Assessing the availability and feasibility of renewable energy on the Great Barrier Reef-Australia
Bibliographic record
Abstract
The Great Barrier Reef (GBR), the world’s largest reef system and a UNESCO World Heritage site, is one of the most complex natural ecosystems on Earth. However, the GBR is at considerable risk from climate change and there is an urgent need to reduce reliance on fossil fuels and decarbonise the many activities taking place on the GBR. This study assesses the availability of renewable energy resources- solar, wind, wave, and tidal- on the GBR. Findings indicate that solar and wind energy are the most abundant natural resources on the GBR, while wave and tidal energy are only available in sparse locations and are low in magnitude. A feasibility analysis is conducted for various renewable energy technologies based on a case study for an Aerosol Radiation Interaction Experimental Laboratory system (ARIEL), an apparatus being used to investigate marine cloud brightening on the GBR. Factors used in the feasibility assessment include maturity of technology, portability, adaptability across the GBR, and ecological impacts on marine life and birds. Results suggest that solar photovoltaics, installed on a barge, would be the most suitable option for rapid near-term implementation. Not only is solar energy available throughout the whole GBR, but it is also a proven and mature technology and would have minimal impact on marine life and on birds. A proposed hybrid energy system could reduce the ARIEL’s CO 2 emissions by 44–59 %, varying by location. The findings offer a roadmap for deploying cleaner energy systems on the GBR, balancing environmental protection with technological considerations. • Renewable energy resource availability is assessed on the Great Barrier Reef. • Different renewable energy technologies are evaluated for marine cloud brightening. • A hybrid solar-battery-diesel energy system is modelled and optimised. • The hybrid energy system is able to reduce CO 2 emissions by 44–59 % relative to a diesel generator.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 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".