A Framework for Evaluating the Economic Performance of Recycling Systems: A Case Study of North American Electronics Recycling Systems
Bibliographic record
Abstract
A framework for evaluating the economic performance of a recycling system is proposed, and data from four electronics recycling systems in North America (Alberta, California, Maine, and Maryland) that use different operating models are used as a preliminary test of the framework. The framework is built around a hierarchy of descriptors that clarify the function of the system components under consideration and the activities, cash flow elements, and resources within those functions; costs are incurred by specific stakeholders. Data from each system on fee and mass collection amounts and collection, processing, and management costs are used to create a matrix of several net costs for stakeholders within each system. Although all four systems are relatively new, thereby making data collection a challenge, some preliminary insights can be gleaned from comparing the systems. Processing costs vary significantly in the four systems, with Alberta and California having the highest reimbursement rates for processing. Alberta and California also have relatively high system management costs, but processors are generally quite satisfied with the systems. Maine has an additional cost for consolidation that is an implicit management cost because of the need to count incoming products by manufacturer.
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 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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".