Building climate resiliency in offshore wind energy expansion plans
Bibliographic record
Abstract
Abstract The offshore wind energy (OWE) sector is experiencing rapid global growth, with ambitious plans to scale up renewable energy capacity significantly. While this expansion is vital for mitigating climate change, ensuring the resilience of OWE infrastructure in the face of extreme weather and climatic events exacerbated by climate change remains a critical yet often overlooked aspect of the current literature. The main objective of this topical review is twofold. First, we provide a critical synthesis of related literature to outline how key aspects of climate change, such as rising ocean temperatures, shifting wind patterns, and intensifying storms, may affect the performance, maintenance needs, and structural integrity of OWE infrastructure. Second, we perform a global spatial analysis that overlays projections of climate hazards under the shared socioeconomic pathways with datasets of current and planned OWE installations. This approach allows us to identify geographic hotspots where climate-related stressors intersect with major OWE development zones, highlighting areas that require targeted resilience strategies. This understanding is essential for developing proactive strategies to ensure the long-term viability and resiliency of current and future OWE infrastructure.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".