Abstract
Purpose
Several studies using life cycle assessment (LCA) have highlighted nuclear electricity’s possible role as a low carbon-emitting electricity source. But the variability of results has also been questioned by several published LCA reviews, the latest identified dating back from 2016. This article aims at assessing whether new developments and knowledge confirm this statement.
Methods
Meta-analysis is a systematic review approach that allows to assess this variability. It was applied in this study to measure and understand the dynamics behind the greenhouse gases (GHG) emissions of nuclear electricity in a life cycle perspective. From 114 publications identified since 2012, 22 primary studies were selected and analysed to provide a meta-database of 63 estimations of greenhouse gases (GHG) per kWh generated. A descriptive analysis of the meta-database provided a status of the art on the topic in terms of approaches adopted, data sources, etc. Additional data exploitation using boxplot graphs was performed to assess the dispersion and variability of the results around these figures depending on several factors such as extraction mining technique and energy demand, enrichment technology used, reactor’s size, and type of LCA practitioners.
Results and discussion
The life cycle GHG emissions of nuclear electricity found with the meta-analysis were 3.09 g CO2 eq./kWh (min), 6.36 g CO2 eq./kWh (median), 12.4 g CO2 eq./kWh (average excluding extrema), and 43.2 g CO2 eq./kWh (max), although extremum values were also identified at 53.4, 60.0, and one outlier, based on theoretical scenarios. Using principal component analysis (PCA), the two most influential variables of the environmental performance of nuclear electricity were identified: GHG emissions intensity of the electricity consumed during the enrichment of uranium and energy demand for the extraction of uranium ore.
Conclusions
Finally, the contributions of this meta-analysis to current knowledge on the GHG emissions intensity of nuclear electricity generation systems were discussed, including life cycle step breakdown, data gaps, limits, and uncertainties associated to the back end and reactor activities. Among the main areas for improvement for future LCA studies, the study helped identify a need for consolidated industrial data along with harmonised practices regarding system boundary definition.
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Data availability
The authors declare that the findings of this study are available within the paper. Source data (primary studies) used to elaborate the meta-analysis are listed and described within the paper's supplementary information files.
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The authors wish to thank all lead authors and specialists that replied to request for clarifications on the primary studies as well as the reviewers for their inputs and suggestions for recommendations.
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Denis Le Boulch and Mickael Buronfosse developed the research idea and created the team; Pierre-Alexis Duvernois and Yannick Le Guern defined the scope of the meta-analysis. Pierre-Alexis Duvernois, with the help of Yannick Le Guern and colleagues Maxime Pousse and Frederic Croison, built the meta-database and performed the analysis; Denis Le Boulch conceived the approach and structure of this paper; Pierre-Alexis Duvernois and Yannick Le Guern developed the manuscript with the help and guidance of Denis Le Boulch and Noëmie Payen.
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Le Boulch, D., Buronfosse, M., Le Guern, Y. et al. Meta-analysis of the greenhouse gases emissions of nuclear electricity generation: learnings for process-based LCA. Int J Life Cycle Assess 29, 857–872 (2024). https://doi.org/10.1007/s11367-024-02293-y
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DOI: https://doi.org/10.1007/s11367-024-02293-y