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Record W2910258192

Defining the Legal and Policy Framework to Stop the Dumping of Environmentally Harmful Products

2018· article· en· W2910258192 on OpenAlexaboutno aff
Stephen O. Andersen, Richard Ferris, Romina Picolotti, Durwood Zaelke, Suely M. Machado Carvalho, Marco Gonzalez

Bibliographic record

VenueeYLS (Yale Law School) · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicRecycling and Waste Management Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsDumpingBusinessLaw and economicsRisk analysis (engineering)International tradeEconomics
DOInot available

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.632
Threshold uncertainty score0.464

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.009
GPT teacher head0.250
Teacher spread0.241 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations6
Published2018
Admission routes1
Has abstractyes

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