AI-Enabled Renewable Optimization for Electric Vehicle Emissions in Urban Bengaluru

Electric vehicles (EVs) represent a cornerstone of sustainable urban mobility, yet their environmental benefits in coal-dependent grids like Bengaluru's require renewable energy integration and AI-driven efficiencies. This study employs a comprehensive Life Cycle Assessment (LCA) framework, augmented by machine learning scenario analysis, to quantify emission reductions from solar-powered charging and AI-optimized operations. Primary data from a stratified survey of 300 Bengaluru EV stakeholders, analysed through t-tests, multiple regression, and structural equation modelling (SEM), demonstrate 38% lower lifecycle GHG emissions and 15% reduced operational energy use. Environmental consciousness significantly predicts willingness-to-pay (WTP), moderated by infrastructure and cost barriers. These results advocate targeted policies for AI-renewable ecosystems, positioning Bengaluru as a model for India's net-zero urban transport ambitions by 2035.