Sustainable Market Forecasting of Electric Vehicle Adoption Using Machine Learning Techniques in Emerging Economies
DOI:
https://doi.org/10.54536/ajsts.v5i2.8655Keywords:
Electric Vehicle Adoption, Emerging Economies, EV Market Transition, Machine Learning, Market Forecasting, Predictive Analytics, Sustainable TransportationAbstract
The rapid transition toward sustainable transportation has increased the importance of electric vehicle (EV) adoption forecasting, particularly in emerging economies where market growth is influenced by economic conditions, charging infrastructure availability, vehicle costs, and policy environments. The scale of EV market penetration, however, is not a straightforward prediction owing to the fact that it can differ based on adoption rates, infrastructure deployment, and socioeconomic conditions in individual countries. This study proposes a machine learning tool to develop a sustainable market forecasting model to forecast adoption of EVs in emerging economies based on past market, economic, infrastructure and policy indicators. An EV adoption dataset was used, which consists of EV sales share, charging infrastructure, economic indicators, vehicle price data, and policy features across 26 emerging economies spanning 2010 to 2025. A set of forecasting models, such as Linear Regression, Ridge Regression, XGBoost, Gradient Boosting and an ensemble approach, was tested with an unseen test period from 2025, employing a leakage aware time-series validation strategy. The ensemble forecasting model presented here proved to have a high level of predictive skill with R², RMSE and MAE values of 0.9010, 5.0773 and 3.7198, respectively. The historical trends of EV adoption, development of charging infrastructure, economic factors, and policy considerations were identified as primary drivers of future EV market growth through feature analysis. The results illustrate how machine learning-based forecasting can help offer decision-support information to policymakers, industry, and sustainable transportation planners. The suggested model is a step towards more data-informed EV transition planning, as it facilitates accurate market prediction and assists in sustainable mobility planning in new economies.
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Copyright (c) 2026 Md Anas Biswas, Mahbube Rabbani, Safia Farzana Nishi, Ashutosh Roy, Md Samir Murad
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