Assessing the effectiveness and efficiency of methane regulations in British Columbia, Canada
Bibliographic record
Abstract
Many jurisdictions are introducing methane reduction policy as part of climate commitments, with a focus on the oil and gas supply chain. Periodic comprehensive leak detection and repair (LDAR) surveys or screening leak detection and repair surveys are required in the British Columbia, Canada oil and gas sector to reduce unintentional methane emissions caused by leaking infrastructure. By finding and fixing leaks quickly, emissions of methane, a potent greenhouse gas, are reduced. In this study, we explored how effectively British Columbia’s regulation, deposited in December 2018, made progress towards meeting the policy objective of a 45% decrease in methane emissions by 2025 from a 2014 baseline. We evaluated the performance and cost effectiveness of regulatory-prescribed LDAR programmes using data collected by the BC Energy Regulator (formerly BC Oil and Gas Commission) for the 2020 year, and survey cost data submitted by service providers. We found that the new regulation was only partially effective due to low compliance rates. We also observed heavy tail leak distributions in LDAR data collected by service companies, but comparatively narrow leak distributions in data from permit holders who internalized LDAR operations, suggesting a difference in work practice or the use of equipment. Comprehensive leak detection surveys were found to be more cost efficient ($23 CAD/tCO2e reduced) compared to screening surveys ($1,787 CAD/tCO2e reduced) because more methane leaks are detected in comprehensive assessments. To meet methane reduction targets, we recommend that: 1) jurisdictions work to improve compliance rates by introducing minimum administrative penalties specific to noncompliance; 2) that all future LDAR surveys are instrument-based; 3) limiting turnaround time limits so that repairs are not delayed indefinitely; and 4) that LDAR completed internally by permit holders undergo independent third-party verification.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".