Are Existing Battery Electric Vehicles Adoption Studies Able to Inform Policy? A Review for Policymakers
2. Current Policies to Encourage EV Adoption
2.1. Monetary Incentives
2.2. Non-Monetary Incentives
2.3. National Targets
2.4. Mandates and Regulations
- Subsidies to the industry for battery innovation seem to be insufficient in most countries. Policymakers should think carefully about how to promote the R&D of battery technology and organize a life-cycle supply chain in the industrial context.
- Most non-monetary policies, such as travel priority (access to bus/HOV lanes, exemption from traffic restrictions) and parking priority are designed and deployed on the local level rather than the national level. Therefore, non-monetary policy measures should be implemented with more flexibility based on the specific local context.
- Monetary incentives are important to close the gap between the purchase price of EVs and ICEs and increase adoption rates. However, the importance of non-monetary incentives should not be ignored by policymakers as they may be more effective in the long term when price is not a major barrier to EV adoption.
- Governance awareness on promoting EV adoption and funding to support local policy measures could be imbalanced within the same country. This may lead to certain areas standing out in terms of offering effective EV policy interventions compared to other regions. Therefore, national targets for EVs and charging infrastructure should be set to guide local governments, and funding from the central government should also be allocated reasonably across regions.
- Fuel economy regulation is an efficient way to help design a taxation system for motor vehicles and ZEV mandates for EV manufacturers.
- Regulations on buildings to meet EV charging requirements and the regulation of public procurement both enable the roll-out of EVs and chargers to potential consumers, which may attract wider adoption.
3. Empirical Studies on Potential Acceptance of Policy Measures to Encourage EV Adoption
3.1. Effective National and Local Policy Measures
3.2. Effective Policy Measures over Time
- Previous studies can be divided into static and dynamic analyses. Static studies focus on the cross-sectional analysis of policy instruments at a specific or fixed time, while dynamic analysis presents the impacts of policies using panel data.
- Effective measures are diverse across different geographic areas, but monetary incentives are significant in all cases. Similarly, the effective formulas for wider EV adoption diversify in different studies, but they all stress the importance of monetary measures and charging infrastructure construction.
- Local policy measures are equally important as national measures. The reason why some local governments stand out at developing successful measures relates to the attitudes of local governments towards clean energy . This finding suggests that the endeavours of local governments are to strengthen their environmental awareness.
- Evidence from California and Norway shows that policies deployed in different countries are not completely independent, they can interact and spark new initiatives. This finding highlights the capacity of some governments to act fast and localise good external policies when this exchange of new policy takes place.
- By observing the dynamic analysis of significant policy measures, monetary incentives are very effective in the short term. However, as the technology develops, the cost of EVs is expected to decrease and the price gap between EVs and ICEs would be minimal. Then the government subsidy process should shift from consumers to manufacturers. In the long term, the focus of governments should be on the integration of EVs and the electricity grid to help achieve more sustainable targets both in the transport and energy industries.
- Over time, the market has had a tendency to transition from PHEVs to BEVs. Therefore, policy measures that target BEVs are more important in the future. Meanwhile, more specific policies should be tailored to different kinds of EVs, as the policy incentives for these two types of cars are different.
