MétaCan
Menu
Back to cohort
Record W6921955293 · doi:10.1021/acs.est.3c02529.s001

Promoting Cross-Regional\nIntegration of Maritime Emission\nManagement: A Euro-American Linkage of Carbon Markets

2023· article· en· W6921955293 on OpenAlexaboutno aff

Bibliographic record

VenueFigshare · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicMaritime Transport Emissions and Efficiency
Canadian institutionsnot available
Fundersnot available
KeywordsGreenhouse gasEmissions tradingLinkage (software)Maritime industryPort (circuit theory)Carbon offsetContainer (type theory)Competence (human resources)

Abstract

fetched live from OpenAlex

Reducing greenhouse gas emissions from maritime transport\nis an\nurgent topic. Some regional emissions trading systems (ETSs), buoyed\nby the globalized market-based measures (MBMs) plan of the International\nmaritime organization, have initially assessed the feasibility of\nincluding maritime emissions under compliance obligations. However,\nincluding maritime emissions (which are interjurisdictional) in the\nexisting ETSs is controversial, and globalized maritime MBMs remain\nelusive. Therefore, this study designed a joint bilateral maritime\ncarbon market (BMCM) model based on the European ETS (EU-ETS) and\n Quebec ETS (QC-ETS). The carbon costs, speed optimization, and marginal\nabatement costs of three container routes under BMCM were analyzed.\nThe results show that this Euro-American linkage achieves adequate\nemission coverage on specific routes and generates acceptable carbon\ncosts for charterers. This study yields a positive result for the\nequal division of ETSs’ exercising competence in cross-regional\nmaritime transport and provides evidence for sector-specific ETS links\nbased on quantitative analysis.

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 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 score0.622
Threshold uncertainty score0.890

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.1110.000

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 head0.019
GPT teacher head0.257
Teacher spread0.238 · 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
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueFigshareSame topicMaritime Transport Emissions and EfficiencyFrench-language works237,207