Mapping the global co-location potential of offshore wind energy and aquaculture production
Bibliographic record
Abstract
Co-location of offshore industries has potential to mitigate the growing congestion in the oceans and support the use of ocean resources for ecologically sustainable economic growth and improved livelihoods (i.e., the blue economy). Despite the benefits, questions remain over the feasibility of co-location due to regulatory and financial risk concerns. Here, we combine existing data on aquaculture production potential and wind energy production potential to map co-location potential for offshore wind and aquaculture (finfish, bivalve, and seaweed) globally. We then incorporate an existing index of each country's blue economy development capacity to assess areas with the greatest opportunity for co-location. Finally, we assess co-location potential in the Bass Strait, Australia to show how our approach can inform regional-level planning. We found potential for co-location across 395,042 km 2 of Exclusive Economic Zones (EEZ) globally for bivalves, 1,337,874 km 2 for finfish, and 1,143,643 km 2 for seaweed across 97 countries. Argentina, Australia, and Russia had the largest potential area for co-location, while Uruguay, Lithuania, and Belgium had the largest proportion of their EEZ. Denmark, Canada, and Finland had the largest proportion of potential area for co-location and highest capacity for blue economy development, signifying potential opportunities to be leaders in co-location implementation. Finally, the Bass Strait had high co-location potential for offshore wind and finfish aquaculture, but less for seaweed and bivalve production. Our research provides a high-level assessment of co-location across scales that can be used to streamline planning efforts, capitalise on potential opportunities, reduce risks, and facilitate blue economic growth. • Nearly 3 million km 2 of ocean space is suitable for co-location of wind and aquaculture (finfish, bivalves and seaweed). • Over 95 countries have suitable area for co-location of these two industries. • Many countries with high co-location potential face social and economic barriers to development • We demonstrate how to downscale our approach using a case study in the Bass Strait to inform future planning.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".