Analysis of Flaring Activity at Liquefied Natural Gas (LNG) Export Facilities Worldwide
Bibliographic record
Abstract
Liquefied natural gas (LNG) export facilities are booming worldwide to supply gas for the growing energy demand. Flaring, the controlled burning of natural gas, occurs at these facilities during operations ranging from start-up to ongoing maintenance and under emergency situations. Although flaring can be a significant air pollutant and greenhouse gas emission source, little information exists on the frequency, duration, and volume of gas flared by LNG export facilities. This study leveraged ten years of data from the Visible Infrared Imaging Radiometer Suite (VIIRS) Night Fire (VNF) product associated with 48 existing LNG export facilities globally to develop probabilities of flaring at different life-cycle stages. We found a significantly higher volume of gas flared in the first two years of a facility's operation (i.e., on average 1.9 (1.0-3.2) billion cubic meters (bcm) per capacity vs 0.62 (0.43-0.92) bcm during subsequent years). During regular operations, the annual volume of gas flared was correlated with the facility's production capacity, and flaring varied greatly among facilities (148 (137-159) flaring days/year on average and 0.73 (0.64-0.85) bcm/capacity). Unfortunately, most environmental assessments overlook the start-up phase and fail to consider worst-case scenarios. As flaring is a source of air pollution, its potential health impacts on local populations may be underestimated in these assessments.
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 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 | 0.000 |
| 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 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".