Country-level methane emissions and their sectoral trends during 2009–2020 estimated by high-resolution inversion of GOSAT and surface observations
Bibliographic record
Abstract
Abstract Considering the significant role of global methane emissions in the Earth’s radiative budget, global or regionally persistent increasing trends in its emission are of great concern. Understanding the regional contributions of various emissions sectors to the growth rate thus has policy relevance. We used a high-resolution global methane inverse model to independently optimize sectoral emissions using GOSAT and ground-based observations for 2009–2020. Annual emission trends were calculated for top-emitting countries, and the sectoral contributions to the total anthropogenic trend were studied. Global total posterior emissions show a growth rate of 2.6 Tg yr −2 ( p < 0.05), with significant contributions from waste (1.1 Tg yr −2 ) and agriculture (0.9 Tg yr −2 ). Country-level aggregated sectoral emissions showed statistically significant ( p < 0.1) trends in total posterior emissions for China (0.56 Tg yr −2 ), India (0.22 Tg yr −2 ), United States (0.65 Tg yr −2 ), Pakistan (0.22 Tg yr −2 ) and Indonesia (0.28 Tg yr −2 ) among the top methane emitters. Emission sectors contributing to the above country-level emission trend are, China (waste 0.35; oil and gas 0.07 Tg yr −2 ), India (agriculture 0.09; waste 0.11 Tg yr −2 ), United States (oil and gas 1.0; agriculture 0.07; coal −0.15 Tg yr −2 ), Brazil (waste 0.09; agriculture 0.08 Tg yr −2 ), Russia (waste 0.04; biomass burning 0.15; coal 0.11; oil and gas −0.42 Tg yr −2 ), Indonesia (coal 0.28 Tg yr −2 ), Canada (oil and gas 0.08 Tg yr −2 ), Pakistan (agriculture 0.15; waste 0.03 Tg yr −2 ) and Mexico (waste 0.04 Tg yr −2 ). Additionally, our analysis showed that methane emissions from wetlands in Russia (0.24 Tg yr −2 ) and central African countries such as Congo (0.09 Tg yr −2 ), etc. have a positive trend with a considerably large increase after 2017, whereas Bolivia (−0.09 Tg yr −2 ) have a declining trend. Our results reveal some key emission sectors to be targeted on a national level for designing methane emission mitigation efforts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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 teacher head, 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".