentions 

have increased globally, adoption remains heavily dependent on external incentives and 

infrastructure availability. The report suggests that states with aggressive incentive programs and 

high charger density achieve significantly higher adoption rates than those with weaker policies. 

One of the most frequently cited barriers to EV adoption is the availability of charging 

stations. Studies indicate consumers are reluctant to transition to EVs if charging is inconvenient, 

unreliable, or significantly slower than refueling a gasoline-powered vehicle. Sierzchula et al. 

(2014) found that while financial incentives positively correlate with EV adoption rates, their 

effectiveness diminishes in regions with low charging station availability. This highlights the 

importance of infrastructure expansion as a parallel strategy to financial incentives. Washington 

state faces regional disparities in charging station distribution. Reports from the Washington 

State Department of Transportation (2021) indicate urban centers like Seattle have relatively 

strong charger networks, but rural areas remain underserved.  

A study by Nicholas and Hall (2022) on charging infrastructure gaps emphasized that 

policy-driven investments in charger deployment significantly impact EV market penetration. 



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Their findings suggest that states with comprehensive charging infrastructure strategies achieve 

higher adoption rates than those relying solely on purchase incentives. Studies have 

demonstrated that government incentives play a crucial role in shaping consumer decisions, but 

the structure and accessibility of those incentives matter. Hardman (2019) analyzed recurring vs. 

one-time incentives, concluding that long-term financial benefits, such as reduced electricity 

rates, access to carpool lanes, and lower maintenance costs, often drive higher adoption rates 

than one-time purchase rebates.  

Additionally, Diaz and Clark (2023) explored the effectiveness of EV rebate programs. 

They found that while upfront financial incentives boost sales, their long-term impact is limited 

when consumers face high operational costs or limited charging access. Their findings suggest 

that aligning incentives with infrastructure expansion is essential for sustained adoption. A 2022 

study by the U.S. Department of Energy (DOE) examined EV adoption trends across states. It 

concluded that states with higher levels of public-private partnerships in EV infrastructure 

development experienced faster adoption rates than those relying solely on government-funded 

programs. This highlights the need for Washington to engage private-sector stakeholders in 

expanding charging networks. 

The findings from these studies reinforce the importance of a dual approach that 

enhances incentives while addressing infrastructure gaps. This research builds on these insights 

by: 

1. Assessing Washington residents' attitudes toward EVs and identifying the key 

motivations behind their purchasing decisions. 

2. Examining regional disparities in charging access and determining the prevalence of 

"EV deserts.” 

3. Evaluating the effectiveness of current incentive programs in meeting the state’s 2035 

ZEV goal. 

By comparing these findings with existing literature, this research aims to validate whether 

Washington state aligns with broader EV adoption trends or faces unique barriers that require 

specialized policy interventions. These insights will help policymakers refine existing strategies, 

improve infrastructure planning, and design more effective consumer engagement programs to 

accelerate EV adoption. 

 

Methods 
 

The problem this study addresses is that Washington state’s goal of achieving 100% zero-

emission vehicle adoption by 2035 is challenged by slow consumer adoption, insufficient 

charging infrastructure, and uneven regional access. Fast charging still lags gasoline refueling in 

both speed and availability, especially in metropolitan areas where long wait times and limited 

power grid capacity create additional barriers. These infrastructure gaps, coupled with consumer 

concerns, inhibit EV adoption and jeopardize the state’s ability to meet its transportation 

decarbonization milestones. 

If these challenges persist, the consequences could be significant. Environmentally, 

continued reliance on internal combustion engine vehicles will contribute to greenhouse gas 

emissions and air pollution. Economically, slow ZEV adoption may hinder growth in 

Washington’s clean energy and transportation sectors, limiting job creation and innovation. 

Socially, underserved areas — particularly rural and low-income communities — may face 

ongoing exclusion from sustainable transportation options, reinforcing existing inequities. These 



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setbacks risk compromising public health and undermining the state’s leadership in climate and 

sustainability policy. 

While prior studies have identified broad factors affecting EV adoption, such as cost, 

range anxiety, and environmental attitudes, few have examined Washington-specific barriers, 

such as regional charging disparities and infrastructure limitations. This study contributes to the 

field by integrating consumer survey data and vehicle registration trends to identify obstacles and 

forecast infrastructure needs. The findings provide actionable insights for policymakers and 

planners working to align EV adoption strategies with Washington’s 2035 ZEV goals, while also 

informing broader efforts to support equitable and effective EV transitions. 

 

Research Questions 

 

• What individual characteristics and external factors contribute most to consumer 

behavior in the context of purchasing a new electric vehicle?  

