Deep‐sea nodules versus land ores: A comparative systems analysis of mining and processing wastes for battery‐metal supply chains
Bibliographic record
Abstract
Abstract To meet UN Sustainable Development goals, a clean‐energy transition with minimal ecological impact from its raw‐material supply chain is essential. Polymetallic nodules lying unattached on the abyssal seafloor of the Pacific Ocean's Clarion Clipperton Zone contain four critical metals (nickel, cobalt, manganese, copper) in large quantities, and the International Seabed Authority may soon enact regulations to allow their commercial exploitation. There are complex global ecological implications of doing so. Nodule exploitation would damage abyssal habitats and may impact midwater‐column organisms; but in the absence of nodule exploitation, terrestrial mining's environmental and social impacts would intensify. This paper adds to the growing systems‐based literature on nodule collection by contributing a preliminary material flow analysis of global‐average cradle‐to‐gate waste streams using either nodules or terrestrial sources as part of a preliminary life cycle assessment, as well as integrated risk assessments of those waste streams. System endpoints are battery precursors (nickel sulfate, cobalt sulfate, manganese sulfate), copper cathode, and a 40% or 75% manganese product. Overburden, tailings, and processing and refining wastes from terrestrial mining are compared to the nodule industry's anticipated offshore and onshore wastes, including sediment disrupted by nodule‐collection machines. Robustness to offshore technology assumptions is tested using Monte Carlo simulation, while onshore mass‐flow scenarios incorporate a “negligible‐waste” flowsheet and high‐waste flowsheets where manganese is not recovered. A billion‐EV scenario incorporates the effects of declining terrestrial copper and nickel ore grades. Results imply that metal production from nodules may produce less waste of lower severities, caveated by uncertain impacts of disrupted sediment.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".