Methane Emissions from Natural Gas Production Sites in the United States: Data Synthesis and National Estimate
Bibliographic record
Abstract
Abstract We used site-level methane (CH4) emissions data from over 1000 natural gas (NG) production sites in eight basins, including 92 new site-level CH4 measurements in the Uinta, northeastern Marcellus, and Denver-Julesburg basins, to investigate CH4 emissions characteristics and develop a new national CH4 emission estimate for the NG production sector. The distribution of site-level emissions is highly skewed, with the top 5% of sites accounting for 50% of cumulative emissions. High emitting sites are predominantly also high producing (>10 Mcfd). However, low NG production sites emit a larger fraction of their CH4 production. When combined with activity data, we predict that this creates substantial variability in the basin-level CH4 emissions which, as a fraction of basin-level CH4 production, range from 0.90% for the Appalachian and Greater Green River to >4.5% in the San Juan and San Joaquin. This suggests that much of the basin-level differences in production-normalized CH4 emissions reported by aircraft studies can be explained by differences in site size and distribution of site-level production rates. We estimate that NG production sites emit total CH4 emissions of 830 Mg/h (95% CI: 530–1200), 63% of which come from the sites producing <100 Mcfd that account for only 10% of total NG production. Our total CH4 emissions estimate is 2.3 times higher than the U.S. Environmental Protection Agency’s estimate and likely attributable to the disproportionate influence of high emitting sites.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".