Climate change is widely recognized as a critical global concern due to the continued emission of greenhouse gases into the atmosphere, posing a significant and long-term risk to both the natural environment and society. The massive increase in Carbon dioxide (CO₂) emissions from petroleum vehicles over the last decade has strongly encouraged major automobile manufacturers to shift to environmentally friendly technologies in an effort to significantly reduce the high amount of CO₂ emissions caused by petroleum vehicles (Pažun et al., 2019) . According to the EEA (2021) , “the automotive sector is the largest factor contributing to the European Union’s emissions of CO₂”. However, additional expansion of the EU’s Battery electric vehicles (BEVs) might support the EU in achieving emission depletion goals and making steady progress toward their long-term goal of becoming GHGs-free by the next 20 years. BEVs are considered a crucial goal of Europe’s mobility system, helping mitigate the consequences of climate change and air pollution (Pažun et al., 2019) . Globally, it has been agreed that BEVs are a potentially crucial part of the climate change solution plan (Lomborg, 2013) .

In 2017, BEVs accounted for approximately 0.6% of the new cars enrolled in the European Union (EEA, 2018) . Based on future EU-WFA CO₂ prescribed limits for small and medium automobiles, BEVs might contribute 4% to 13.1% of new vehicles enrolled by 2030 (EC, 2017) . While in 2022, in the European Union’s 28 countries, BEVs accounted for approximately 9.9% of all new car enrollments (ACEA, 2022) . In comparison, there is an apparent disparity between the European Union members. The considerable disparity between the European Union members can be clearly seen. In comparison, in 2022, the number of BEVs in Germany reached 32,234 (Kane, 2022) . While in 2022, the number of BEVs in Hungary reached 18,800 (Medve, 2022) . However, the number of BEVs in Hungary has increased considerably in the last seven years. The BEVs saw the light officially in Hungary in 2016 with about 405 BEVs, which is considered the start of the BEVs in Hungary (Medve, 2022) . After 2016, the number of BEVs in Hungary increased significantly, with 1153 new BEVs recorded in 2017, 2460 in 2018, 3696 in 2019, 6101 in 2020, 10,626 in 2021, and 18,800 in 2022 (Medve, 2022) . The modern transportation system and climate change are firmly bound in various manners (Fuinhas et al., 2021) .

A large number of empirical research have examined the link between BEVs and CO₂ emissions in response to climate change. Based on Table 1 , Doucette and McCulloch (2011) examined the relationship between BEVs and CO₂ emissions. As a result, they found that BEVs can reduce up to 68.88% of CO₂ emissions compared to fuel vehicles. Furthermore, Casals et al. (2016) discovered that BEVs could help decrease harmful emissions, particularly in densely crowded regions. Hofmann et al. (2016) investigated the impact of BEVs on Carbon dioxide emissions. The investigation findings indicated that BEVs could assist in lowering carbon dioxide emissions by 25% of the total CO₂ emissions. Mishina and Muromachi (2017) determined by estimating the possible future Carbon dioxide emissions reductions from BEVs in Japan. Plötz et al. (2018) studied the impact of daily and annual driving on fuel economy and CO₂ emissions of plug-in hybrid electric vehicles in the United States. The study examined Plug-in Hybrid Electric Vehicles (PHEVs), revealing deviations from standardized values ranging from 2% to 120%, particularly in short-ranged models. It shows daily and annual driving distances significantly impact fuel consumption showing a decrease of 2% to 3% in CO₂ emissions per additional km of electric range. Fritz et al. (2019) explored and examined the impact of fuel economy regulations on CO₂ emissions reduction, highlighting the importance of plug-in electric vehicles (PEVs) to achieve targets below 75 gCO₂/km. Analyzing European regulations, which aim for 59.4 gCO₂/km by 2030, it projected PEV sales and CO₂ emissions based on data from 2010 to 2016, showing a need for PEVs to represent 27% to 41% of sales by 2030. However, the study suggested that current regulations might not meet car manufacturers’ targets by 2025.

Source: Own elaborations based on Doucette & McCulloch (2011) , Casals et al. (2016) , Hofmann et al. (2016) , Mishina & Muromachi (2017) , Plötz et al. (2018) , Fritz et al. (2019) , and Küfeoğlu & Hong (2020) .

