Harnessing green innovation and artificial intelligence: Assessing the impact of renewable energy integration and AI-driven optimization on the life cycle emissions of electric vehicles in urban Bengaluru

Green innovation and artificial intelligence (AI) are revolutionizing the electric vehicle (EV) landscape, especially in rapidly urbanizing cities like Bengaluru. This paper investigates the synergistic impact of renewable energy and AI technologies on reducing the environmental footprint of electric vehicles in an urban Indian context. The adoption of electric vehicles (EVs) in rapidly urbanizing cities like Bengaluru offers a promising pathway towards sustainable transportation and reduced greenhouse gas (GHG) emissions. However, maximizing environmental benefits depends on integrating renewable energy for EV charging and leveraging artificial intelligence (AI) for operational efficiency. This study examines the combined impact of renewable energy integration and AI-driven optimization on the life cycle emissions of EVs in Bengaluru. A Life Cycle Assessment (LCA) framework enhanced with AI-based scenario modelling was used to compare greenhouse gas emissions from conventional grid-charged EVs against renewable-powered, AI-optimized counterparts. Data were collected via a survey of 300 EV owners and potential users, incorporating awareness, perceptions, and willingness to pay for these green technologies. Statistical analyses employing t-tests, regression, and structural equation modelling (SEM) revealed significant emission reductions from renewable-powered charging (38% lower GHG emissions) and substantial efficiency gains from AI optimization (15% reduction in operational energy consumption). Environmental awareness positively influenced willingness to pay for green AI-enabled EV services, whereas perceived financial and infrastructure barriers impeded adoption. The findings underscore the transformative potential of combining green innovation and AI in advancing sustainable urban mobility and offer policy recommendations for accelerating adoption.