Bibliographic record
Abstract
We investigate how recycling can be a strategic source of supply in the presence of a changing supply market. This research is inspired by the metal cutting tools industry, where challenges regarding a key raw material present an opportunity for the manufacturers to create an alternative supply source by recycling. In this study, there is a virgin material market that supplies two manufacturers differentiated in their recycling ability. The problem is formulated as a game, where the manufacturers first make a decision to recycle or not, and then decide on their respective production quantities, and recycling rates. Depending on the fixed recycling cost relative to the unit cost of the virgin material, as well as the recycling cost structure of the two manufacturers, there are four possible equilibria: both manufacturers recycle, neither manufacturer recycles, only the more recycling‐capable manufacturer recycles, or a scenario with two Nash equilibria (either manufacturer recycles whereas the other does not). We show that recycling is indeed a strategic supply source resulting in higher quantities and profits. Interestingly, a manufacturer may recycle less if the unit cost of the virgin material increases, at high recycling rates. This result emphasizes the importance of carefully modeling the recycling cost structure. Although a recycled unit has necessarily a lower life‐cycle environmental impact than a unit made of virgin materials, the industry‐wide environmental impact can be higher in a recycling scenario due to higher production quantities overall. Welfare, however, is higher with recycling.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.001 |
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".