Modeling and Evaluation of Market Incentives for Battery Electric Vehicles
Abstract
:1. Introduction
2. Materials and Methods
2.1. Data Collection
2.2. Generalized Linear Model
3. Modeling the BEV Market
4. Discussion
5. Conclusions
- Enact policies that will drive economic development thereby improving the purchasing power of the citizenry;
- Attract the investment of EV original equipment manufacturers (OEMs) in production facilities, as this will boost the availability of EVs and components, with a further chance of reducing the technological uncertainty amidst potential adopters;
- Invest in and promote charging infrastructure provisions, particularly fast chargers, to lower range anxiety;
- Consider offering incentives based on the potential sustainability impact associated with the vehicle types.
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Data | Description | Source |
---|---|---|
BEVs market | Number of BEVs for selected countries (2010–2018) | EAFO 1 [21], ACEA 2 [22] |
AFV market | Number of alternative fuel vehicles for selected countries (2010–2018) | EAFO [21], ACEA [22] |
Passenger car market | Total number of passenger cars for each of the studied countries (2010–2018) | ACEA [22] |
AC charger ≤ 22 kw | Number of slow charging points for selected countries (2010–2018) | EAFO [21] |
DC charger > 22 kw | Number of fast charging points for selected countries (2010–2018) | EAFO [21] |
DC charger/100 km | number of public fast charging points per 100 km of the highway (2010–2018) | EAFO [21] |
V/C ratio | The ratio of vehicles per total number of charging points | EAFO [21] |
GDP | Per capita GDP for the selected countries (2010–2018) | Eurostat [23] |
% Employment | Employment ratio as a percentage of total population (2010–2018) | OECD 3 [24] |
Gasoline prices | Gasoline pump price (EUR/lit) | WB 4 [25], European Commission [26] |
Electricity prices | Annual electricity prices in EUR | Statista [27] |
Incentives | Availability of financial and non-financial incentives | EAFO [21], ACEA [22] |
Urban population | Population living in the urbanized area | WB [25], Demographia 5 [28] |
Urban area | The urbanized area of the selected countries | WB [25], Demographia [28] |
Variable | Description |
---|---|
BEVsMS | Market share of BEVs per year |
Fast&Norm | Total number of the charging points (fast charger + normal charger) |
FCUP | Number of the fast charging points per 100,000 urban population |
V/C | BEVs per total number of the charging point |
GDP | Per capita GDP |
Emp | Employment ratio as a percentage of the total population |
Elec | Annual electricity prices in EUR |
Gas | Gasoline pump prices (EUR/lit) |
Urban Density | Urban population per km2 of urban land area |
Purchase | (yes/no) Monetary subsidy given to the buyer during first purchase of a BEV |
Reg | (yes/no) Registration tax benefit for the potential buyer of BEVs |
Own | (yes/no) Ownership or circulation tax benefit for the BEV users |
Comp | (yes/no) Company tax benefit for the BEV users |
VAT | (yes/no) Value-added tax benefit for the BEV buyers |
Other | (yes/no) Other benefits for BEV users |
Prod | (yes/no) Availability of local BEV production facility |
Infra | (yes/no) Government fiscal incentive for the installation of the charging point |
Local | (yes/no) Local incentives such as free parking, access to the bus lane, toll fees, free charging, access to the restricted area |
Criteria | Model 1 c | Model 2 d |
---|---|---|
Deviance | 33,759.311 | 4798.546 |
Observations | 135 | 135 |
Log Likelihood b | −564.274 | −432.585 |
Akaike’s Information Criterion (AIC) | 1170.548 | 907.171 |
Finite Sample Corrected AIC (AICC) | 1178.725 | 915.348 |
Bayesian Information Criterion (BIC) | 1231.559 | 968.181 |
Consistent AIC (CAIC) | 1252.559 | 989.181 |
Model 2 | Unstandardized Coefficients B | Standardized Coefficients Beta | Sig. |
---|---|---|---|
(Constant) | −284.0034 | 0.734 | |
Prod | 24.9076 | 0.880 | 0.000 * |
Comp | −16.9491 | −0.561 | 0.000 * |
GDP | 0.0005 | 0.538 | 0.000 * |
FCUP | 1.7571 | 0.530 | 0.000 * |
Emp | −0.8974 | −0.471 | 0.000 * |
VAT | 9.9685 | 0.330 | 0.000 * |
Reg | −9.4810 | −0.317 | 0.000 * |
Purchase | −7.8017 | −0.292 | 0.001 * |
V/C | 0.6767 | 0.276 | 0.001 * |
Infra | −7.4017 | −0.255 | 0.000 * |
Urban Density | −0.0028 | −0.217 | 0.001 * |
Gas | −11.4346 | −0.174 | 0.008 * |
Elec | −0.3407 | −0.108 | 0.131 |
Own | 4.0963 | 0.099 | 0.218 |
Other | 1.2978 | 0.041 | 0.701 |
Local | 0.4558 | 0.014 | 0.845 |
Year | 0.1761 | 0.034 | 0.672 |
Fast&Norm | 0.0001 | 0.038 | 0.546 |
Raw Residual | Statistic | |
---|---|---|
Mean | 0.00000 | |
95% Confidence Interval for Mean | Lower Bound | −1.01865 |
Upper Bound | 1.01865 | |
5% Trimmed Mean | −0.00829 | |
Median | 0.59505 | |
Variance | 35.810 | |
Std. Deviation | 5.984149 | |
Minimum | −26.412 | |
Maximum | 31.532 | |
Range | 57.944 | |
Interquartile Range | 6.168 | |
Skewness | 0.327 | |
N | 135.000 | |
R2 | 0.801 | |
Adjusted R2 | 0.768 |
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Ogunkunbi, G.A.; Al-Zibaree, H.K.Y.; Meszaros, F. Modeling and Evaluation of Market Incentives for Battery Electric Vehicles. Sustainability 2022, 14, 4234. https://doi.org/10.3390/su14074234
Ogunkunbi GA, Al-Zibaree HKY, Meszaros F. Modeling and Evaluation of Market Incentives for Battery Electric Vehicles. Sustainability. 2022; 14(7):4234. https://doi.org/10.3390/su14074234
Chicago/Turabian StyleOgunkunbi, Gabriel Ayobami, Havraz Khedhir Younis Al-Zibaree, and Ferenc Meszaros. 2022. "Modeling and Evaluation of Market Incentives for Battery Electric Vehicles" Sustainability 14, no. 7: 4234. https://doi.org/10.3390/su14074234