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Record W4406809699 · doi:10.1080/14693062.2025.2451645

Equitable marine carbon dioxide removal: the legal basis for interstate benefit-sharing

2025· article· en· W4406809699 on OpenAlexafffund
Neil Craik

Bibliographic record

VenueClimate Policy · 2025
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicClimate Change Policy and Economics
Canadian institutionsBalsillie School of International AffairsUniversity of Waterloo
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsCarbon dioxideBusinessNatural resource economicsCarbon dioxide removalEnvironmental scienceEnvironmental resource managementEconomicsChemistry

Abstract

fetched live from OpenAlex

Marine carbon dioxide removal (mCDR) deployment strategies appear likely to involve the use of common ocean resources to generate financial benefit for mCDR developers. An important consideration for the future design of an equitable mCDR governance regime is the distribution of those benefits between states, particular between developed and developing states. This paper addresses the legal basis for inter-state benefit-sharing arrangements arising from mCDR activities through an analysis of existing inter-state benefit-sharing arrangements in international resource regimes. The paper identifies a set of key conditions that are present and justify the imposition of benefit-sharing between states. Specifically, claims for inter-state benefit-sharing are legally justified where the resources exploited are located in areas beyond national jurisdiction; the resource opportunities are finite; and the ability to access the resource opportunities are constrained by technological and financial capacity. These conditions will most likely be present for many deployment scenarios for key mCDR technologies. In presenting an argument in favour of inter-state benefit-sharing, this paper seeks to provide a more complete understanding of the equitable and legal dimensions of distributing the economic and non-economic benefits from mCDR activities.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.419
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.067
GPT teacher head0.292
Teacher spread0.224 · 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 designTheoretical or conceptual
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

Citations3
Published2025
Admission routes2
Has abstractyes

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