Deep-sea mining and its risks for social-ecological systems: Insights from simulation-based analyses
Bibliographic record
Abstract
The pros and cons of deep-sea mining (DSM) is currently hotly debated. Here, we assess the environmental, economic, and social risks of DSM by comparing scenarios with and without DSM involvement. The "Without" scenario relies solely on land-based mining and circular economy solutions, while the "With" scenario incorporates DSM alongside circular strategies, highlighting the dangers of heavy DSM dependence. Through literature review and expert interviews, our study identifies key risk indicators across environmental, economic, and social dimensions, forming a comprehensive assessment framework. Through the application of qualitative data and fuzzy cognitive mapping, the analysis reveals that environmental factors are the most influential (centrality: 1.46), followed by social (1.32) and economic (1.0) factors. In the "With DSM" scenario, all indicators show increased risks, with environmental factors, particularly "coastal state vulnerability," experiencing a 13% rise. Social risks, including "violation of law," "participatory rights," "lack of effective control," and "degraded reputation," increase by 8-11%, while economic risks, such as "contractual violations," "lack of special provision," "knowledge gap on economic assistance fund" and disputes among "multiple stakeholders," see an 11% uptick. Our results suggest that the risks DSM poses to deep-sea marine ecosystems are likely too significant to justify its pursuit and advocates for circular economy solutions as viable alternatives to mitigate environmental, social, and economic risks. We recommend that policies should promote circular practices through resource recovery incentives.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".