4. Future Policy Interventions Driven by Consumer Adoption Intention Studies
4.1. Perceived Behavioural Control
4.2. Subjective Norms
4.5. External Factors: Monetary Policies, Marketing, V2G Availability
4.6. Individual Characteristics
Conflicts of Interest
|Author/Year||Sample Size||EV Adoption Intention Measurement||Significant Factors||Methodology||Socio-Economic Characteristics Stated with EV Adoption Intention|
|Chen et al. (2020) ||4885 survey respondents, Denmark, Finland, Iceland, Norway, Sweden||Potential EV adoption||Fuel economy|
|Hierarchical regression analysis||Younger males|
Higher number of children
Have previous EV experiences
Hold sustainability values
|Zhuge and Shao (2019) ||Beijing, China||EV purchase intention||Vehicle price (1)|
Vehicle usage (2)
Social network (3)
Environmental awareness (4)
Purchase restriction (5)
Traffic restriction (6)
Multinomial Logit (MNL) models;
|Higher income |
Higher education level
People who have similar attitudes towards vehicle usage and purchase restrictions tend to live close to each other
|Xu et al. (2019) ||382 respondents, Zhejiang province, China||Customers’ purchase intention of BEVs||Perceived behavioural control (1)|
Subjective norm (2)
Environmental performance (3)
Monetary incentive policy (5)
|Structural equation model (SEM);|
Neural network (NN);
|Huang and Ge (2019) ||502 survey, Beijing, China||EV purchasing intention||Consumer cognitive status|
Perceived behavioural control
Monetary incentive policy measures
|structural equation model (SEM);||Younger group |
Group without cars
High level of education
Male group (focus on EV performance)
Female group (focus on economic incentives)
|Simsekoglu and Nayum (2019) ||205 conventional car drivers, Norway||Intention to buy a BEV||Perceived behavioural control (1)|
Subjective norm (2)
Environmental-economic attributes (3)
|Regression analysis;||Being male negatively related to the intention|
|Habich-Sobiegalla et al. (2019) ||1080 respondents, China||EV purchase intention||Social network||Ordered logistic regression||Older groups|
Higher income groups
|Haustein and Jensen (2018)||CV users (1794), Sweden and Demark||EV adoption intention||Symbolic attitudes (1)|
Perceived behavioural control (2)
Affective attitudes of driving pleasure and excitement (3)
Subjective norm (4)
Personal norm (5)
People with university education
|Berkeley et al. (2018) ||26,000 motorists, UK||Perceived barriers to EV adoption||Economic uncertainty|
|Exploratory factor analysis||Not mentioned|
|Wang et al. (2018) ||458 respondents, Shanghai, China||EV public acceptance||Marketing (1, −)|
Technical level (2, +)
Environmental awareness (3, +)
Perceived risks (4, −)
Structural equation model;
|Intention to buy EVs as the second car: |
Age of 18 to 25 years old
Educational level of junior middle school or lower
Household income < 50,000 yuan and 200,000 < household income < 300,000 yuan
Intention to buy EVs to replace the CVs:
41–50 years old
300,000 < household income < 500,000 yuan
|Priessner et al. (2018) ||1000 respondents, Austria||Willingness to purchase EVs||Pro-environmental attitude |
|Multinomial logistic regression (MLR)||Do not own or regularly need a car |
Bigger household size
|Egbue et al. (2017) ||157 responses of a Facebook survey||EV adoption intention||Pro-innovation attitude|
EV speed perception
|Logistic regression||Not mentioned|
|Adnan et al. (2017b) ||391 respondents, Malaysia||Customer’s EV purchase intention||Environmental concern|
|Partial least square (PLS);|
Structural equation Modelling (SEM);
Higher level of education
|Mohamed et al. (2016) ||3505 households, Canada||Intention to adopt EV||Environmental concern (indirect)|
Perceived behavioural controlAttitude
|Structural equation modelling (SEM);|
Two-Step cluster analysis;
|Typical early adopters (45%):|
Young to middle-aged, well educated, working, growing families, financial capability to afford an EV, high proportion of single detached dwellings and availability of garage, live in predominately suburban areas with some non-metropolitan members
Emerging early adopters (28.2%):
Highly educated, small families, more urban oriented with a high peak in condo and apartment dwellings, interested in EV as a future purchase in 3-5 years
Interested retirees (26.6%):
Small families, interested in a replacement vehicle, annual travelled distance is shorter
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|Policy Incentives||European Union||Norway||Netherlands||Sweden||UK||Germany||France||China||Japan||Korea||Canada||US|
|Monetary incentives||Tax benefits|
