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Record W4385696873 · doi:10.1021/acs.est.3c02529

Promoting Cross-Regional Integration of Maritime Emission Management: A Euro-American Linkage of Carbon Markets

2023· article· en· W4385696873 on OpenAlexafffundabout
He Peng, Chunjiang An, Zhikun Chen, Xuelin Tian, Yao Sun

Bibliographic record

VenueEnvironmental Science & Technology · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicMaritime Transport Emissions and Efficiency
Canadian institutionsConcordia University
FundersNatural Sciences and Engineering Research Council of CanadaEuropean University Institute
KeywordsGreenhouse gasLinkage (software)Emissions tradingBusinessCarbon leakageCompetence (human resources)Environmental economicsInternational tradeEconomics

Abstract

fetched live from OpenAlex

Reducing greenhouse gas emissions from maritime transport is an urgent topic. Some regional emissions trading systems (ETSs), buoyed by the globalized market-based measures (MBMs) plan of the International maritime organization, have initially assessed the feasibility of including maritime emissions under compliance obligations. However, including maritime emissions (which are interjurisdictional) in the existing ETSs is controversial, and globalized maritime MBMs remain elusive. Therefore, this study designed a joint bilateral maritime carbon market (BMCM) model based on the European ETS (EU-ETS) and Quebec ETS (QC-ETS). The carbon costs, speed optimization, and marginal abatement costs of three container routes under BMCM were analyzed. The results show that this Euro-American linkage achieves adequate emission coverage on specific routes and generates acceptable carbon costs for charterers. This study yields a positive result for the equal division of ETSs' exercising competence in cross-regional maritime transport and provides evidence for sector-specific ETS links based 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.609
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0000.005
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.006
GPT teacher head0.233
Teacher spread0.227 · 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

Citations27
Published2023
Admission routes3
Has abstractyes

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