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Record W2003288016 · doi:10.1080/0003684021000035818

Market power and hot air in international emissions trading: the impacts of US withdrawal from the Kyoto Protocol

2003· article· en· W2003288016 on OpenAlexaboutno aff
Christoph Böhringer, Andreas Löschel

Bibliographic record

VenueApplied Economics · 2003
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicClimate Change Policy and Economics
Canadian institutionsnot available
FundersDeutsche ForschungsgemeinschaftEuropean Commission
KeywordsKyoto ProtocolGreenhouse gasEconomicsRevenueEmissions tradingMontreal ProtocolInternational economicsConventionUnited Nations Framework Convention on Climate ChangeInternational tradeEconomyFinancePolitical science

Abstract

fetched live from OpenAlex

Ten years after the initial Climate Change Convention from Rio in 1992 the industrialized world is finally likely to ratify the Kyoto Protocol, which will impose legally binding greenhouse gas emission reductions on the developed world. However, the Kyoto Protocol will enter into force without the USA, which withdrew under President Bush in March 2001. Accounting for hot air and market power of the Former Soviet Union on emission permit markets, it is shown that US withdrawal has important consequences on environmental effectiveness, compliance costs, and excess costs of market power under the Kyoto Protocol. Non-compliance of the USA implies a dramatic decrease in environmental effectiveness as well as compliance costs of OECD countries whereas the Former Soviet Union and transitional economies in Eastern Europe suffer from a huge decline in permit sales revenues. Excess costs of market power in permit trade increase in relative terms, but decline substantially in absolute terms due to US withdrawal. Policy options are quantified to bypass the problems of hot air and market power through compensation mechanisms.

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.004
Scholarly communication0.0070.007
Open science0.0010.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0060.000

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 head0.034
GPT teacher head0.246
Teacher spread0.212 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations68
Published2003
Admission routes1
Has abstractyes

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