Federalism and climate governance: assessing the effectiveness of Canada's carbon pricing policy within the framework of the Paris Agreement
Bibliographic record
Abstract
Climate change presents one of the most pressing policy challenges of our era, affecting ecosystems and societies globally. According to the IPCC report (2022), human activities, particularly the release of greenhouse gases (GHGs), are identified as the primary drivers of this crisis. In response, there is widespread recognition of the urgent need to transition to a low-carbon economy. Key to this transition are policies aimed at curbing GHG emissions, while fostering economic sustainability. One important policy instrument that has emerged over time is 'carbon pricing', designed to incorporate the environmental costs of carbon emissions into economic decision-making. By establishing a price on carbon, these policies encourage businesses and individuals to opt for more environmentally friendly alternatives, thereby contributing to overall emission reduction targets. Canada, as a prominent developed nation and a signatory to the Paris Agreement, has implemented robust carbon pricing strategies to regulate GHG emissions. Since 2015, the federal government has made a national carbon pricing policy a cornerstone of its climate change strategy. The introduction of the Greenhouse Gas Pollution Pricing Act (GHGPPA) in 2018 exemplifies Canada's commitment, aiming to incentivize emission reductions across various sectors of the economy. Despite its promise, the real-world impact of carbon pricing remains a subject of debate. While widely acknowledged as a potentially effective approach, questions persist about its actual efficacy in combating climate change, especially considering the influence of federalism in either hindering or bolstering policy effectiveness within federal states. This study seeks to contribute to this ongoing discourse by conducting a thorough analysis of Canada's experience with carbon pricing. The study explores the dynamics that led to the collapse of support for a national system of carbon pricing in Canada. It argues that, while there was considerable expert consensus and political support for carbon pricing, given the earlier provincial policies, ultimate support for a national system of carbon pricing collapsed for largely political reasons. Despite surviving numerous court challenges, which affirmed the federal government's right to set a price on carbon emissions, regionalism and demands for provincial autonomy ultimately undermined broader support for the policy.
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 | 0.011 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.011 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| 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".