Trade Measures for Regulating Transboundary Movement of Electronic Waste
Bibliographic record
Abstract
International trade in used electrical and electronics equipment (UEEE) provides an avenue for socio-economic development in the developing world and also serves as a conduit for transboundary dumping of waste electrical and electronic equipment (WEEE) also referred to as electronic waste or e-waste. The latter problem arises from the absence of a regulatory framework for differentiating between functional UEEE and junk e-waste. This has resulted in both functional UEEE and junk e-waste being concurrently shipped to developing countries under the guise of international trade in used electronics. Dealing with these problems will require effective regulation of international trade in UEEE from both exporting and importing countries. Although, the export of e-waste from the European Community to developing countries is currently prohibited, significant amount of e-waste from the region continue to flow into developing countries due to lax regulatory measures in the latter. Hence, there is need for a regulatory regime in developing countries to complement the prohibitory regime in the major e-waste source countries. This paper proposes trade measures modelled in line with WTO rules which could be adopted by developing countries in addressing these problems. The proposed measures include the development of a compulsory certification and labelling system for functional UEEE as well as trade ban on commercial importation of UEEE not complying with the said certification and labelling system. The paper then goes further to examine these proposed measures in the light of WTO rules and jurisprudence.
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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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".