The Legal Framework for Carbon Dioxide Removal in Canada
Bibliographic record
Abstract
Recent assessments of progress on greenhouse gas (GHG) emissions reductions suggest that efforts to reduce emissions are well below what is necessary to meet current global targets of 2 degrees Celsius, let alone 1.5 degrees Celsius above pre-industrial levels. Current Intergovernmental Panel on Climate Change models include significant amounts of carbon dioxide removal (CDR) from the atmosphere as necessary to meet the 2 degrees Celsius target. The models assume the availability of CDR technologies to contribute to climate goals, but significant uncertainties remain regarding the efficacy, costs, scalability, environmental impacts, and broader public acceptability of these technologies. In Canada, CDR technologies are a crucial element of Canada’s long-term climate strategy towards achieving net-zero emissions by 2050. Still, little to no national policy attention has been paid to researching, assessing, and implementing CDR measures, including the necessary legal framework in which these technologies would operate. This article provides an overview of Canada’s existing legal framework that will apply to various CDR methods as they are developed. It examines the legal framework as it may apply to CDR measures collectively (particularly in consideration of how these technologies will be treated in Canada’s broader climate framework), and individually. It aims to take stock of existing federal and provincial rules and assess the potential gaps that will need to begin to be addressed as Canada develops CDR capacities.
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 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.019 | 0.012 |
| Scholarly communication | 0.017 | 0.003 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.011 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".