• What is the current capacity of charging stations in WA, and what is the gap to where 

it needs to be to fulfill the 2035 goal of 100% passenger ZEV? 

• How do people feel about electric vehicles and related policies/legislation? 

 

Sample selection 

 

The target population for this research project consisted of Washington state residents 

aged 18 or older who own a vehicle. This included individuals who have purchased either 

electric or non-electric vehicles and who may be responsible for making vehicle purchase 

decisions within their household. By including both electric and non-electric vehicle owners, this 

study explores the factors influencing vehicle purchases, particularly what factors were most 

important in the decision-making process. Understanding the motivations, preferences, and 

considerations of these consumers provided valuable insight into the effectiveness of incentives, 

market trends, and potential barriers to EV adoption.  

The subject selection process used convenience sampling. Invitations to the survey were 

distributed through social media platforms and the professional and personal networks of the 

researchers to reach a diverse group of vehicle owners. Additionally, QR codes linking to the 

survey were posted at multiple EV charging stations within the research area to encourage 

participation from electric vehicle owners. Although convenience sampling may limit 

representativeness, the chosen methods enabled efficient data collection from a relevant and 

informed population, facilitating a meaningful analysis of purchasing motivations and general 

sentiment toward EVs. 

 

Data collection procedures 

 

This research project employed a mixed-methods approach, combining quantitative and 

qualitative analyses. The former was used on existing data sources, including vehicle registration 

records and alternative fuel station information. Coupled with established statistics on EV 

driving range, EV battery charging speed, and EV operation recommendations, the researchers 

used both large datasets to analyze the discontinuity of charging infrastructure in Washington 

State.  



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This study also employed a qualitative approach, using open-ended survey questions, to 

explore consumer attitudes and decision-making processes regarding EV adoption. This thematic 

analysis was essential for understanding the motivations, barriers, and firsthand experiences that 

influence EV purchase decisions. The survey conducted for this research included both 

structured and semi-structured questions to collect qualitative and quantitative data. Survey 

respondents were asked about their experiences with EVs, general driving preferences and 

habits, concerns regarding EV range and infrastructure, and opinions on current incentive 

programs. Demographic questions in the survey were included to help the researchers understand 

how different consumer characteristics may influence the decision to purchase an EV and to gain 

insight into overall sentiment toward EV adoption.  

 

Validity and reliability 

 

The survey's overall distribution limited the sample population because it relied on 

researchers’ individual networks, both personal and professional. As a result, a large majority of 

survey respondents are from Western Washington. Additionally, missing zip codes and zip codes 

not in Washington were removed from the survey results to avoid unintended variation and to 

ensure any analysis could be traced back to a Washington resident. Additionally, the zip code 

question in the survey was displayed last and was required. Therefore, if someone left this 

question blank, it could be considered an incomplete response. Filtering these values reduced 

missing responses among participants. Because the survey was only available online, it was 

limited to people with an internet connection and either a computer or a mobile device. A future 

iteration of this research could adopt a more programmatic approach to distribute the survey to a 

broader population and improve accessibility.  

Both existing data sources were quite large and, in some contexts, lacked documentation 

indicating which variable meant what. One example of this is the “status code” variable in the 

dataset for alternative-fuel charging stations. It was discovered that a value of “P” in this dataset 

meant the station was planned and not yet available. These were removed from the analysis to 

better gauge truly existing infrastructure. 

 

Ethical considerations 

 

This research followed key ethical principles regarding data privacy, consent, anonymity, 

and bias in survey data. Participants were informed of the study’s purpose through a project 

description, and participation was voluntary, in line with ethical guidelines on consent and 

transparency (ACM, 2022, Sections 1.3, 1.7). Anonymity was preserved by omitting identifiable 

information and using broad demographic categories (ACM, 2022, Sections 1.6–1.7). To ensure 

data privacy, Qualtrics encrypted responses and limited access to authorized personnel. Survey 

bias was minimized through careful review of questions for neutrality (ACM, 2022, Section 1.1). 

For secondary data, ethical considerations included legal access and data accuracy. 

Datasets were obtained from publicly accessible sources such as Washington’s open data portal 

and the National Renewable Energy Laboratory and will be cited appropriately (ACM, 2022, 

Section 1.5). Data were evaluated for reliability and relevance to ensure sound analysis and 

conclusions (ACM, 2022, Section 2.1). 

 



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Results and Discussion 
 

Survey demographics 

 

The survey, used for both quantitative and qualitative analysis, received 267 responses, 

but 220 were included after filtering to match the target population of Washington state residents 

aged 18 or older who own a vehicle. While the survey aimed to understand Washington state 

residents' views on EVs, the use of convenience sampling likely led to demographics and 

responses that do not fully represent the target population. Most respondents were from Western 

Washington, with a sizeable portion from a south-central zip code and a small number from 

Eastern Washington.   

