The Optimal Level of Automation for Carbon-efficient Autonomous Vehicles

Jincheng Yang

doi:10.54254/2755-2721/2026.MH29409

Applied and Computational EngineeringOpen access

The Optimal Level of Automation for Carbon-efficient Autonomous Vehicles

Research Article

Open Access

The Optimal Level of Automation for Carbon-efficient Autonomous Vehicles

Jincheng Yang ^1*

¹ Duke Kunshan University

^*Corresponding author: Jincheng.yang@dukekunshan.edu.cn

Published on 11 November 2025

ACE Vol.205

ISSN (Print): 2755-273X

ISSN (Online): 2755-2721

ISBN (Print): 978-1-80590-521-9

ISBN (Online): 978-1-80590-522-6

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Abstract

Battery electric vehicles offer zero emissions and higher efficiency.This paper presented a cradle-to-grave LCA on SAE Level 0–5 automated BEVs and found the optimal automation level to reduce carbon was, surprisingly, only at SAE Level 4. The environmental costs of sensor and computing hardware were combined with operational efficiency plus/minus factors, using region-specific electrical grid emission factors for North America (0.403 kg CO2/kWh), Europe (0.238 kg CO2/kWh), and China (0.556 kg CO2/kWh). The mass of hardware inventory increased from 0.015 kg (Level 1) to 8.32 kg (Level 5), and operational performance improvements expected varied between 5% and 15% per level. The findings implied that SAE Level 1 attained the largest environmental gains in all regional environments, with CO2savings of between 1.6% and 5.0% relative to manual driving, due to its low-cost hardware needs and moderate efficiency benefits. Level 3 succeeded only—and no better than—under low-carbon electricity (≤0.30 kg CO2/kWh), while Levels 4–5 resulted in larger and increasingly significant GHG emissions per lifecycle associated with hardware scale, which were not offset by operational gains. The results challenged the narrative of the climate benefits of automation and suggested that reductions in emissions from the transportation sector depended less on new technology and more on environmental setting. The paper was the first to systematically analyze potential environmental impacts of AV deployments, thereby enabling policymakers to specialize automation investments toward regional decarbonization targets and operational conditions for desirable sustainable mobility transitions.

Keywords:

Electric Vehicles, Automation Levels, Life-Cycle Assessment

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[1]. Nasser, N. and Fazeli, M. (2020) Buffered-Microgrid Structure for Future Power Networks; A Seamless Microgrid Control. IEEE Transactions on Smart Grid, 12, 131-140.

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Cite this article

Yang,J. (2025). The Optimal Level of Automation for Carbon-efficient Autonomous Vehicles. Applied and Computational Engineering,205,39-51.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

About volume

Volume title: Proceedings of CONF-MCEE 2026 Symposium: Geomaterials and Environmental Engineering

ISBN: 978-1-80590-521-9(Print) / 978-1-80590-522-6(Online)

Editor: Ömer Burak İSTANBULLU, Manoj Khandelwal

Conference website: https://www.confmcee.org/mounthelen.html

Conference date: 21 January 2026

Series: Applied and Computational Engineering

Volume number: Vol.205

ISSN: 2755-2721(Print) / 2755-273X(Online)

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