Global energy sector methane emissions estimated by using facility-level satellite observations
Bibliographic record
Abstract
Methane emissions from energy sector facilities (oil, gas, and coal) represent a substantial contribution to greenhouse gas emissions with substantial mitigation potential. We estimated global 2023 methane emissions from energy sector point sources using the high spatial resolution GHGSat satellite constellation. GHGSat detected 8.30 [Formula: see text] 0.24 million tonnes per year of methane emissions from 3114 emission sites. Detected oil and gas- and coal-emitting sites were found to be emitting 16 and 48% of the time, respectively, above GHGSat's detection limit without obvious continental variation. Compared with the Global Fuel Exploitation Inventory (GFEIv3) estimate, GHGSat's estimate comprises 12% of GFEIv3's total emissions, or 24% over GHGsat-observed locations, with good spatial correlation at the country scale but only weak spatial correlation at 0.2°-×-0.2° grid cell scale.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".