Global satellite survey reveals uncertainty in landfill methane emissions
Bibliographic record
Abstract
Abstract Methane is a potent but short-lived greenhouse gas and rapid reductions of its anthropogenic emissions could help decrease near-term warming 1 . Solid waste emits methane through the decay of organic material, which amounts to about 10% of total anthropogenic methane emissions 2 . Satellite instruments 3 enable monitoring of strong methane hotspots 4 , including many strongly emitting urban areas that include solid waste disposal sites as most prominent sources 5 . Here we present a survey of methane emissions from 151 individual waste disposal sites across six continents using high-resolution satellite observations that can detect localized methane emissions above 100 kg h –1 . Within this dataset, we find that our satellite-based estimates generally show no correlation with reported or modelled emission estimates at facility scale. This reveals major uncertainties in the current understanding of methane emissions from waste disposal sites, warranting further investigations to reconcile bottom-up and top-down approaches. We also observe that managed landfills show lower emission per area than dumping sites, and that detected emission sources often align with the open non-covered parts of the facility where waste is added. Our results highlight the potential of high-resolution satellite observations to detect and monitor methane emissions from the waste sector globally, providing actionable insights to help improve emission estimates and focus mitigation efforts.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".