The skewed distribution of demographic factors also suggests that the survey responses 

are unlikely to be representative of the target population. Figure 1 shows the distribution of 

demographics within the survey population, skewed towards certain groups. 74.1% of 

respondents had annual household incomes greater than $100,000, 75.1% owned their primary 

residence, 89.4% were between the ages of 25 and 64, and 72.4% had completed a college 

degree. As a result, the conclusions drawn from the survey data are likely to reflect the 

perspectives of these majority groups rather than those of the broader target population. 

However, analyzing the survey data would still yield valuable insights for forming 

recommendations regarding EV adoption.  

 

Figure 1 

 

Distribution of survey respondent demographics 

 
Note. The survey's demographic questions yield a wide range of perspectives. Although the 

question about annual household income could have included more layers at the higher end to 

better split the income distribution. 

 



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What individual characteristics and external factors contribute most to consumer behavior in 

the context of purchasing a new electric vehicle? 

 

Based on survey results, all demographic factors included — current car type, age, annual 

household income, primary residence, and education level — show some correlation with the 

likelihood of purchasing an EV. Current EV owners were more likely to consider another EV 

purchase. In contrast, hybrid owners had a median response of “neither likely nor unlikely” and 

non-EV owners had more responses for “unlikely” (Figure 2). Households with higher incomes 

had more responses indicating “somewhat likely” or “extremely likely” to purchase an EV 

(Figure 3). Responses from homeowners were split between “likely” and “unlikely,” while 

renters and those in other housing situations had more “unlikely” responses (Figure 4). Lower 

education levels had more "unlikely” responses, while higher education levels were split between 

“likely” and “unlikely” or had higher counts for “likely” (Figure 8).  

The overall pattern of responses shifting from “unlikely” to split or “likely” or vice versa 

across demographic groups suggests a possible relationship between these factors and the 

likelihood of purchasing an EV. However, the varied response distributions within each 

demographic group make it difficult to identify specific individual characteristics that impact the 

likelihood of purchasing an EV. 

 

Figure 2 

 

Likelihood distribution by current vehicle type 

 

 

Note. The median likelihood for each group were: non-EV – somewhat unlikely, EV – extremely 

likely, hybrid – neither likely nor unlikely. 

 

Figure 3 

Likelihood distribution by annual household income 

 



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Note. The median likelihood for each group was: $0-$49,999 – extremely unlikely, $50,000-

$99,999 – somewhat unlikely, $100,000-$149,999 – somewhat unlikely, and more than $150,000 

– somewhat likely. There was low granularity for amounts over $150,000, suggesting that more 

income range options could have been included in the survey. 
 

Figure 4 

Likelihood distribution by primary residence 

 

 

Note. The median likelihood for each group were: own – neither likely nor unlikely, rent – 

somewhat unlikely, other – extremely unlikely. 

 

Figure 5 

 

Likelihood distribution by education 



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Note. shows the distribution of likelihood of purchasing an EV across different education levels. 

The median likelihood for each group was: less than high school* – extremely likely, high school 

graduate – somewhat unlikely, some college – extremely unlikely, 2-year degree – extremely 

unlikely, 4-year degree – neither likely nor unlikely, professional degree – somewhat unlikely, 

master's – somewhat likely, and doctorate – neither likely nor unlikely. 

 
To further explore this relationship, the correlation coefficient was calculated between the 

likelihood of purchasing an EV and the demographic factors. This was done to quantify the 

strength of the relationship between these variables and determine whether individual 

characteristics influence the likelihood of an EV purchase. The signs of the coefficients for the 

demographic factors generally reflect the relationships observed in the survey data. For example, 

the age range had a coefficient of -0.150, and the highest level of education had a coefficient of 

0.323, indicating the likelihood tending towards “likely” with younger ages and higher levels of 

education. However, all the coefficients were between -0.5 and 0.5, suggesting a weak or 

negligible correlation between the likelihood of purchasing an EV and the demographic factors 

(Turney, 2024). This suggests that individual characteristics, specifically demographics, do not 

significantly impact EV purchase likelihood. These findings are consistent with previous 

research on the influence of demographics on EV adoption, which found weak or indirect effects 

(Rezvani et al., 2014; Sierzchula et al., 2014). 

According to the survey results, the external factors affecting EV adoption were high EV 

purchase prices and a lack of charging infrastructure. Survey participants were asked to rank 

specific barriers to EV adoption from largest (1) to smallest (7) and the count of each ranking for 

each barrier is shown in Figure 9. High EV purchase price and the lack of charging infrastructure 

were consistently ranked among the top 2 barriers, with high counts at rankings 1, 2, and 3. 

