Exploring plausible future scenarios of deep seabed mining in international waters
Bibliographic record
Abstract
The global transition to renewable energy has intensified the demand for critical minerals, which are essential components in key green technologies. Many of these minerals are abundant on the international seabed, but negotiations surrounding deep-sea mining regulations have met significant challenges. Developing countries, having historically been marginalized in the benefits of resource extraction, have expressed concern about the regulatory framework for deep-sea areas. Additionally, scientific understanding of the potential ecological impacts of deep-sea mining on marine ecosystems remains limited. Despite these concerns, the economic incentives for exploiting deep-sea minerals are driving pressure to finalize regulatory frameworks and commence mining activities. While speculation abounds regarding the future trajectory of deep seabed mining, significant uncertainties persist when considering its development in international waters. This paper explores these uncertainties and examines the potential future implications of global policy decisions for both ecological sustainability and economic outcomes. Drawing on document analysis and expert interviews, we identify critical uncertainties and other drivers of change shaping the future of deep-sea mining. Using the 2 x 2 ‘intuitive logics’ matrix method, we develop scenario narratives based on the two most critical uncertainties: the place of environmental management and redistribution of benefits in the nascent industry. The scenarios present possible futures for deep-sea mining in international waters, providing insights to inform regulatory decision-making.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".