Present and future biodiversity risks from fossil fuel exploitation
Bibliographic record
Abstract
Abstract Currently, human society is predominantly powered by fossil fuels—coal, oil, and natural gas—yet also ultimately depends on goods and services provided by biodiversity. Fossil fuel extraction impacts biodiversity indirectly through climate change and by increasing accessibility, and directly through habitat loss and pollution. In contrast to the indirect effects, quantification of the direct impacts has been relatively neglected. To address this, we analyze the potential threat to >37,000 species and >190,000 protected areas globally from the locations of present and future fossil fuel extraction in marine and terrestrial environments. Sites that are currently exploited have higher species richness and endemism than unexploited sites, whereas known future hydrocarbon activities will predominantly move into less biodiverse locations. We identify 181 “high‐risk” locations where oil or gas extraction suitability coincides with biodiversity importance, making conflicts between extraction and conservation probable. In total, protected areas are located on $3‐15 trillion of unexploited hydrocarbon reserves, posing challenges and potentially opportunities for protected area management and sustainable financing.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".