Other barriers with higher rankings included total cost of ownership and battery reliability, which 

had relatively high counts for rankings of 2, 3, and 4. Other studies examining the relationship 

between various factors and EV adoption also found that car or ownership costs, a lack of 

charging infrastructure, and reliability are influential (Carley et al., 2013; Liao et al., 2017; 

Sierzchula et al., 2014). This highlights the need to address high EV purchase prices and lack of 

charging infrastructure to increase EV adoption.  

 



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Figure 6 

 

Participant ranking of EV adoption barriers from largest (1st) to smallest (7th) 

 

Note. The median ranking for each barrier were 3, 3, 6, 5, 4, 4, and 3, respectively. 

 

Decision tree classification model 

 

A selection of survey variables was used in a decision tree classification model to predict 

whether someone was “somewhat” or “extremely” likely to purchase an EV. The question of 

whether someone currently owns an EV was intentionally left out because of the obvious 

correlation between owning one and purchasing another. The goal of the model was to determine 

whether a combination of variables was more effective at predicting the target outcome: the 

likelihood of purchasing an EV. These variables could be leveraged to improve EV adoption 

across the state.  

While not all variables were classified as having high importance, the variables used in 

the model were: income range, whether someone owned or rented their primary residence, 

confidence level in the state having the infrastructure to support its 100% ZEV goal, how high 

someone thinks their energy bill might be if they charge an EV at home, sentiment toward the 

US phasing out gasoline-powered vehicles, rating of availability of EV models on the market, 

age range, highest level of education attained, awareness level of government incentive and 

longitude of respondent’s zip code. 

By adjusting the constraints to prevent overfitting and memorizing the data, ensuring 

splits are performed with enough data, and reducing variance by requiring leaves to have more 

samples, the model’s accuracy is 87.5%. Table 1 details the classification report. It is slightly 

better at classifying Class 2 outcomes (likely) vs. Class 1 outcomes (unlikely). The confusion 

matrix for the decision tree model shows high prediction accuracy, with only 5 actual values 

misclassified. The most important features of the model are (in ranked order): sentiment toward 

the phasing out of gasoline-powered vehicles, bill expectations for at-home EV charging, 

primary residence (own or rent), and age.  



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Table 1 

 

Decision tree model classification report 

 

Score precision recall f1-score support 

Class 1 0.84 0.89 0.86 18 

Class 2 0.90 0.86 0.88 22 

Accuracy   0.88 40 

Macro avg 0.87 0.88 0.87 40 

Weighted avg 0.88 0.88 0.88 40 

Note. The full classification report shows the precision, recall and f1-score of the decision tree 

model. The model is slightly better at predicting class 2 (90%) than at predicting class 1 (84%). 

 

The top four features could be applied to larger populations in specific contexts. The 

decision tree predicts whether someone will be likely to buy an EV based on their sentiment 

toward the phasing out of gasoline-powered vehicles, to be excited or neutral (phased_out < = 

2.5), the individual being a homeowner (residence < = 1.5), and the assumption that one’s bill 

might be on the lower side (bill < = 2.5). In contrast, the model predicts an unlikely response 

when sentiment toward phasing out of gasoline-power vehicles in negative (phased_out > 2.5), 

assumed bill increase is on the higher side (bill > 2.5), and they are older (age > 3.5).  

  

What is the current capacity of charging stations in WA and what is the gap to where it needs 

to be to fulfill the 2035 goal of 100% passenger ZEV? 

 

Two primary data sources were used in the calculations to address the issue of current 

charging infrastructure capacity and the growth needed to meet Washington’s 100% ZEV goal 

by 2035. The first is an export of the National Renewable Energy Laboratory’s database of 

alternative fuel stations. The export includes 2,904 stations and information about each, 

including latitude and longitude, connector types and counts, accessibility (public vs private), 

and many other variables. For this research project, the data have been filtered to include only 

stations not in “Planned” status, resulting in a final total of 2,785 stations analyzed.  

To quantify “EV Deserts” in the state, additional data (Charger Types & Speeds, n.d.) 

was merged with this dataset. Some stations had multiple connector types, while others had 

connector types not listed in the specifications data source. Only stations with available 

specifications were analyzed.  

 

EV Deserts 

 

The survey results reveal challenges with the current EV charging infrastructure in 

Washington. Most notably, respondents cited simple availability, deficient or broken stations, 

and range anxiety (a lack of comfort driving longer distances due to poor station coverage). 