In the last ten years, reducing CO₂ emissions has become one of the entire world’s interests in serious endeavors to mitigate the consequences of climate change on the entire planet. However, these endeavors were translated into the so-called European Union’s long-term strategy, which seeks to cut CO₂ emissions by more than half over the next 30 years (Kawase et al., 2006) . In the EU-28 countries, car emissions of CO₂ represent approximately a quarter (25%) of carbon output. Solely transportation sector has seen a rise in CO₂ emissions over the last three decades, 33.5%, compared to the residential and commercial sector, Energy supply sector, industry sector, and agricultural sector (EP, 2022) , as shown in Figure 1 .

Road transportation accounted for 71.7% of the 25% of total CO₂ emissions emitted by the transportation sector between 1990 and 2019 (European Environment Agency, 2022) . The CO₂ emissions differ between the modes of transportation, exceptionally hanging on the mode of transportation, which is distributed as follows: 60.6% standard passenger cars, 27.1% Heavy duty trucks, and 11.0% light duty trucks (EP, 2022) , as shown in Figure 2 .

According to the European Union’s long-term strategy, the best way to reduce CO₂ emissions is to change the fuel used for various reasons, including climate change and scarcity of resources. BEVs when compared to fuel vehicles, it is widely assumed that BEVs emit less CO₂ and have high energy efficiency (Dong, 2020; Magazzino et al., 2021) . Electric vehicles (EVs) utilize electrical power to supplant the current energy sources in the field of transportation to assist in minimizing CO₂ emissions and reducing air pollution (Xu et al., 2021) .

This section focuses on the study’s statistical analysis results. The secondary data for this study was obtained from Statista (2023) , which was used to demonstrate the variation in BEVs and the amount of CO₂ emissions reduction in Hungary between 2016 and 2022 compared to gasoline, diesel, HEVs, and PHEVs. Furthermore, the time series simple exponential smoothing (SES) method was used since it permitted the accurate prediction of the number of BEVs that will be achieved in 2030, as well as the calculation of the amount of CO₂ emissions that will be reduced based on the Gimbert (2022) tool.

According to the Gimbert tool, each passenger BEV emits 75 g/km of CO₂, each gasoline passenger vehicle emits 241 g/km; each diesel passenger vehicle emits 231 g/km; each HEV emits 190 g/km; each passenger PHEV emits 178 g/km of CO₂. The objective of this study is to predict the future numbers of battery electric vehicles (BEVs) and their corresponding CO₂ reduction in Hungary for the next seven years.

To accomplish this, the research utilized time series data sourced from Statista (2023) , spanning the years 2016 to 2022. The time series data was disintegrated into its three key components: trend, seasonal, and error. The SES technique was applied to model the time series data, accounting for both the trend and seasonal components. The smoothing constant, alpha, and the seasonal smoothing constant, gamma, were optimized to minimize the Mean Squared Error (MSE) of the model. Finally, the model was employed to predict the BEVs number and CO₂ reduction in g/km for the time period between 2023 and 2030. The SES formula was used in this process.

$F_t=\alpha A_{t-1}+\left(1-\alpha \right)F_{t-1}$

In this formula, F_t represents the forecasted value for the next period, A_t−1 represents the actual value for the previous period, and F_t−1 represents the forecasted value for the previous period.

The alpha (α) in the formula is the smoothing constant, which determines the weight given to the most recent observation versus the previous forecast. A higher alpha value gives more weight to the most recent observation, while a lower alpha value gives more weight to the previous forecast.

The goal is to find the value of alpha that minimizes the sum of squared errors between the predicted values and the actual values.

The formula to calculate the alpha value using the method of least squares is as follows:

$\alpha =1-\frac{\Sigma \left(Y_t-S_{t-1}\right)\ast \left(Y_t-S_t\right)}{\Sigma {\left(Y_t-S_{t-1}\right)}^2}$ (Brown, 1956)

where:

The number of periods (N) = 7.

The numerator of the formula;

$\begin{array}{l}\Sigma \left(Y_t-S_{t-1}\right)\ast \left(Y_t-S_t\right)\\ =\left(Y_1-S_0\right)\ast \left(Y_1-S_1\right)+\left(Y_2-S_1\right)\ast \left(Y_2-S_2\right)+\left(Y_3-S_2\right)\ast \left(Y_3-S_3\right)\\ +\left(Y_4-S_3\right)\ast \left(Y_4-S_4\right)+\left(Y_5-S_4\right)\ast \left(Y_5-S_5\right)+\left(Y_6-S_5\right)\ast \left(Y_6-S_6\right)\\ +\left(Y_7-S_6\right)\ast \left(Y_7-S_7\right)\\ =33187479\end{array}$

the denominator of the formula;

