Monitoring of Unlit Flares using Sentinel-2: A Global Emissions Inventory for 2021 
Bibliographic record
Abstract
<div> <div> <div> <p>Immediate-term reductions of methane emissions are widely seen as a critical step toward holding global warming below 1.5–2°C. The oil and gas sector is a dominant source of anthropogenic methane emissions and numerous stakeholders have implicated gas flaring at upstream production sites as a key source to mitigate. Flaring is the process whereby OG producers destroy spurious yet significant volumes of methane-rich natural gas via open-air combustion. During their lifetime, gas flares may intermittently become extinguished in which case the flare, which generally reduces greenhouse gas emissions by converting methane to carbon dioxide, effectively becomes a direct vent of methane to atmosphere. Recently, ground-, air-, and satellite-based instruments have identified a troubling regularity with which these so-called unlit flares may exist in some jurisdictions. Unlit flares may genuinely be accidental – resulting from equipment malfunctions, extreme wind conditions, or unpredictable emergency scenarios – or deliberate – to support maintenance operations, due to poor operating practices, or to obscure volumes of flared gas. Regardless of intent, the frequency with which flares are unlit and, critically, the global scale of the associated methane emissions are poorly understood.</p> <p>In this work, publicly available imagery data from the multispectral instrument on-board the Sentinel-2 satellites are leveraged to develop a global emissions inventory for unlit flaring during 2021. Sentinel-2 data are processed with published methane retrieval algorithms and a novel plume detection algorithm to identify and estimate emission rates of detected unlit flares. Detected emissions are subsequently considered in the context of <em>scene-specific</em> probabilities of detection, which permits extrapolation of available data to infer a first-ever global emissions inventory for unlit flares. A Monte Carlo analysis is used to robustly characterize uncertainties of this approach.</p> </div> </div> </div>
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".