Addressing Competitiveness of Emissions-intensive and Trade-exposed Sectors: A Review of Alberta’s Carbon Pricing System
Bibliographic record
Abstract
In 2018, the Canadian province of Alberta introduced a revamped carbon;pricing regime – the Carbon Competitiveness Incentives Regulation (CCIR) – for large industrial emitters. This regulation set product benchmark;emissions intensities and required that facilities purchase emissions credits for the portion of emissions that fell above that product benchmark. With a focus on Alberta's oil and gas industry, this paper assesses mechanisms used under the CCIR to address competitiveness-driven carbon leakage for emissions-intensive and trade-exposed sectors. These mechanisms include exclusions, output-based allocations and financial compensation in the form of market-based compliance flexibility and access to innovation funding. The CCIR also provided additional cost containment for facilities that demonstrated significant economic risk resulting from carbon compliance costs. The output-based allocation, which is the primary policy mechanism for addressing competitiveness, is compared with the carbon pricing system in California. This review suggests that the CCIR failed to provide evidence that emission allocation levels were sufficient to mitigate carbon leakage. Further, the system's definition of competitiveness failed to incorporate the ability to attract new investment, which is particularly important for the oil and gas sector due to its capital-intensive nature. Understanding policy options for protecting industry competitiveness from carbon pricing may help to inform future regulatory design that mitigates the likelihood of carbon leakage.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".