CO₂ Emission Optimization in Internet of Vehicles-Enabled Smart Transportation Systems for Sustainable Smart Cities

Transport is a major contributor to global carbon dioxide (CO₂) emissions due to traffic congestion, urbanisation, and increasing vehicle density. Sustainable smart transportation is therefore essential for reducing environmental impact while maintaining efficient mobility. The Internet of Vehicles (IoV) enables real-time communication among vehicles, roadside units, and traffic management systems, providing opportunities for intelligent traffic management, emission reduction, and green mobility. Conventional navigation and traffic management systems primarily optimise travel time and distance without explicitly considering CO₂ emissions and operational cost. Stop-and-go traffic, excessive idling, and inefficient route selection increase fuel consumption and emissions. Therefore, an intelligent transportation framework is required to jointly optimise environmental impact, mobility efficiency, and system cost. The proposed framework uses IoV-enabled vehicles to continuously transmit vehicle speed, fuel consumption, traffic density, and driving-condition information to a central processing server. A hybrid Long Short-Term Memory (LSTM) and attention-based model predicts traffic conditions and congestion levels. A multi-objective optimisation framework then dynamically selects environmentally efficient routes by jointly considering CO₂ emissions, fuel consumption, travel time, and operational cost. The performance of the proposed IoV-enabled approach is evaluated against conventional routing without IoV. The simulation results demonstrate that the IoV-enabled framework reduces CO₂ emissions by more than 30% compared with conventional routing without IoV. It also achieves approximately 25% lower fuel consumption and more than 20% reduction in average travel time. Although IoV introduces additional communication and computational overhead, the overall operational cost remains controlled, while the environmental and mobility benefits provide a favourable cost–performance trade-off. Predictive traffic management further improves traffic distribution and mitigates major congestion corridors. This study presents an intelligent IoV-based framework for CO₂ emission optimisation in smart city transportation. By integrating real-time vehicular communication, traffic prediction, and multi-objective optimisation, the framework achieves substantial reductions in CO₂ emissions, fuel consumption, and travel time while considering operational cost. The results demonstrate the potential of IoV technology to support cost-effective, environmentally sustainable, and efficient green transportation in smart cities.