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Record W4410288297 · doi:10.1007/s10584-025-03933-4

An assessment of ocean thermal energy conversion resources and climate change mitigation potential

2025· article· en· W4410288297 on OpenAlexafffund
Anna G Nickoloff, Sophia T Olim, Michael Eby, Andrew J. Weaver

Bibliographic record

VenueClimatic Change · 2025
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOceanographic and Atmospheric Processes
Canadian institutionsUniversity of Victoria
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsOcean thermal energy conversionEnvironmental scienceClimate changeRenewable energyClimate modelElectricity generationClimate change mitigationClimatologyMeteorologyPower (physics)OceanographySeawaterEcologyGeography

Abstract

fetched live from OpenAlex

Ocean thermal energy conversion (OTEC) is a renewable energy system that harnesses the thermal gradient between surface and deep waters. Many multi-century simulations with a fully coupled climate-carbon cycle model are presented to explore the amount of extractable energy and the climate change mitigation potential from the widespread implementation of OTEC. The sustainability of OTEC power generation was assessed for present and possible future climate states. A warmer climate reduced the sustainable power potential of OTEC. OTEC could briefly produce over 35 TW of power and, depending on the climate state, maximum power production rates of 5 to 10 TW were found to be sustainable on multi-millennial timescales. Over 500 years of simulation, with a high emission scenario (equivalent to RCP8.5), the power from OTEC deployments, with peak power generation ranging from 3 to 15 TW at the year 2100, resulted in cumulative emission reductions equivalent to 36% to 111% of historical carbon emissions from 1750 to 2023 relative to the scenario without OTEC. Such significant emissions reductions coupled with sustained OTEC-induced mixing led to globally averaged atmosphere temperature decreases of up to 2.5 ºC by the year 2100 and up to 4 ºC by the year 2500 compared to a scenario without OTEC. While caution is required, and the engineering challenges would be large, early indications suggest that the large-scale implementation of OTEC could make a substantial contribution to climate change mitigation.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.075
Threshold uncertainty score0.397

Codex and Gemma teacher scores by category

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

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 head0.013
GPT teacher head0.248
Teacher spread0.234 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations7
Published2025
Admission routes2
Has abstractyes

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