Planning Transitions: SEAs, Climate Change, and Energy Transitions in the Ocean
Bibliographic record
Abstract
Oceans have been gaining attention as a solution to climate change, including as sites of renewable energy development. Thus, oceans are becoming increasingly important sites of the energy transitions necessary for countries to meet climate change commitments. However, oil remains deeply entrenched in many places, often making it difficult to envision and enact different energy futures. This paper addresses the potential of strategic environmetal assessments (SEAs) to play a role in ocean-based energy transitions, specifically exploring whether they provide the information needed to support decisions related to energy transitions. By drawing on 69 offshore energy-related SEAs done in 11 countries between 2000 and 2021, this paper takes a broad view of ocean energy SEAs across time and space. In particular, we explore whether these SEAs consider climate change, include the broader context of climate goals, and assess alternatives. Findings varied by jurisdiction but in many cases, SEAs were done in ways that limited their potential to contribute to energy transition decisions. This is important because countries around the world have climate commitments and SEAs represent an opportunity - specifically one that uses existing and familiar tools - to help with long term planning around offshore energy that can help meet these commitments.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".