A Comparative Analysis of Autonomous Vehicle Lifecycle Emissions versus Traditional Vehicles: Assessing the Potential for Environmental Impact Reduction

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Priya Menon

Abstract

This paper presents a comparative analysis of the lifecycle emissions of autonomous vehicles (AVs) versus traditional vehicles (TVs), evaluating the potential for environmental impact reduction. As the transportation sector grapples with the dual challenges of greenhouse gas emissions and energy consumption, AVs offer a promising alternative to conventional vehicles. AVs, equipped with advanced technologies for automation, have the potential to optimize energy efficiency and reduce emissions. However, their lifecycle emissions, including production, operation, and disposal, must be thoroughly examined to understand their overall environmental impact. This paper analyzes various factors contributing to the emissions of both AVs and TVs, including manufacturing processes, energy sources, operational efficiency, and end-of-life management. The findings highlight that while AVs can offer significant reductions in operational emissions, their overall environmental impact is highly dependent on the energy sources used and the efficiency of their lifecycle management. The study underscores the importance of renewable energy integration, advanced materials, and recycling processes in maximizing the environmental benefits of AVs. By providing a comprehensive comparison, this paper aims to inform policymakers, industry stakeholders, and researchers on the pathways to achieving sustainable transportation through the adoption of Avs. 

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A Comparative Analysis of Autonomous Vehicle Lifecycle Emissions versus Traditional Vehicles: Assessing the Potential for Environmental Impact Reduction. (2023). International Journal of Machine Intelligence for Smart Applications, 13(12), 21-30. https://dljournals.com/index.php/IJMISA/article/view/12
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How to Cite

A Comparative Analysis of Autonomous Vehicle Lifecycle Emissions versus Traditional Vehicles: Assessing the Potential for Environmental Impact Reduction. (2023). International Journal of Machine Intelligence for Smart Applications, 13(12), 21-30. https://dljournals.com/index.php/IJMISA/article/view/12

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