“Carbon Bombs” - Mapping key fossil fuel projects
Bibliographic record
Abstract
Meeting the Paris targets requires reducing both fossil fuel demand and supply, and closing the “production gap” between climate targets and energy policy. But there is no supply-side mitigation roadmap yet. We need criteria to decide where to focus efforts. Here, we identify the 425 biggest fossil fuel extraction projects globally (defined as >1 gigaton potential CO2 emissions). We list these “carbon bombs” by name, show in which countries they are located and calculate their potential emissions which combined exceed the global 1.5 °C carbon budget by a factor of two. Already producing carbon bombs account for a significant percentage of global fossil fuel extraction. But 40% of carbon bombs have not yet started extraction. Climate change mitigation efforts cannot ignore carbon bombs. Defusing them could become an important dimension of climate change mitigation policy and activism towards meeting the Paris targets. So far, few actors, mainly from civil society, are working on defusing carbon bombs, but they are focussing on a very limited number of them. We outline a priority agenda where the key strategies are avoiding the activation of new carbon bombs and putting existing ones into “harvest mode”.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".