Towards a co‐crediting system for carbon and biodiversity
Bibliographic record
Abstract
Societal Impact Statement Humankind is facing both climate and biodiversity crises. This article proposes the foundations of a scheme that offers tradable credits for combined aboveground and soil carbon and biodiversity. Multidiversity—as estimated based on high‐throughput molecular identification of soil meiofauna, fungi, bacteria, protists, plants and other organisms shedding DNA into soil, complemented by acoustic and video analyses of aboveground macrobiota—offers a cost‐effective method that captures much of the terrestrial biodiversity. Such a voluntary crediting system would increase the quality of carbon projects and contribute funding for delivering the Kunming‐Montreal Global Biodiversity Framework. Summary Carbon crediting and land offsets for biodiversity protection have been developed to tackle the challenges of increasing greenhouse gas emissions and the loss of global biodiversity. Unfortunately, these two mechanisms are not optimal when considered separately. Focusing solely on carbon capture—the primary goal of most carbon‐focused crediting and offsetting commitments—often results in the establishment of non‐native, fast‐growing monocultures that negatively affect biodiversity and soil‐related ecosystem services. Soil contributes a vast proportion of global biodiversity and contains traces of aboveground organisms. Here, we outline a carbon and biodiversity co‐crediting scheme based on the multi‐kingdom molecular and carbon analyses of soil samples, along with remote sensing estimation of aboveground carbon as well as video and acoustic analyses‐based monitoring of aboveground macroorganisms. Combined, such a co‐crediting scheme could help halt biodiversity loss by incentivising industry and governments to account for biodiversity in carbon sequestration projects more rigorously, explicitly and equitably than they currently do. In most cases, this would help prioritise protection before restoration and help promote more socially and environmentally sustainable land stewardship towards a ‘nature positive’ future.
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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 | 0.000 | 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 | 0.000 | 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) | 0.000 | 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 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".