The decarbonization of transportation and environmental quality enhancement have become more and more reliant on eco-innovation,which incorporates both technological change and systemic coordination and governance.The...The decarbonization of transportation and environmental quality enhancement have become more and more reliant on eco-innovation,which incorporates both technological change and systemic coordination and governance.The review is a summary of the evidence that can be translated into environmental sustainability outcomes on how smart vehicle technologies,including electrified powertrains and vehicle-grid interfaces,connected and cooperative systems(Vehicleto-Everything,V2X),automation and advanced automation,and Artificial Intelligence(AI)-enabled optimization can be transformed.Using a structured analytical framework linking technology capability to eco-innovation mechanisms and sustainability impacts,we reconcile findings across operational,well-to-wheel,and life-cycle boundaries.The literature indicates that electrification delivers strong local air-quality benefits and,in most contexts,substantial climate gains,but net outcomes depend on grid carbon intensity,charging time profiles,battery production,and end-of-life pathways,making managed charging and circularity pivotal complements.Connectivity and cooperative control improve energy efficiency primarily through coordination effects such as traffic smoothing,eco-routing,and platooning,yet benefits are non-linear and sensitive to penetration rates and infrastructure interoperability.Automation offers efficiency and safety co-benefits but exhibits the widest uncertainty because induced demand,empty travel,and mode substitution can offset per-vehicle improvements.AI-driven fleet optimization can reduce empty miles and extend component life,although computational and hardware overhead and rapid obsolescence can introduce trade-offs.We identify persistent gaps in comparability,non-exhaust emissions assessment,causal evaluation at scale,and equity-aware impact metrics,and propose a research and policy agenda emphasizing integrated Life Cycle Assessment(LCA)system modeling,standardized reporting,interoperable data governance,and demand management to secure durable environmental gains.展开更多
文摘The decarbonization of transportation and environmental quality enhancement have become more and more reliant on eco-innovation,which incorporates both technological change and systemic coordination and governance.The review is a summary of the evidence that can be translated into environmental sustainability outcomes on how smart vehicle technologies,including electrified powertrains and vehicle-grid interfaces,connected and cooperative systems(Vehicleto-Everything,V2X),automation and advanced automation,and Artificial Intelligence(AI)-enabled optimization can be transformed.Using a structured analytical framework linking technology capability to eco-innovation mechanisms and sustainability impacts,we reconcile findings across operational,well-to-wheel,and life-cycle boundaries.The literature indicates that electrification delivers strong local air-quality benefits and,in most contexts,substantial climate gains,but net outcomes depend on grid carbon intensity,charging time profiles,battery production,and end-of-life pathways,making managed charging and circularity pivotal complements.Connectivity and cooperative control improve energy efficiency primarily through coordination effects such as traffic smoothing,eco-routing,and platooning,yet benefits are non-linear and sensitive to penetration rates and infrastructure interoperability.Automation offers efficiency and safety co-benefits but exhibits the widest uncertainty because induced demand,empty travel,and mode substitution can offset per-vehicle improvements.AI-driven fleet optimization can reduce empty miles and extend component life,although computational and hardware overhead and rapid obsolescence can introduce trade-offs.We identify persistent gaps in comparability,non-exhaust emissions assessment,causal evaluation at scale,and equity-aware impact metrics,and propose a research and policy agenda emphasizing integrated Life Cycle Assessment(LCA)system modeling,standardized reporting,interoperable data governance,and demand management to secure durable environmental gains.