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Given the current infrastructure's limitations for the state's EV population, significant 

improvements are needed to sustain a fleet that’s 24 times its current size. To help address 

infrastructure gaps, additional analysis of charging-station location data was conducted to 

understand the prevalence of “EV Deserts” in Washington. For this paper, “EV Deserts” are 

defined as areas with limited charging availability, including both connector specifications and 

quantities, as well as station count. More specifically, charging stations with fewer than 10 

nearby stations were classified as “EV Deserts.” In this case, “nearby” is defined as outside of 

the minimum driving range for the average EV after charging at that station for 60 minutes, but 

inside the maximum driving range after charging at that center station for 60 minutes. The 

station’s maximum connector speed was used for this analysis. Stations with DC Fast Chargers, 

including CHAdeMO and Tesla chargers, are outliers in these results because it takes only 20 to 

60 minutes to get a full charge on an EV using one of these connectors. Once fully charged, 

driving range varies by car make and model, but on average, EVs have a driving range of 

approximately 270 miles (Vehicle Technologies Office, 2024).  

It was determined that 300 of the stations analyzed fit into this category and therefore 

represent an EV Desert in Washington. That does not include areas without any stations. In 

summary, approximately 11% of the level 2 charging stations have little to no continuity to the 

rest of the state’s EV infrastructure. It should also be noted that this analysis was based on 

charging for 60 minutes. Filling up a gasoline-powered vehicle takes between 5 and 8 minutes, 

on average (GasBuddy, 2018). 

 

Figure 7 

 

EV charging continuity 

 

 



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Note. Blue areas are regions of EV charging continuity for stations with at least one J1772 Level 

2 charger. Therefore, the areas left blank on the map would be considered “EV Deserts” — 

where either no charging station exists or there is very limited availability and little continuity to 

other nearby charging stations. A conversion factor of x/1.3 was used to convert air miles to 

approximate driving miles (M. A. Diaz et al, 2003) 

 

These areas of discontinuity are further emphasized by examining where along the longitudinal 

lines alternative-fuel charging stations exist and how that corresponds to where electric vehicles 

are registered (see Figure 8). The mirroring of these two distributions reveals the lack of 

infrastructure support for driving outside of the immediate area for EV owners.  

 

Figure 8 

 

Longitudinal distribution of charging stations and registered electric vehicles 

 

 
Note. Longitude data for both charging stations and registered EVs in Washington were binned 

into equally distributed groups. The lines represent a percentage of the total for each measure.  

 

Infrastructure demand 

 

Tesla remains the most popular EV manufacturer in Washington state (see Figure 9), yet 

its presence in the public charging infrastructure remains limited. As the number of Tesla 

vehicles continues to grow, it will be essential for both the manufacturer and the state to invest in 

expanding accessible, non-proprietary charging infrastructure. Recent organizational changes at 

Tesla (Kirkham et al., 2024) underscore the risks of relying heavily on a single manufacturer for 

public EV charging access, further highlighting the need for diversified infrastructure solutions.   

 



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Figure 9 

 

New and renewal transactions by car make 

 
Note. This line chart displays electric vehicle registrations by make, focusing on the top five 

manufacturers. Tesla has experienced the most substantial growth in the past five years. 

 

To assess current demand and future needs for EV charging infrastructure, Washington 

state vehicle registration data from 2019 to 2024 was analyzed. In 2024, electric vehicles 

accounted for 4.1% of all registration transactions. Since 2019, EV registrations have grown by 

an average of 20% annually. If this growth rate continues, EVs will not match the number of all 

vehicles on the road until approximately 2041, well beyond the state's 2035 goal. This estimate 

does not account for the slower decline of internal combustion engine (ICE) vehicles, which 

have decreased by only 5% per year. With over 2 million ICE vehicles currently registered, 

significant turnover will take time. Survey results also highlight strong support for targeted 

infrastructure expansion. Sixty-one percent of respondents preferred new charging stations along 

major highways, followed by workplace and shopping center locations (see Figure 10). 

 

Figure 10 

 

New charging infrastructure potential 

 



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Note. The answer to the question “Where do you want to see charging stations built?” varied 

between age ranges, but not significantly. For this question, respondents could select up to 3 

options or type in their own option (which are also reflected in the table). The number indicates 

how many people in that age group selected that option; the percentage is proportional to the 

total number of people in that age group who answered that question. This question was only 

asked of people who indicated they currently own an electric vehicle.  

  

How do people feel about electric vehicles and related policies/legislation? 

 

Three open-ended survey questions yielded 192 responses, from which seven key themes 

emerged: infrastructure, financial/economic concerns, environmental impact, product lifecycle, 

policies/regulations, behavioral factors, and emotional responses. Infrastructure and 

environmental concerns were most frequently mentioned, often accompanied by strong 

emotional reactions. 

