Indonesian aquaculture futures—identifying interventions for reducing environmental impacts
Bibliographic record
Abstract
Abstract Indonesia is the world’s second largest producer and third largest consumer of seafood. Fish is therefore essential to the nation, both financially and nutritionally. Overfishing and the effects of climate change will, however, limit future landings of capture fisheries, so any increases in future seafood production will need to come from aquaculture. The ecological effects of aquaculture are dependent upon the choice of species, management, and where it is sited. In the present study we use life cycle assessment (LCA) to evaluate how possible interventions and innovations can mitigate environmental impacts related to the aquaculture sector’s growth. The mitigation potential of six interventions were also quantified, namely (1) FCR reductions for whiteleg shrimp, carp, and tilapia; (2) sustainable intensification of milkfish and Asian tiger shrimp polyculture; (3) shifting groupers from whole fish diets to pellets; (4) favoring freshwater finfish over shrimp; (5) renewable electricity; and (6) reduced food waste and improved byproduct utilization. If all six interventions are implemented, we demonstrate that global warming, acidification, eutrophication, land occupation, freshwater use, and fossil energy use could be reduced by between 28% and 49% per unit of fish. The addition of many innovations that could not be quantified in the present study, including innovative feed ingredients, suggest that production could double within the current environmental footprint. This does not, however, satisfy the expected 3.25-fold increase under a business-as-usual scenario, neither does it satisfy the government’s growth targets. We therefore also explore possible geographical areas across Indonesia where aquaculture expansions and ecological hotspots may conflict. Conclusively, we advocate more conservative production targets and investment in more sustainable farming practices. To accelerate the implementation of these improvements, it will be central to identify the most cost-effective aquaculture interventions.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".