Climate policy subsystems in Alberta and British Columbia: Lessons for climate policymaking
Bibliographic record
Abstract
Given climate policy stagnation at the federal level in Canada, it is important to study provincial climate policy efforts as an important location of policy innovation. This paper uses the literature on policy subsystems to analyze provincial climate initiatives in Alberta and British Columbia. Specifically, the Specified Gas Emitters Regulation in Alberta, and the carbon tax in BC will be evaluated for their stringency and effectiveness in meeting provincial climate plans. The central issue to be explored is how the nature of the different subsystems has influenced policy outcomes and instrument choice. Close attention will be paid to the political economy of climate policy instrument choice in each subsystem, drawing on the observations of David Victor who suggests that climate policy is driven by who pays the costs of the policy and the relative distribution of these costs. This requires consideration of the structural power of emitters within the subsystem. The relative power of environmental groups and industry was found to influence the instrument choice within each subsystem. It suggests that subsystems with greater countervailing power to industry are more likely to choose market-based policy instruments, as regulations can be used to control the costs and their allocation, often to the detriment of emissions reductions. The paper also suggests that while provincial action is stalled without further national or US action, it also highlights that Alberta and British Columbia were willing to strengthen their climate policies given greater collective action.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.007 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".