Environmental Trade-Offs of Downcycling in Circular Economy: Combining Life Cycle Assessment and Material Circularity Indicator to Inform Circularity Strategies for Alkaline Batteries
Bibliographic record
Abstract
The application of circularity strategies to improve resource use and recovery should be considered with their potential impacts on the environment. Their effectiveness could be evaluated by combining the material circularity indicator (MCI) and life cycle assessment (LCA) methods. Environmental trade-offs may be underestimated for some strategies given that the loss of material quality with recycling has not been captured within the methodological framework of MCI. The current study demonstrates how significantly this limitation may influence the trade-offs in a case study. The methods are applied to several scenarios for the circularity improvement of alkaline batteries. The joint interpretation of MCI and LCA scores is carried out using waterfall charts and normalized indicator scores. Results suggest that improving circularity generally reduces environmental impacts, although there is large variability among two sets of values. For example, an increase of MCI score by 14% for two recycling scenarios translates to a small reduction of impacts in one case (0.06–1.64%) and a large reduction in another (9.84–56.82%). Observations from the case study are used to discuss the design and scope of MCI use and its combining with LCA. Lastly, we draw on the opportunities of the new comparative approach.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| 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".