A Patchwork of Climate Policies that Reflect Subnational Jurisdiction
Bibliographic record
Abstract
The United Nations introduced the Paris Agreement in 2015 aimed at reducing emissions globally. Signatories to the Agreement are responsible for amending their domestic climate policies and implementing binding emission targets. Federations confront several challenges when adopting such policies since jurisdiction is shared between orders of government. This paper explores why Canadian and American federal governance structures constrain the implementation of national climate policies following the Paris Agreement. Despite both countries having ratified the Agreement, climate action in Canada and the US is enacted in a patchwork fashion that reflects subnational jurisdiction. The paper begins by assessing the Paris Agreement and the challenges federations confront when adopting climate legislation. The following section examines the Canadian and American federations separately to identify the factors that inhibit a coordinated response to reducing national emissions post-2015. The paper concludes by exploring strategies to advance climate action on a subnational basis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".