The effectiveness of Carbon Pricing: The Case of Alberta and British Columbia
Bibliographic record
Abstract
The Intergovernmental Panel on Climate Change (IPCC) recently presented its Fourth Report that contained additional evidence that human activities are a significant cause of increases in Greenhouse Gas (GHG) emissions resulting in a warming of the climate. There has been growing pressure on governments to substantially reduce these emissions by adopting effective policy mechanisms. In Canada, individual provinces have implemented a variety of approaches that best fit their individual circumstances. This, in turn, provides an opportunity to assess the effectiveness of different policy approaches in reducing carbon emissions. This report focuses on the relative impacts of the carbon tax implemented by British Columbia (B.C.), and Alberta’s carbon levy. Having neither a carbon tax or carbon levy, Saskatchewan is used as the ‘control province.’ A difference-in-difference estimate is used to study the real mitigation effects of the carbon tax and levy. The results indicate that Alberta’s carbon levy has had a positive impact in reducing the emissions intensity levels of the oil and gas, electricity and heat, transportation and residential buildings sectors. The mitigation effects of the B.C. carbon tax were limited to the transportation sector. Based on the findings of the statistical analysis presented in the report, several recommendations are made so that a greater reduction in emissions can be achieved. The recommendations include: expanding the size and scope of the levy for large emitters, and subjecting small emitters to the carbon levy, phasing out the use of coal-fired plants for power generation in Alberta, introducing energy efficiency programs, and monitoring the performance of Alberta’s Specified Gas Emitters Regulation on a continuous basis.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".