Unlocking the Future of Carbon Capture and Storage Policy in Canada: A Cross-Jurisdictional Analysis of Carbon Capture Incentives
Bibliographic record
Abstract
Carbon removal technologies like carbon, capture, and storage (CCS) have been framed as one solution to rapidly reduce emissions in heavy emitting sectors. While Alberta and Saskatchewan operate several industry-leading CCS projects, the technology's widespread adoption has been stalled largely due to unstable financial and policy-related risks. To address these risks, this report uses cross-jurisdictional research featuring CCS policy incentives in Europe, the United States, and in Canada. The analysis demonstrated that all three jurisdictions have been unsuccessful in establishing a diverse policy environment that is conducive to the growth of their respective CCS industries. In particular, Canada has largely relied on direct grants to promote investment into the CCS market, which has not provided proponents with enough motivation to launch CCS projects in heavy emitting regions like Alberta. To address these financial risks, the report recommends that Canada implements a robust CCS investment tax credit regime, increased CCS demonstration and FEED study funding, along with policy incentives that encourage the clustering of CCS activities. Nevertheless, to confront the existing policy barriers, the report suggests creating a mechanism for guaranteeing future federal carbon prices, while allowing carbon credit stacking between provincial and federal CCS programs and enhancing knowledge sharing opportunities between project developers.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.007 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".