|Free tolls, parking, ferries|
|Non-monetary incentives||Travel priority|
|Target||Target for vehicles|
|Target for chargers|
|Fuel economy standards|
|Author/Year||Country||Research Focus||Effective Policy Measures|
|Effective Policy Measures in Different Countries|
|Kester et al., 2018 ||Nordic countries||Qualitative analysis of policy effectiveness||Cost reduction mechanisms (taxation exemption)|
Charging infrastructure for public
Consumer awareness (information campaigns)
|Olson 2018 ||Norway, California||Relative importance of technology improvements and EV supporting public policies||EV supporting public policies |
(motivate early adoption)
Technical deficiencies & high prices
(hinder EV penetration)
|Wang et al., 2017 ||China||EV adoption intentions||Financial policies|
Information provision policies
Convenience policies * (exemption from travel restrictions and parking priority)
|Zhang et al., 2018 ||China (Beijing)||Stated preferences towards EVs||License plate lottery *|
|Effective Paradigms of Policy Measures|
|Held and Gerrits, 2019 ||15 European cities||Configuration that is sufficient for the favourable outcome||Cost reduction measures + charging infrastructure construction + public charging grid design + ICE restriction|
|Rietmann and Lieven, 2019 ||20 countries||The effectiveness of policy measures and a prediction of future growth trends||Monetary measures + charging infrastructure|
|Nie et al., 2016 ||Hypothetical case study||Optimal design of subsidy by using mathematical models||Investment priority on building charging stations compared to purchase subsidies|
|Effective Policy Measures over Time|
|Li et al., 2019 ||China||Dynamic impacts of government policies||Short term: appropriate fuel vehicle license plate restriction + gradually reduced consumer purchase subsidy|
Middle term: gradually decreased manufacturer subsidy + reduced parking fees and road maintenance fees + standardised charging facilities
Long term: improve smart grid infrastructure + start vehicle-to-grid
Whole period: perfect carbon tax policy + develop EV core technologies
|Benvenutti et al., 2017 ||Brazil||Impact of public policies on the long-term diffusion dynamics of AFV||Short term: tax policies for consumers and manufacturers|
Long term: banning regulation
|Skjølsvold and Ryghaug, 2019 ||Norway||Norwegian EV transition from a socio-technical perspective||Interactive effects of policy measures across countries|
|Rietmann and Lieven, 2019 ||20 countries||The effectiveness of policy measures and a prediction of future growth trends||Policies respond to the preference of adopting BEVs more than PHEVs|
|Kangur et al., 2017 ||Netherland||How policies interact with consumer behaviour over time||A combination of monetary, structural and informational measures|
Support to BEVs, not hybrid vehicles
|Neves et al., 2019 ||24 EU countries||Factors supporting the transition to EVs over time||Technology progress|
Provision charging stations
Policies tailored individually for BEV and PHEV
|Zhu et al., 2019 ||China||Indirect network effects between EV sales and charging infrastructure constructions under the phasing out subsidy situation||Indirect network effects: subsidy from consumers to charging infrastructure |
Integration of EVs with clean electricity production
Increase in gasoline price
|Significant Factors for EV Adoption Intentions||Suggestions for Policy Interventions|
|Perceived behavioural control||Vehicle price, vehicle usage |
|Driving range, battery life |
|Subjective norm||Social networks |
|Attitudes||Environmental awareness [35,41,44]|
|Personal norms (individual beliefs) |
|Emotions||Driving emotions |
|External factors||Monetary incentive policy [39,40]|
|V2G capability |
|Consumer cognitive status |
|Socio-economic characteristics||Age, gender, income, education level, number of children, household size, people with similar attitudes towards EVs live closer, do not own a car|
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Song, R.; Potoglou, D. Are Existing Battery Electric Vehicles Adoption Studies Able to Inform Policy? A Review for Policymakers. Sustainability 2020, 12, 6494. https://doi.org/10.3390/su12166494
Song R, Potoglou D. Are Existing Battery Electric Vehicles Adoption Studies Able to Inform Policy? A Review for Policymakers. Sustainability. 2020; 12(16):6494. https://doi.org/10.3390/su12166494Chicago/Turabian Style
Song, Rongqiu, and Dimitris Potoglou. 2020. "Are Existing Battery Electric Vehicles Adoption Studies Able to Inform Policy? A Review for Policymakers" Sustainability 12, no. 16: 6494. https://doi.org/10.3390/su12166494