Question 15 responses (n = 116) emphasized infrastructure, environmental impact, and 

emotion. Emotional sentiment was present in 59% of responses, with 83% of those expressing 

negative views — primarily pessimism, resistance to change, and range anxiety. 

Infrastructure concerns appeared in 48% of responses, focusing on the reliability, 

availability, and speed of charging stations, as well as limitations of the power grid. 

Environmental motivations were cited by 25%, though 16 respondents raised sustainability 

concerns, particularly about battery disposal and electricity sources. Financial concerns (19%) 

related to EV costs and equity issues. Policy-related feedback (15%) reflected support for more 

decisive government intervention through incentives and emissions regulation, though opinions 

were mixed on the best way to achieve climate goals. Additionally, some respondents indicated 

that electric vehicles do not adequately meet their transportation needs, particularly in terms of 

interior space and hauling capacity. 



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Question 26 reinforced these themes. Pessimism (30%) and resistance (23%) remained 

high, with respondents citing inadequate infrastructure and technology. Environmental 

awareness (21%) persisted, though often tempered by practical concerns. Infrastructure issues — 

charging access (15%), range anxiety (15%), broken stations (16%), and slow charging speeds 

(8%) — were frequently cited as barriers. Financial concerns (13%) focused on cost-

effectiveness but also noted burdens from upfront expenses and home charging setup. 

Question 32 responses largely mirrored Q15 and Q26, with high levels of pessimism 

(30%) and resistance to change (23%), especially around cost, infrastructure, and technological 

constraints. Calls for policy intervention (10%) continued, emphasizing affordability and 

adoption incentives. 

Overall, the analysis indicates that while environmental motivations exist, EV adoption is 

significantly hindered by infrastructure deficits, financial barriers, and emotional skepticism — 

highlighting a need for policy action and system-wide improvements. 

 

Recommendations and Conclusions 
 

Policy Implications 

 

This study underscores the need for enhanced policy interventions to support electric 

vehicle adoption. Addressing the cost barrier is paramount; revising existing trade policies — 

such as tariffs on imported EVs and batteries — may improve market competitiveness, expand 

model variety, and reduce prices (Magill, 2024; Workman, n.d.). Incentives should also target 

current EV owners, not solely first-time buyers. Long-term benefits — such as toll lane access 

regardless of carpool status — could reduce the total cost of ownership and encourage retention. 

Given widespread skepticism about Washington’s 100% ZEV target by 2035, a reassessment of 

the timeline or adoption percentage is warranted. Public resistance, particularly in regions with 

limited infrastructure, suggests significant groundwork is needed before such goals are 

achievable. 

 

Environmental considerations 

 

Respondents highlighted environmental concerns tied to both vehicle disposal and the EV 

lifecycle. Transitioning from internal combustion engine (ICE) vehicles necessitates structured 

buyback and recycling programs to recover critical materials and reduce landfill waste. Further, 

the sustainability of EVs is contingent on reducing environmental harm from battery production. 

This includes investing in battery recycling, reducing reliance on rare-earth materials, and 

supporting next-generation battery technologies. To fully realize EVs' environmental benefits, 

the charging infrastructure must also transition to renewable energy sources. Integrating solar, 

wind, and hydroelectric power, alongside grid-scale storage solutions, is critical for reducing 

reliance on fossil fuels and ensuring consistent, clean energy delivery. Finally, strengthening grid 

resilience is essential as electricity demand rises. Upgrading the power grid, supporting 

decentralized energy solutions (e.g., home solar and battery storage), and enhancing demand-

response systems will be vital to supporting widespread EV use without overloading current 

infrastructure. A transitional strategy should also address non-EV emissions. Policies promoting 

fuel efficiency, cleaner fuels, and carbon offsets for ICE and hybrid vehicles can ensure 

environmental progress before full electrification. 



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Technological improvements 

 

Key technological advancements are necessary to support broader EV adoption. 

Expanding and standardizing charging infrastructure — particularly along major transit routes — 

will alleviate range anxiety and improve the feasibility of long-distance travel. Mobile 

applications that integrate data from all major charging networks can further help drivers locate 

available charging stations. Manufacturers should develop EVs with greater power, durability, 

and utility to expand market appeal for industries and off-road applications. Enhanced battery 

insulation and temperature control are also needed to ensure reliability in extreme climates. 

Improving charging station security is another critical concern. Implementing tamper-resistant 

designs, remote monitoring, and vandalism deterrents will help maintain consistent access and 

reduce service disruptions. Together, these policies, environmental, and technological strategies 

can collectively address the barriers identified in this study and support a more sustainable and 

accessible EV ecosystem. 

