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Healing the Ozone Layer

2019· book-chapter· en· W2982041295 on OpenAlexaboutno aff
Frederike Albrecht, Charles F. Parker

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldEconomics, Econometrics and Finance
TopicClimate Change Policy and Economics
Canadian institutionsnot available
Fundersnot available
KeywordsMontreal ProtocolOzone layerLeverage (statistics)Kyoto ProtocolProtocol (science)VetoPolitical scienceBusinessEnvironmental economicsComputer scienceOzoneEconomicsMeteorologyClimate changeGeographyLaw

Abstract

fetched live from OpenAlex

Abstract The Montreal Protocol—the regime designed to protect the stratospheric ozone layer—has widely been hailed as the gold standard of global environmental governance and is one of few examples of international institutional cooperative arrangements successfully solving complex transnational problems. Although the stratospheric ozone layer still bears the impacts of ozone depleting substances (ODSs), the problem of ozone depletion is well on its way to being solved due to the protocol. This chapter examines how the protocol was designed and implemented in a way that has allowed it to successfully overcome a number of thorny challenges that most international environmental regimes must face: how to attract sufficient participation, how to promote compliance and manage non-compliance, how to strengthen commitments over time, how to neutralize or co-opt potential ‘veto players’, how to make the costs of implementation affordable, how to leverage public opinion in support of the regime’s goals, and, ultimately, how to promote the behavioural and policy changes needed to solve the problems and achieve the goals the regime was designed to solve. The chapter concludes that while some of the reasons for the Montreal Protocol’s success, such as fairly affordable, available substitutes for ODSs, are not easy to replicate, there are many other elements of this story that can be utilized when thinking about how to design solutions to other transnational environmental problems.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.781
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0140.036

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 head0.188
GPT teacher head0.251
Teacher spread0.064 · 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; both teacher heads agree on what is shown here.

Study designTheoretical or conceptual
Domainnot available
GenreOther

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

Citations22
Published2019
Admission routes1
Has abstractyes

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