Investigating The Differences In Corporate Greenhouse Gas Inventories Between Reporting Jurisdictions
Bibliographic record
Abstract
The IPCC considers climate change to be one of the six major sustainability problems in the world. In Canada policies and regulations in various jurisdictions have been developed to mitigate emissions. These all impact the oil and gas industry, a major emissions contributor. These regulations differ among jurisdictions. The objective of this study is to determine if, and if so how corporate GHG inventories differ between Alberta (AB) and British Columbia (BC) in terms of calculated emissions as a result of these differences. The scope, rigidity, operational boundaries and methodologies of the regulations are compared. The results indicate that the corporate inventory of an oil and gas company vary significantly if compiled following AB regulations versus BC regulations. Differences in the scope, rigidity and operational boundary were identified. Material differences between fugitive, pneumatic, fuel and flare methodologies were identifed. Based on absolute materiality, this paper concludes the two inventories are significantly different from one another. Recommendations are provided to improve the commensuration of the inventories including the use of site specific and disaggregated data.
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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".