The Social Cost of Informal Electronic Waste Processing in Southern China
Bibliographic record
Abstract
Large amounts of e-waste are processed “informally” in lower income countries. Such processing releases dangerous pollutants, which increase mortality and reduce cognitive functioning. This paper estimates the social cost of informal e-waste processing in Southern China. This parameter may be “plugged-in” to cost-benefit analyses that evaluate the economic efficiency of policies to reduce informal e-waste processing in China or other lower income countries. It may also be used in the estimation of the social benefits (or costs) of new or proposed e-waste processing policies in higher income countries. We estimate that the social cost of informal e-waste processing in Guiyu is about $529 million. This amount is equivalent to about $423 per tonne (in 2018 US dollars) or $3528 per person, which is over half the gross income per capita of the residents of Guiyu. We also perform sensitivity analysis that varies the estimated mortality outcomes, the value of a statistical life and the amount of e-waste processed.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".