Seeing Double: Peace, Order, and Good Government, and the Impact of Federal Greenhouse Emissions Legislation on Provincial Jurisdiction
Bibliographic record
Abstract
Federal regulation of greenhouse gas (GHG) emissions presents a difficult challenge for Canadian constitutional law. The federal government’s legislation to implement a national minimum standard of GHG emissions pricing, the Greenhouse Gas Pollution Pricing Act (GGPPA), and the trio of reference cases launched by Saskatchewan, Ontario, and Alberta questioning its constitutional validity, have brought the law and politics of GHG emissions pricing to the forefront of Canadian federalism. In the two appellate court decisions delivered to date, the legislation has been sustained as a valid exercise of Parliament’s power to legislate for the Peace, Order, and Good Government (POGG) of Canada. In each case, however, judges have expressed significant concern with respect to the impact of the legislation on provincial jurisdiction. We draw on recent and historic jurisprudence to characterize conceptual errors that have bedevilled POGG, specifically in the tendency to overestimate its impact on provincial jurisdiction. We then examine the existing interpretive principles that limit POGG’s ability to upend the critical balance inherent in the division of powers. Finally, we discuss how a properly empowered, calibrated, and constrained POGG relates to the GGPPA. We argue that the reduction of national GHG emissions constitutes a valid federal subject under the national concern branch of POGG, and that the GGPPA is a valid exercise of federal jurisdiction. We see no reason under the double aspect doctrine and cooperative federalism why provinces would lose any existing provincial jurisdiction as a result of the implementation of the GGPPA. Rather, a restrained approach to paramountcy, and the mechanics of the GGPPA itself suggest that provincial and federal legislation will work concurrently on GHGs. That seems entirely appropriate given the nature of the climate change crisis before us. In the legislative challenge of our time, we believe Canada’s Constitution is up to the task.
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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.043 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.007 |
| 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".