Using global isotopic data to constrain the role of shale gas production in recent increases in atmospheric methane
Bibliographic record
Abstract
Abstract The accelerated increase in global methane (CH 4 ) in the atmosphere, accompanied by a decrease in its 13 C/ 12 C isotopic ratio ( δ 13 C CH4 ) from −47.1‰ to −47.3‰ observed since 2008, has been attributed to increased emissions from wetlands and cattle, as well as from shale gas and shale oil developments. To date both explanations have relied on poorly constrained δ 13 C CH4 source signatures. We use a dataset of δ 13 C CH4 from >1600 produced shale gas samples from regions that account for >97% of global shale gas production to constrain the contribution of shale gas emissions to observed atmospheric increases in the global methane burden. We find that US shale gas extracted since 2008 has volume-weighted-average δ 13 C CH4 of −39.6‰. The average δ 13 C CH4 weighted by US basin-level measured emissions in 2015 was −41.8‰. Therefore, emission increases from shale gas would contribute to an opposite atmospheric δ 13 C CH4 signal in the observed decrease since 2008 (while noting that the global isotopic trend is the net of all dynamic source and sink processes). This observation strongly suggests that changing emissions of other (isotopically-lighter) CH 4 source terms is dominating the increase in global CH 4 emissions. Although production of shale gas has increased rapidly since 2008, and CH 4 emissions associated with this increased production are expected to have increased overall in that timeframe, the simultaneously-observed increase in global atmospheric CH 4 is not dominated by emissions from shale gas and shale oil developments.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".