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Record W2522676053

Greenhouse Gas Emissions from International Maritime Transport: The Science in a New Zealand and Australian Context

2010· article· en· W2522676053 on OpenAlexaboutno aff
Inga J. Smith, Oliver J.A. Howitt, Vincent G.N. Revol, Warren B. Fitzgerald, Craig J. Rodger

Bibliographic record

VenueAustralian and New Zealand Maritime Law Journal · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicMaritime Transport Emissions and Efficiency
Canadian institutionsnot available
Fundersnot available
KeywordsKyoto ProtocolGreenhouse gasContext (archaeology)International tradeMontreal ProtocolLiabilityInternational lawAviationEmissions tradingBusinessEconomyPolitical scienceEconomicsLawEngineeringGeographyFinanceMeteorology
DOInot available

Abstract

fetched live from OpenAlex

Greenhouse gas emissions from international maritime transport contribute to anthropogenic global warming, as do those from international air transport. However, no liability was apportioned for these international emissions under the Kyoto Protocol when it was adopted in 1997. Instead, Article 2.2 of the Kyoto Protocol (United Nations, 1998) stated that: “2. The Parties included in Annex 1 shall pursue limitation or reduction of emissions of greenhouse gases not controlled by the Montreal Protocol from aviation and marine bunker fuels, working through the International Civil Aviation Organization and the International Maritime Organization, respectively.” In recent years, there has been increasing international attention given to quantifying such emissions, with a view to possible inclusion of liabilities under future international climate agreements, particularly leading up to the United Nations Climate Change Conference (Conference of Parties (COP) 15) in Copenhagen, 7-18 December 2009. This paper will review the science of greenhouse gas emissions from international maritime transport in the context of international maritime vessels passing through New Zealand and Australian ports. New Zealand and Australia are both countries with entirely maritime international borders and are geographically remote from many of their trading partners. Both nations are therefore heavily reliant on international maritime transport for the trade of goods with other countries. The implications of recent legal and policy developments in the Australasian geographic context will be briefly discussed.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.376
Threshold uncertainty score0.984

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 communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0170.000

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 head0.014
GPT teacher head0.240
Teacher spread0.226 · 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.

Study designObservational
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

Citations0
Published2010
Admission routes1
Has abstractyes

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