Oil Sands, Carbon Sinks and Emissions Offsets: Towards a Legal and Policy Framework
Bibliographic record
Abstract
The development of Alberta's oil sands will result in significant greenhouse gas (GHG) emissions. This paper summarizes the implications of this development for Canada's emissions profile and reviews briefly the rationale for biotic carbon sequestration as a means of offsetting GHG emissions. The paper then turns to eight important issues for sinks-based offsets. These issues are: (1) the legal foundation for biotic carbon sequestration; (2) the risk of project failure and leakage; (3) monitoring and verification; (4) market intermediaries; (5) environmental risks; (6) land-use conflicts; (7) the alignment of regulatory requirements, policies and incentives; and (8) collateral benefits and strategic objectives. While some of these issues were identified in the federal and Alberta climate change plans released in 2002, these plans fall far short of establishing a comprehensive legal and policy framework for sinks-based offsets. The paper concludes by arguing that this framework should include carbon rights legislation, a regulatory and certification regime, and non-market mechanisms to increase biotic carbon sequestration. The promotion of sinks-based offsets should also occur as part of an integrated approach to resource and environmental management.
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 | 0.014 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.009 | 0.047 |
| Scholarly communication | 0.018 | 0.012 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.018 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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".