Oil and gas industry emissions in Saskatchewan, Canada: a case study in uncertain reduction trends
Bibliographic record
Abstract
The province of Saskatchewan, Canada, came under new oil and gas industry emission regulations in 2020 to meet Canada’s methane emission reduction target for 2025. Although the province reportedly met its reduction target only two years into a five-year commitment, this finding was based on a bottom-up inventory whose operator-reported data had not been public until very recently. With these data made public for the first time, we recreated the federal government inventory for Saskatchewan (2012-2022) to better understand where and how emissions have changed in response to new regulations. Not only will this shed light on the regulations themselves (an updated version of which will be under review for Canada’s longer-term targets), but this inventory will also permit more detailed comparisons with forthcoming top-down measurements.Since new measurements are expected to be included in this inventory structure, we also used recently published aerial LiDAR measurements to update the inventory. Importantly, while the unmodified inventory confirms the significant emission reduction reported by the Saskatchewan Government (~45% reduction), the inclusion of aerial measurement data suggests that emissions may have actually increased over the same time period. We discuss how the manner in which new measurements are included can influence the emission reductions relative to an uncertain baseline year, and we discuss whether a trend can be calculated reliably. Careful consideration will therefore be needed when including new measurement data into existing inventories.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.010 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".