 

Limitations and Future Work 

 

This study, while informative, was limited by its reliance on convenience sampling, 

which may affect the generalizability of its findings. The demographic distribution of 

respondents suggests that the sample may not accurately represent the broader population. It is 

therefore crucial for future research to consider a more systematic and stratified approach to 

survey distribution. This will ensure a more accurate and comprehensive understanding of the 

factors influencing EV adoption across different regions and accessibility levels. The qualitative 

analysis was conducted manually, introducing potential subjectivity and interpretation bias. 

Employing automated sentiment analysis tools or inter-rater reliability measures in future studies 

could enhance the consistency and objectivity of qualitative data interpretation.  

Despite these limitations, the analysis offers meaningful insights into the barriers to EV 

adoption in Washington state. The resulting recommendations serve as a foundation for future 

policy and program development. These recommendations, when refined through feasibility 

studies and stakeholder engagement, will not only ensure alignment with state infrastructure 

capacity and consumer behavior patterns but also enhance their practicality and effectiveness. 

Implementation will require significant financial investment over the next decade, likely 

supported by federal funding, public-private partnerships, and targeted state initiatives. A phased 

strategy — incorporating near-term incentive adjustments, medium-term infrastructure 

development, and long-term regulatory reforms — will be critical to ensuring a sustainable and 

equitable transition to electric vehicles in Washington. This approach provides a clear roadmap 

for the future and instills confidence in the management of the transition. 

  



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References 
 

AAA Washington Annual EV Survey Shows Strong Interest in Electric Vehicles, Charging Gap 

Hurdles. (2024, September 25). [Press release]. 

https://wa.aaa.com/impact/press/releases/ev-survey-shows-strong-interest-and-charging-

gap-hurdles 

ACM Ethics. (2021, October 22). ACM Code of Ethics and Professional Conduct. 

https://ethics.acm.org/ 

All Stations (Version v1). (n.d.). [Dataset; API]. National Renewable Energy Laboratory. 

https://developer.nrel.gov/docs/transportation/alt-fuel-stations-v1/all/ 

Askariyeh, M. H., Kota, S. H., Vallamsundar, S., Zietsman, J., & Ying, Q. (2017). AERMOD for 

near-road pollutant dispersion: Evaluation of model performance with different emission 

source representations and low wind options. Transportation Research Part D Transport 

and Environment, 57, 392–402. https://doi.org/10.1016/j.trd.2017.10.008 

At a Glance: Electric Vehicles. (2023a). In Office of Energy Efficiency & Renewable Energy. 

U.S. Department of Energy. https://afdc.energy.gov/files/u/publication/electric-

drive_vehicles.pdf 

At a Glance: Electric Vehicles. (2023b). In Department of Energy. 

https://afdc.energy.gov/files/u/publication/electric-drive_vehicles.pdf 

Axsen, J., & Kurani, K. S. (2013). Hybrid, plug-in hybrid, or electric—What do car buyers want? 

Energy Policy, 61, 532–543. https://doi.org/10.1016/j.enpol.2013.05.122 

Beshay. (2024, November 18). About 3 in 10 Americans would seriously consider buying an 

electric vehicle. Pew Research Center. https://www.pewresearch.org/short-

reads/2024/06/27/about-3-in-10-americans-would-seriously-consider-buying-an-electric-

vehicle/ 

Brenan, M. (2023, April 12). Most Americans are not completely sold on electric vehicles. 

Gallup.com. https://news.gallup.com/poll/474095/americans-not-completely-sold-

electric-vehicles.aspx 

Carley, S., Krause, R. M., Lane, B. W., & Graham, J. D. (2012). Intent to purchase a plug-in 

electric vehicle: A survey of early impressions in large US cites. Transportation 

Research Part D Transport and Environment, 18, 39–45. 

https://doi.org/10.1016/j.trd.2012.09.007 

Charger types and speeds. (n.d.). U.S. Department of Transportation. 

https://www.transportation.gov/rural/ev/toolkit/ev-basics/charging-speeds 

Coffin, D., Walling, J., & U.S. International Trade Commission. (2024). Chinese vehicle exports: 

electrified. In U.S. International Trade Commission Executive Briefings on Trade. 

