Meta-analysis of life cycle assessments for Li-ion batteries production emissions

Abstract Purpose This paper investigates the environmental impact of Li-ion batteries by quantifying manufacturing-related emissions and analyzing how electricity mix and production scale affect emission intensity. Methods To this end, we conduct a meta-analysis of Life cycle assessments on Lithium-ion batteries published over the past two decades, categorizing them by year, battery chemistry, functional unit, system boundaries, and electricity mix. Through the meta-analysis, we highlight the differences in methodologies and outcomes associated with various functional unit choices and emphasize the need for a standardized approach to measuring emissions from battery manufacturing. We then carry out a cradle-to-gate assessment for a nickel manganese cobalt 811 battery with a silicon-coated graphite anode, analyzing how variations in the carbon intensity of the electricity mix affect emissions, with case studies for China, South Korea, and Sweden. Finally, we develop a set of regression models that link annual battery production and the carbon intensity of China’s electricity mix to the average mass-specific emissions observed each year. Results and discussion The meta-analysis shows a median global warming potential of 17.63 kg CO 2 -eq/kg of battery, with a standard deviation of 7.34. Our assessment results closely align: 17.33 kg CO 2 -eq/kg for China, 16.85 for South Korea, and 16.47 for Sweden. Differences in electricity mix mainly influence emissions from the energy-intensive cell production, particularly from cathode material processing. We used the data gathered in the meta-analysis to analyze and compare several regression models. We found that a multivariate linear regression using production volume and the carbon intensity of the Chinese electricity mix as predictors explains emissions with moderate accuracy ( R 2 = 0.6034). Conclusion The environmental impact of battery manufacturing can be reduced by using clean energy sources in production processes. However, achieving substantial reductions requires clean energy throughout the entire supply chain, as importing materials from regions with carbon-intensive electricity mixes can undermine these efforts. Our findings also highlight the emission-reducing effect of learning associated with increased production scale, supporting the integration of learning effects in future life cycle assessment models.