U.S. Ethane Emissions and Trends Estimated from Atmospheric Observations
Bibliographic record
Abstract
Oil and natural gas (O&G) production and processing activities have changed markedly across the U.S. over the past several years. However, the impacts of these changes on air pollution and greenhouse gas emissions are not clear. In this study, we examine U.S. ethane (C 2 H 6 ) emissions, which are primarily from O&G activities, during years 2015–2020. We use C 2 H 6 observations made by the NOAA Global Monitoring Laboratory and partner organizations from towers and aircraft and estimate emissions from these observations by using an inverse model. We find that U.S. C 2 H 6 emissions (4.43 ± 0.2 Tg·yr –1 ) are approximately three times those estimated by the EPA’s 2017 National Emissions Inventory (NEI) platform (1.54 Tg·yr –1 ) and exhibit a very different seasonal cycle. We also find that changes in U.S. C 2 H 6 emissions are decoupled from reported changes in production; emissions increased 6.3 ± 7.6% (0.25 ± 0.31 Tg) between 2015 and 2020 while reported C 2 H 6 production increased by a much larger amount (78%). Our results also suggest an apparent correlation between C 2 H 6 emissions and C 2 H 6 spot prices, where prices could be a proxy for pressure on the infrastructure across the supply chain. Overall, these results provide insight into how U.S. C 2 H 6 emissions are changing over time.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".