Modeling and Analysis of Carbon Emissions Throughout Lifecycle of Electric Vehicles Considering Dynamic Carbon Emission Factors

Yanhong Xiao, Bin Qian, Houpeng Hu, Mi Zhou, Zerui Chen, Xiaoming Lin, Peilin He and Jianlin Tang ()
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Yanhong Xiao: Guizhou Power Grid Co., Ltd., Guiyang 550002, China
Bin Qian: CSG Electric Power Research Institute, Guangzhou 510663, China
Houpeng Hu: Guizhou Power Grid Co., Ltd., Guiyang 550002, China
Mi Zhou: CSG Electric Power Research Institute, Guangzhou 510663, China
Zerui Chen: Guizhou Power Grid Co., Ltd., Guiyang 550002, China
Xiaoming Lin: CSG Electric Power Research Institute, Guangzhou 510663, China
Peilin He: Guizhou Power Grid Co., Ltd., Guiyang 550002, China
Jianlin Tang: CSG Electric Power Research Institute, Guangzhou 510663, China

Sustainability, 2025, vol. 17, issue 14, 1-20

Abstract: Amidst the global strategic transition towards low-carbon energy systems, electric vehicles (EVs) are pivotal for achieving deep decarbonization within the transportation sector. Consequently, enhancing the scientific rigor and precision of their life-cycle carbon footprint assessments is of paramount importance. Addressing the limitations of existing research, notably ambiguous assessment boundaries and the omission of dynamic coupling characteristics, this study develops a dynamic regional-level life-cycle carbon footprint assessment model for EVs that incorporates time-variant carbon emission factors. The methodology first delineates system boundaries based on established life-cycle assessment (LCA) principles, establishing a comprehensive analytical framework encompassing power battery production, vehicle manufacturing, operational use, and end-of-life recycling. Subsequently, inventory analysis is employed to model carbon emissions during the production and recycling phases. Crucially, for the operational phase, we introduce a novel source–load synergistic optimization approach integrating dynamic carbon intensity tracking. This is achieved by formulating a low-carbon dispatch model that accounts for power grid security constraints and the spatiotemporal distribution of EVs, thereby enabling the calculation of dynamic nodal carbon intensities and consequential EV emissions. Finally, data from these distinct stages are integrated to construct a holistic life-cycle carbon accounting system. Our results, based on a typical regional grid scenario, reveal that indirect carbon emissions during the operational phase contribute 75.1% of the total life-cycle emissions, substantially outweighing contributions from production (23.4%) and recycling (1.5%). This underscores the significant carbon mitigation leverage of the use phase and validates the efficacy of our dynamic carbon intensity model in improving the accuracy of regional-level EV carbon accounting.

Keywords: electric vehicles; life-cycle assessment; carbon emissions; optimal power flow; dynamic carbon emission factors (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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