$\begin{array}{c}\Sigma {\left(Y_t-S_{t-1}\right)}^2={\left(Y_1-S_0\right)}^2+{\left(Y_2-S_1\right)}^2+{\left(Y_3-S_2\right)}^2+{\left(Y_4-S_3\right)}^2\\ +{\left(Y_5-S_4\right)}^2+{\left(Y_6-S_5\right)}^2+{\left(Y_7-S_6\right)}^2\\ =79779266\end{array}$

the alpha value α = 1 − (numerator/denominator);

$\text{alpha}=1-\frac{\text{numerator}}{\text{denominator}}$

$\alpha =1-\frac{33187479}{79779266}$

$\alpha =0.584$

Essentially, the formula is used to update the forecast for the next period based on the actual value from the previous period and the forecasted value from the previous period, with the smoothing constant determining the weight given to each component. This iterative process is repeated for each period in the time series, producing a forecast for each period based on the actual and forecasted values from the previous periods.

The transition to Battery Electric Vehicles (BEVs) presents a significant opportunity for Hungary to reduce carbon emissions and improve air quality within its transportation sector. Analyzing data up to 2022 and employing forecasting techniques, it is evident that BEVs are poised for substantial growth in the coming years. This growth trajectory underscores the importance of policy interventions aimed at promoting BEV adoption, particularly those focused on cost reduction.

As of 2022, BEVs accounted for a mere 0.4% of Hungary’s total registered passenger cars, with 18,800 BEVs among a total of 3.8 million registered vehicles. Despite their low penetration, BEVs have exhibited remarkable growth, increasing from a mere 405 in 2016. Simple Exponential Smoothing (SES) forecasts project a promising future, estimating that the number of BEVs in Hungary will reach 84,192 by 2030, signifying a substantial increase over the next eight years.

The Hungarian Ministry of Innovation and Technology’s strategy to reduce BEV costs could catalyze a transformative shift. If successful in increasing BEV penetration to 10%, it could translate to 463,600 BEVs by 2030. This intervention holds the potential to significantly mitigate carbon dioxide (CO₂) emissions, with an estimated reduction of 76,957,600 g/km compared to conventional gasoline, diesel, hybrid electric vehicles (HEVs), and plug-in hybrid vehicles (PHEVs).

The environmental benefits of BEVs are undeniable. Previous empirical studies have demonstrated that BEVs emit substantially lower levels of CO₂ compared to traditional fuel vehicles. With BEVs emitting only 75 g/km of CO₂, while gasoline and diesel vehicles emit 241 g/km and 231 g/km respectively, the potential for emissions reduction is profound. Furthermore, BEVs offer the potential to decrease harmful emissions, particularly in densely populated urban areas, thereby improving overall air quality and public health outcomes.

The findings underscore the critical role of BEVs in Hungary’s efforts to transition towards a sustainable and environmentally conscious transportation system. The projected growth of BEVs presents a significant opportunity for reducing CO₂ emissions and improving air quality. Policy interventions aimed at reducing the cost barriers associated with BEVs are essential to unlocking their full potential. By fostering an enabling environment for BEV adoption, Hungary can accelerate its progress towards achieving its climate and environmental sustainability goals while fostering innovation and economic growth in the automotive sector.

The assessment of data collected from stata.com has revealed that the number of battery-electric vehicles (BEVs) in Hungary has been on the rise, reaching 18,800 in 2022. This represents 0.4% of the total number of registered passenger cars in Hungary, which stands at 3.8 million, as reported by Statista (2023) . However, a time series forecast using the SES method has projected that the number of BEVs in Hungary will reach 84,192 by 2030, indicating a percentage increase of 2.21% over the next eight years. While this rate of increase is modest, it is a positive start.

Further progress can be made with the help of the Hungarian Ministry of Innovation and Technology, whose seventh pillar strategy aims to reduce the cost of BEVs to make them more affordable for people. If successful, this initiative could result in a 10% increase in the percentage of BEVs, bringing the number of BEVs in Hungary to 463,600. Such a development would have a significant impact on the environment, reducing up to 76,957,600 g/km of CO₂ compared to an equivalent number of other fuel-type vehicles.

If more serious efforts are made in Hungary to increase the adoption of BEVs, the number of BEVs could increase two, three, or even four times, assisting in controlling at least 25% of this social-environmental phenomenon that has a negative impact on the entire society.