Optimization of the end‐of‐life tire repartition within the European treatment system to minimize its environmental impacts
Bibliographic record
Abstract
Abstract This study contrasts two different approaches to inform European‐scale decision‐making to mitigate the environmental impacts of the end‐of‐life tires (ELT) management system. The first analysis is a traditional life cycle assessment (LCA) that compares the environmental performances of the 12 main available European end‐of‐life (EOL) technologies in ELT processing while restricting the boundaries to the EOL stage. The second analysis has a broader scope, addressing the optimization of the ELT distribution within the 12 considered pathways to minimize the environmental impacts of the total tire use in Europe under present capacity and constraints. The results of the traditional LCA show that, except for landfill, all the tested EOL routes present environmental benefits. Material recovery pathways bring the most environmental credits, whereas civil engineering pathways are the least promising. The LCA results that emerged from the optimization of ELT management technologies yield two optimal technological mixes that maximize the quantity of ELT recycled in molded objects production: such results represent a hypothetical case with no constraints. When considering constraints, that is, limitations on maximum quantities of ELT that can undergo retreading, pyrolysis, or recycling in synthetic turfs, in molded objects and in production, the number of optimal technology mixes increases to five. The type of technologies favored depends on the minimized impact categories (climate change, fossil and nuclear energy use, human health, and ecosystem quality). A comparison between constrained and unconstrained scenarios shows that achieving the best environmental performances is conditional to the accessibility of the EOL technologies as well as their individual environmental impacts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".