High-resolution estimates of national methane emissions for all countries of the world using TROPOMI observations
Bibliographic record
Abstract
Observational constraints on national scale methane emissions are needed to assist progress towards the goals of the Paris Agreements and the Global Methane Pledge. Here, we use 2023 blended TROPOMI+GOSAT observations of atmospheric methane in multiple analytical inversions to estimate emissions for all countries of the world at up to 25 km resolution. Prior emissions estimates are spatially distributed according to state-of-the-science bottom-up inventories, and country-level prior totals are adjusted by sector to match the emissions most recently reported to the UNFCCC. We enhance each inversion’s ability to capture point-source emissions not included in bottom-up inventories by redistributing oil-gas and coal emissions based on a gridded inventory constructed from GHGSat plume and null detections, and by enforcing native resolution emissions optimization at locations where plumes were observed by point source imagers including PRISMA, Sentinel-2, Landsat, EnMAP, GOES, and EMIT, and where large plumes were detected by TROPOMI. Our global total posterior emission of 562 Tg for 2023 is in line with previous coarse-scale global inversion studies. The inversions’ high resolution allows source separation and independent optimization of individual countries, confirmed by small posterior error correlations between countries. China (52.7 Tg), the U.S. (32.2 Tg), India (25.7 Tg), Brazil (18.5 Tg), and Indonesia (10.7 Tg) have the highest anthropogenic emissions, representing 14%, 9%, 7%, 5%, and 3% of the global total anthropogenic source, respectively. Uncertainty estimates come from an inversion ensemble with varied inversion parameters. Results provide an estimate of emissions from all countries in a globally consistent inverse modeling framework, serve as a direct comparison and aid of countries’ UNFCCC reporting, and provide up-to-date observational constraints on emissions from countries where reporting is unfeasible or out of date.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".