Economic and Environmental Viability of Lithium-Ion Battery Recycling—Case Study in Two Canadian Regions with Different Energy Mixes
Bibliographic record
Abstract
Lithium-ion battery (LIB) pack is the core component of electric vehicles (EVs). As the demand is continuously increasing, it puts a lot of strain on the battery raw material supply chains. Likewise, the large quantity of spent LIBs from different sources will add to the complexity of end-of-life (EoL) management. Battery recycling processing is a potential source of critical cathode precursor materials as an alternative to virgin raw material sourcing. Indeed, metal sulfates (nickel, cobalt, and manganese) and lithium carbonate could be recovered through EoL processing. This study aims to provide an economic and environmental life cycle sustainability assessment of recycled battery materials. This assessment is based on a bottom-up approach considering geographical boundaries and process data inputs. The two sources of critical cathode battery materials, virgin and recycled battery materials, are compared based on economic and environmental indicators. This study identified the province of Quebec in Canada as the geographical boundary where several battery processing plants have been recently announced. The best available recycling process (hydrometallurgy) was selected. For the virgin materials, this study considers the option of importing from other jurisdictions by using global average supply chain values. Furthermore, a comparison of alternative supply chain configurations was performed using a spatially differentiated approach. The main findings of this study are as follows: (i) the environmental credit of recycled cathode active materials (CAMs) is estimated as −6.46 kg CO2e/kg CAM, and (ii) the overall cost and environmental impacts of producing LIB cathode active material from recycled battery materials can be 48% and 54% lower than production from virgin materials, respectively, considering the upstream, midstream, and downstream stages of the CAM supply chain. The main drivers for the reduction in these financial costs and emissions are the local transportation and the hydrometallurgical process. The assessment results provide insights to support the development of appropriate policies and R&D solutions adapted to local considerations as well as offer additional possibilities to improve the design of sustainable supply chains for LIB recycling.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".