Environmental impacts from the widespread implementation of ocean thermal energy conversion
Bibliographic record
Abstract
Abstract Ocean thermal energy conversion (OTEC) is a renewable energy system that could potentially displace significant amounts of fossil fuel-generated electricity. This study presents numerous multi-century simulations of the University of Victoria Earth System Climate Model, a coupled climate-carbon cycle model, to better understand the global-scale environmental impacts of the widespread implementation of OTEC at varying total power levels (3, 5, 7, 10, and 15 TW). Environmental impacts include reduced warming of the sea surface by up to 3.1 ºC, increased heat uptake at intermediate depths, and enhanced biological production compared to a fossil fuel intensive control scenario. At year 2100, OTEC-induced mixing contributes roughly 60% of the relative cooling, while the remainder is from OTEC-related emission reductions. Once OTEC is terminated, all relative cooling is caused by accumulated emissions reductions. If acting alone, the residual effect of OTEC-induced mixing would contribute to a minor relative warming of the sea surface. The effect of OTEC on the expansion of known oxygen minimum zones was minimal. In many circumstances, OTEC deployment opposes the projected impacts of climate change. Relative to a high carbon emissions control scenario, OTEC deployment is associated with less surface warming, a smaller increase in surface water pCO2, a suppression of ocean acidification, and significantly smaller declines in the strength of the Atlantic Meridional Overturning Circulation. Despite the potential engineering challenges and economic costs, 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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".