Direct measurements of methane emissions from natural gas end use in Germany
Bibliographic record
Abstract
Natural gas appliances and piping in buildings, or post-meter sources, are estimated to represent 15% of U.S. natural gas distribution sector emissions of methane. However, recent atmospheric methane measurement studies in urban areas indicate that end use emissions may be several times higher than currently estimated in national inventories. National inventories use bottom-up methods to estimate post-meter methane emissions, but due to the relatively small set of direct measurements, if available, many inventory estimates are likely to be highly uncertain. Moreover, there are systematic differences in building heating systems and natural gas appliance usage across countries and regions. For example, North American households mainly use forced air systems that rely on ducts and vents; while in Germany, it is common to distribute heat from a central heating unit through radiators. Therefore, although there have been several publications of direct measurement studies conducted in the U.S., it is difficult to extrapolate these findings to other countries and regions, including Germany, the largest natural gas user in Europe.To better understand and quantify emissions from natural gas end use in Germany, we analyze spatially-integrated tall-tower eddy covariance surface fluxes of methane and conduct direct measurements of methane emissions from natural gas appliances and piping in homes and other buildings. The measurement data includes gas composition analysis and are analyzed in conjunction with natural gas appliances and building attributes. Our results can inform effective methane emission mitigation strategy development and energy transition policies in Germany and elsewhere.
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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 | 0.000 |
| 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) | 0.000 | 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".