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The investigation of machine learning models in Europe and India for predicting future sales of electric cars (EVs) worldwide has been the focus of present research. In this study, Long Short-Term Memory (LSTM), Support Vector Regression (SVR), Autoregressive Integrated Moving Average (ARIMA), Linear Regression, hybrid long- and short-term memory neural network model and gradient boosted regression trees model (LSTM-GBRT) models based machine learning techniques are compared for performance analysis to predict EV sales. Moreover, in this study, a hybrid LSTM-GBRT technique was developed, which forecasts EV sales, particularly for Battery Electric Vehicles (BEVs) and Plug-in Hybrid Electric Vehicles (PHEVs) over the next five years. During the prediction of EV sales, it has been validated that hybrid LSTM-GBRT technique has better predictive power. In addition, the present study attempts to provide estimated sales statistics for the designated locations. The results indicate that EV industry is evolving, with India showing a strong growth rate in BEV sales and establishing itself as a major participant in the global EV scenario by showing a notable increase in its share of worldwide EV sales volume by 2027. Conversely, the performance of EV sales in Europe has been analysed and validated through the proposed LSTM-GBRT model.

Electric vehicles are vehicles that do not emit harmful emissions to the environment and operate on electrical energy. With developing technology and increasing awareness, electric vehicles are rapidly increasing their popularity as environmentally friendly transportation alternatives. Furthermore, electric vehicle sales in Türkiye, are increasing rapidly. This study aims to examine the relationship between electric vehicle sales and carbon emissions and energy consumption using data from January 2019 to November 2022. The study first started with the Fourier-ADF unit root test. In the next stage, Fourier-Granger and Fourier-Toda-Yamamoto causality tests were calculated according to the stationarity levels in the phenomenon. According to the results, it was determined that there is a bidirectional causality relationship between electricity consumption and electric vehicle sales. After the causality tests were completed, the cointegration test was started. Fourier ARDL cointegration test was used to determine the cointegration relationship between carbon emissions and electric vehicle sales. Fourier ADL cointegration tests were applied for electric vehicle sales and electricity consumption, which show the same level of stationarity. According to the results, a cointegration relationship was found between electric vehicle sales and electricity consumption. In line with these results, FMOLS Fourier Cointegration coefficient estimate was calculated. Therefore, future studies can conduct a comparative analysis of the environmental impacts of electric vehicle markets in different geographical regions. [ABSTRACT FROM AUTHOR]

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