Comparing environmental impacts of deep‐seabed and land‐based mining: A defensible framework
Bibliographic record
Abstract
The crises of climate change and biodiversity loss are interlinked and must be addressed jointly. A proposed solution for reducing reliance on fossil fuels, and thus mitigating climate change, is the transition from conventional combustion-engine to electric vehicles. This transition currently requires additional mineral resources, such as nickel and cobalt used in car batteries, presently obtained from land-based mines. Most options to meet this demand are associated with some biodiversity loss. One proposal is to mine the deep seabed, a vast, relatively pristine and mostly unexplored region of our planet. Few comparisons of environmental impacts of solely expanding land-based mining versus extending mining to the deep seabed for the additional resources exist and for biodiversity only qualitative. Here, we present a framework that facilitates a holistic comparison of relative ecosystem impacts by mining, using empirical data from relevant environmental metrics. This framework (Environmental Impact Wheel) includes a suite of physicochemical and biological components, rather than a few selected metrics, surrogates, or proxies. It is modified from the "recovery wheel" presented in the International Standards for the Practice of Ecological Restoration to address impacts rather than recovery. The wheel includes six attributes (physical condition, community composition, structural diversity, ecosystem function, external exchanges and absence of threats). Each has 3-5 sub attributes, in turn measured with several indicators. The framework includes five steps: (1) identifying geographic scope; (2) identifying relevant spatiotemporal scales; (3) selecting relevant indicators for each sub-attribute; (4) aggregating changes in indicators to scores; and (5) generating Environmental Impact Wheels for targeted comparisons. To move forward comparisons of land-based with deep seabed mining, thresholds of the indicators that reflect the range in severity of environmental impacts are needed. Indicators should be based on clearly articulated environmental goals, with objectives and targets that are specific, measurable, achievable, relevant, and time bound.
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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 | 0.012 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.014 | 0.009 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| 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".