## 5 Conclusions  
This study presents a data-driven approach to modeling and forecasting electric vehicle (EV) adoption in the UK using a hybrid framework that integrates SARIMAX, Prophet, and XGBoost. Each model contributes a distinct analytical lens: SARIMAX captures autoregressive patterns and exogenous influence in a structured, time-aware format; Prophet excels at long-term trend forecasting with interpretable growth dynamics; and XGBoost reveals the relative influence of various explanatory factors in a flexible, nonparametric way.  
All three models were trained on a 10-year dataset spanning 2015 to 2024, which reflects the modern period of rapid EV adoption. Despite the limited time span, model performance was strong: SARIMAX achieved an in-sample R\({}^{2}\) of 0.98, Prophet reached 0.99, and XGBoost--used diagnostically--achieved a perfect R\({}^{2}\) of 1.00. The strength of the predictions stems from the clear exponential growth in EV sales and the well-aligned selection of regressors, including charger density, battery cost, GDP growth, and a policy dummy variable. The SARIMAX coefficient estimates and Prophet's average regressor weights further validate the directionality and significance of these drivers, while XGBoost confirmed charger density and year as the most important features.  
Forecasts under baseline conditions project that EV sales could reach 2.8 million units annually by 2035. However, scenario-based modeling reveals a critical insight: the long-term forecast is highly sensitive to infrastructure deployment and battery pricing. In an optimistic scenario, UK EV sales could exceed 3.1 million units, while under a pessimistic scenario--characterized by policy delays and slower cost reductions--annual sales may fall short by over 500,000 units. These projections reinforce the importance of timely government intervention, infrastructure investment, and economic stability in accelerating the EV transition.  
Despite its success, the analysis is constrained by the relatively short historical time series. EV adoption before 2015 was negligible, limiting the feasibility of using longer training windows. This also influenced the SARIMAX specification, which prioritized parsimony (AR(1), no MA term) and omitted the intercept to reduce overfitting in small samples. Nonetheless, model selection was rigorously supported by error metrics (MAPE, RMSE, R\({}^{2}\)), diagnostic plots, and comparative parameter analysis.  
Overall, the integrated modeling approach provides a practical roadmap for forecasting EV growth while also offering insights into key market drivers. Future work could extend the forecasting horizon using synthetic scenarios, explore regional disaggregation within the UK, or integrate additional behavioral and technological variables. The findings provide a valuable evidence base for policymakers aiming to shape the UK's sustainable transport future.  
Conceptualization, All authors.; methodology, S.V.; software, S.V. and M.M.; validation, S.V. and M.M.; formal analysis, S.V. and A.S.; investigation, S.V. and A.S.; data curation, S.V. and P.E.; writing--original draft preparation, S.V. and M.M.; writing--review and editing, S.V. and M.M.; visualization, S.V.; supervision, S.V. and M.M.; All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The data used in this research has been obtained from the resources that are mentioned in the references.  
**Conflicts of Interest:** Author [PERSON] was employed by the company Irsa San'at Shahr No Afarin, author [PERSON] was employed by the company Centre for Factories of the Future (C4 FF). The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.