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Record W4408423100 · doi:10.5194/egusphere-egu25-3295

Global gas flaring volumes may be underestimated: comparisons with over a decade of industry reporting in Canada

2025· preprint· en· W4408423100 on OpenAlexaffabout
Scott P. Seymour, Donglai Xie, Mary Kang

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnergy
TopicOil, Gas, and Environmental Issues
Canadian institutionsMcGill University
Fundersnot available
KeywordsEnvironmental scienceAccountingBusiness

Abstract

fetched live from OpenAlex

Global gas flaring from the oil and gas industry was estimated to be 148 billion cubic meters in 2023, based on satellite observations from the Visible Infrared Imaging Radiometer Suite (VIIRS; World Bank, 2024). Both lit and unlit flares are sources of potent greenhouse gases and health hazards, making it a source requiring accurate global monitoring. The VIIRS instrument often forms the basis for gas flaring volumes, but our study reveals that these estimates are underestimated in Canada, and potentially elsewhere.Comparing VIIRS flaring observations with industry reporting across Western Canada for 2012-2023, we found that industry reports ~2.3-times more gas flaring than estimated by satellite. This appears to be primarily the result of small/medium-sized flares going undetected (generally less than 350 m3/h, or 220 kg/h, assuming 90% methane content), but we also estimate that ~17% of industry-reported flaring was missed because of enclosed combustors, which do not have a flame visible to VIIRS. For flares that are detected by VIIRS, aggregate volume estimates agree within ~8% of industry reporting, although individual flares can be +/- an order of magnitude from industry reporting, similar to offshore findings from Brandt (2020).If this issue of underestimated flaring volumes from VIIRS is limited to Canada, global gas flaring estimates would increase by only 1%, but Canada would be the 10th most flaring country (up from 23rd). However, if undetected flares are more widespread, global flaring could be much more deeply underestimated. VIIRS’s theoretical detection limits imply that smaller flares should be detected, implying other factors are impacting detection/quantification, such as VIIRS data filtering, flaring practices (e.g., daytime-only blowdown flaring), or persistent cloud cover. ReferencesBrandt AR. 2020. Accuracy of satellite-derived estimates of flaring volume for offshore oil and gas operations in nine countries. Environmental Research Communications 2(5). IOP Publishing. doi: 10.1088/2515-7620/ab8e17World Bank. 2024. Global Gas Flaring Tracker Report. (June). Washington, DC. Available at https://www.worldbank.org/en/programs/gasflaringreduction/global-flaring-data. Accessed 2024 Jul 2.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.192
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.036
GPT teacher head0.290
Teacher spread0.254 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes2
Has abstractyes

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