Defining the Legal and Policy Framework to Stop the Dumping of Environmentally Harmful Products
Bibliographic record
Abstract
Environmental dumping is a practice historically associated with the export of hazardous product waste from a developed country for irresponsible and often illegal disposal in a developing country. Now, with the industrialization and globalization of China and other developing countries, environmental dumping can involve both developing and developed countries as origin and destination. This dumping can be especially harmful to attempts to control under the Montreal Protocol ozone-depleting and climate-forcing chemical substances and/or products requiring unnecessarily high energy consumption. While developing country Parties to the Montreal Protocol are allowed to delay their phasedown of climate-forcing and ozone-depleting hydrofluorocarbons (HFCs) during a multi-year grace period, there are advantages to earlier implementation when superior alternatives are already available at reasonable costs, as is the case for many uses of HFCs today. Thus, developing countries can benefit under the Protocol from setting controls for environmental dumping. This article aims to give policymakers, especially those in developing countries, a legal and policy “toolkit” that can be used to stop unwanted environmental dumping. It includes an examination of the history of environmental dumping, illustration of such dumping in practice, a detailed explanation and examination of the legal and policy tools, and a summary of the consequences of environmental dumping.
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 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 | 0.039 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.009 | 0.035 |
| Scholarly communication | 0.015 | 0.015 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.030 | 0.022 |
| Insufficient payload (model declined to judge) | 0.004 | 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".