Coward, R. (2024, October 6). Four years after buying an electric car, why am I still forced to 

play hunt-the-charger? The Guardian. 

https://www.theguardian.com/commentisfree/2024/oct/05/four-years-after-buying-an-

electric-car-why-am-i-still-forced-to-play-hunt-the-charger 

Data.gov. (2024, May 8). Department of Transportation - Motor Vehicle Registrations 

Dashboard data. https://catalog.data.gov/dataset/motor-vehicle-registrations-dashboard-

data 

Diaz, M. A., Hendey, G. W., & Winters, R. C. (2003). How far is that by air? The derivation of 

an air:ground coefficient. Journal of Emergency Medicine, 24(2), 199–202. 

https://doi.org/10.1016/s0736-4679(02)00725-4 



     Business Management Research and Applications: A Cross Disciplinary Journal 

 

 

19  

Diaz, M., & Clark, C. (2023). Federal Policies to Expand Electric Vehicle Charging 

Infrastructure. In Congressional Resaarch Service (No. R47675). Congressional 

Research Service. Retrieved January 20, 2025, from 

https://crsreports.congress.gov/product/details?prodcode=R47675 

GasBuddy. (2018). Foot traffic report for the fuel and convenience retailing industry 2018. In 

GasBuddy. https://blog-content.gasbuddy.com/uploads/2019/01/2018-GasBuddy-Foot-

Traffic-Report.pdf 

Hardman, S., Shiu, E., & Steinberger-Wilckens, R. (2016). Comparing high-end and low-end 

early adopters of battery electric vehicles. Transportation Research Part a Policy and 

Practice, 88, 40–57. https://doi.org/10.1016/j.tra.2016.03.010 

Ingeborgrud, L., Heidenreich, S., Ryghaug, M., Skjølsvold, T. M., Foulds, C., Robison, R., 

Buchmann, K., & Mourik, R. (2019). Expanding the scope and implications of energy 

research: A guide to key themes and concepts from the Social Sciences and Humanities. 

Energy Research & Social Science, 63, 101398. 

https://doi.org/10.1016/j.erss.2019.101398 

Javid, R. J., & Nejat, A. (2016). A comprehensive model of regional electric vehicle adoption 

and penetration. Transport Policy, 54, 30–42. 

https://doi.org/10.1016/j.tranpol.2016.11.003 

Kirkham, C., Jin, H., & Roy, A. (2024, May 15). The inside story of Elon Musk’s mass firings of 

Tesla Supercharger staff. Reuters. https://www.reuters.com/business/autos-

transportation/inside-story-elon-musks-mass-firings-tesla-supercharger-staff-2024-05-15/ 

Landry, N. (2024, September 17). EV battery charging best practices. FLO. 

https://www.flo.com/insights/ev-battery-charging-best-practices-the-20-80-rule-for-

batteries 

Liao, F., Molin, E., & Van Wee, B. (2016). Consumer preferences for electric vehicles: a 

literature review. Transport Reviews, 37(3), 252–275. 

https://doi.org/10.1080/01441647.2016.1230794 

Magill, K. (2024, September 16). Biden finalizes China tariff hikes, including for EVs, batteries 

and solar panels. Utility Dive. https://www.utilitydive.com/news/joe-biden-china-tariff-

hikes-ev-battery-semiconductor-final/727014/ 

Rezvani, Z., Jansson, J., & Bodin, J. (2014). Advances in consumer electric vehicle adoption 

research: A review and research agenda. Transportation Research Part D Transport and 

Environment, 34, 122–136. https://doi.org/10.1016/j.trd.2014.10.010 

Sierzchula, W., Bakker, S., Maat, K., & Van Wee, B. (2014). The influence of financial 

incentives and other socio-economic factors on electric vehicle adoption. Energy Policy, 

68, 183–194. https://doi.org/10.1016/j.enpol.2014.01.043 

Sovacool, B. K., Axsen, J., & Kempton, W. (2017). The Future Promise of Vehicle-to-Grid 

(V2G) Integration: A Sociotechnical Review and Research Agenda. Annual Review of 

Environment and Resources, 42(1), 377–406. https://doi.org/10.1146/annurev-environ-

030117-020220 

Turney, S. (2024, February 10). Pearson Correlation Coefficient (r) | Guide &#038; Examples. 

Scribbr. https://www.scribbr.com/statistics/pearson-correlation-coefficient/ 

Vehicle Technologies Office. (2024, January 1). Top range for model year 2023 EVs was 516 

miles on a single charge. Energy.gov. 

https://www.energy.gov/eere/vehicles/articles/fotw-1323-january-1-2024-top-range-

model-year-2023-evs-was-516-miles-single 



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20  

Washington State Department of Commerce. (2024, August 4). Washington EV Instant Rebate 

Program. https://www.commerce.wa.gov/clean-transportation/ev-instant-rebate/ 

Washington State Plan for Electric Vehicle Infrastructure Deployment. (2022). In Washington 

State Department of Transportation (No. 1416150644). Washington State Department of 

Transportation. Retrieved January 20, 2025, from 

https://stlow.iii.com/record=b1951224~S2 

Workman, D. (n.d.). Electric cars imports by country. 

https://www.worldstopexports.com/electric-cars-imports-by-country/ 

 


