Quantifying Domestic Used Electronics Flows using a Combination of Material Flow Methodologies: A US Case Study
Bibliographic record
Abstract
This paper describes the scope, methods, data, and results of a comprehensive quantitative analysis of generation, stock, and collection of used computers and monitors in the United States , specifically desktops, laptops, CRT monitors, and flat panel monitors in the decade leading up to 2010. Generation refers to used electronics coming directly out of use or postuse storage destined for disposal or collection, which encompasses a variety of organizations gathering used electronics for recycling or reuse. Given the lack of actual statistics on flows of used electronics, two separate approaches, the sales obsolescence method (SOM) and the survey scale-up method (SSUM), were used in order to compare the results attained and provide a range for estimated quantities. This study intentionally sought to capture the uncertainty in the estimates. To do so, uncertainty in each data set was incorporated at each stage using Monte Carlo simulations for SOM and establishing scenarios for SSUM. Considering the average results across both methods, we estimate that in 2010 the U.S. generated 130-164 thousand metric tons of used computers and 128-153 thousand tons of used monitors, of which 110-116 thousand tons of used computers and 105-106 thousand tons of used monitors were collected for further reuse, recycling, or export. While each approach has its strengths and weaknesses, both the SOM and the SSUM appear to be capable of producing reasonable ranges of estimates for the generation and collection of used electronics.
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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 | 0.002 | 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 | 0.000 | 0.002 |
| 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".