Characterization of methane emissions from oil and gas production in Mexico: Linking measurements to mitigation
Bibliographic record
Abstract
A wide body of research has characterized methane emissions from the oil and gas supply chain in the US, with recent efforts gaining traction in Canada and Europe. In contrast, empirical data is limited for other significant oil and gas producing regions across the global south. Consequently, measuring and characterizing methane emissions across global oil and gas operations is crucial to the design of effective mitigation strategies. Several countries have announced pledges to reduce methane emissions from this sector (e.g., North America, Climate and Clean Air Coalition [CCAC] ministers). In the case of Mexico, the federal government recently published regulations supporting a 40-45% reduction of methane emissions from oil and gas. For these regulations to be effective, it is critical to understand the current methane emission patterns. We present results from multi-scale empirical estimates of methane emissions from Mexico’s major oil and gas production regions (both offshore and onshore), based on a set of airborne-based measurement campaigns, analysis of satellite data (TROPOMI), and development of spatially explicit inventories. Our results provide a revised estimate of total emissions in the sampled regions and highlight the importance of empirically based characterization as a basis for prioritization in terms of emission reduction opportunities. Finally, we highlight how these measurements –as well as similar policy-relevant studies- connect into action, based on the current needs from relevant stakeholders (e.g., inventory builders, regulators and industry).
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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 | 0.000 |
| 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".