Life Cycle Assessment of Ocean Energy Technologies: A Systematic Review
Bibliographic record
Abstract
The increase of greenhouse gases (GHG) generated by the burning of fossil fuels has been recognized as one of the main causes of climate change (CC). Different countries of the world have developed new policies on national energy security directed to the use of renewable energies mainly, ocean energy being one of them. The implementation of ocean energy is increasing worldwide. However, the use of these technologies is not exempt from the generation of potential environmental impacts throughout their life cycle. In this context, life cycle assessment (LCA) is a holistic approach used to evaluate the environmental impacts of a product or system throughout its entire life cycle. LCA studies need to be conducted to foster the development of ocean energy technologies (OET) in sustainable management. In this paper, a systematic review was conducted and 18 LCA studies of OET were analyzed. Most of the LCA studies are focused on wave and tidal energy. CC is the most relevant impact category evaluated, which is generated mostly by raw material extraction, manufacturing stage and shipping operations. Finally, the critical stages of the systems evaluated were identified, together with, the opportunity areas to promote an environmental management for ocean energy developers.
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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 | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.016 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".