The sustainable worldwide offshore wind energy potential: A systematic review
Bibliographic record
Abstract
The offshore wind industry is expanding rapidly around the world due to several factors enabling this source of renewable energy. Stronger wind resources in offshore areas, lack of social and geographical constraints related to onshore wind power, the evolution of technology, and increasing demand for electricity in coastal regions as a result of a massive increase in population are some of the factors favoring the use of wind energy. The assessment of the potential global capacity that considers the different economic, environmental, and social factors and the dynamics of market, policy, and technology are vital for estimating the competitiveness of offshore wind energy in the future energy profile. There are several studies and technical reports that evaluate the potential of offshore wind energy in different countries or regions. They used a different source of data, metrics, and quantitative approaches in appraising the potential offshore wind power capacity and its cost efficiency. The critical factors that have been considered are geographical, technical, economic, environmental, and social and market elements. This paper provides a systematic review for analyzing the studies that address the potential offshore wind energy around the world and published during the 2000–2016 period. This study highlights the key criteria for assessing the potential for offshore wind energy deployment and the related tools and methods.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".