Tracking the Global Generation and Exports of e-Waste. Do Existing Estimates Add up?
Bibliographic record
Abstract
The transport of discarded electronic and electrical appliances (e-waste) to developing regions has received considerable attention, but it is difficult to assess the significance of this issue without a quantitative understanding of the amounts involved. The main objective of this study is to track the global transport of e-wastes by compiling and constraining existing estimates of the amount of e-waste generated domestically in each country MGEN, exported from countries belonging to the Organization for Economic Cooperation and Development (OECD) MEXP, and imported in countries outside of the OECD MIMP. Reference year is 2005 and all estimates are given with an uncertainty range. Estimates of MGEN obtained by apportioning a global total of ∼ 35,000 kt (range 20,000-50,000 kt) based on a nation's gross domestic product agree well with independent estimates of MGEN for individual countries. Import estimates MIMP to the countries believed to be the major recipients of e-waste exports from the OECD globally (China, India, and five West African countries) suggests that ∼ 5,000 kt (3,600 kt-7,300 kt) may have been imported annually to these non-OECD countries alone, which represents ∼ 23% (17%-34%) of the amounts of e-waste generated domestically within the OECD. MEXP for each OECD country is then estimated by applying this fraction of 23% to its MGEN. By allocating each country's MGEN, MIMP, MEXP and MNET = MGEN + MIMP - MEXP, we can map the global generation and flows of e-waste from OECD to non-OECD countries. While significant uncertainties remain, we note that estimated import into seven non-OECD countries alone are often at the higher end of estimates of exports from OECD countries.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.009 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".