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Record W3216485027 · doi:10.1073/pnas.2022668118

Narrowing feedstock exemptions under the Montreal Protocol has multiple environmental benefits

2021· article· en· W3216485027 on OpenAlexaboutno aff
Stephen O. Andersen, Suely Carvalho, Tad Ferris, Marco Gonzalez, Nancy J. Sherman, Yiyao Wei, Durwood Zaelke

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicMicroplastics and Plastic Pollution
Canadian institutionsnot available
Fundersnot available
KeywordsMontreal ProtocolOzone layerRaw materialProtocol (science)Scope (computer science)Environmental scienceBusinessEnvironmental protectionWaste managementEnvironmental economicsComputer scienceOzoneEngineeringChemistryEconomics

Abstract

fetched live from OpenAlex

The Montreal Protocol on Substances that Deplete the Ozone Layer (Montreal Protocol) can be further strengthened to control ozone-depleting substances and hydrofluorocarbons used as feedstocks to provide additional protection of the stratospheric ozone layer and the climate system while also mitigating plastics pollution. The feedstock exemptions were premised on the assumption that feedstocks presented an insignificant threat to the environment; experience has shown that this is incorrect. Through its adjustment procedures, the Montreal Protocol can narrow the scope of feedstock exemptions to reduce inadvertent and unauthorized emissions while continuing to exempt production of feedstocks for time-limited, essential uses. This upstream approach can be an effective and efficient complement to other efforts to reduce plastic pollution. Existing mechanisms in the Montreal Protocol such as the Assessment Panels and national implementation strategies can guide the choice of environmentally superior substitutes for feedstock-derived plastics. This paper provides a framework for policy makers, industries, and civil society to consider how stronger actions under the Montreal Protocol can complement other chemical and environmental treaties.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.710
Threshold uncertainty score0.471

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 studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.038
GPT teacher head0.258
Teacher spread0.220 · 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 designBench or experimental
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

Citations46
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the National Academy of SciencesSame topicMicroplastics and Plastic PollutionFrench-language works237,207