Scaling CCUS in Canada: An assessment of fiscal and regulatory frameworks
Bibliographic record
Abstract
Canada's position as a global leader in oil and gas production, as well as a proponent of emissions reduction, has led to significant support for the commercialization of carbon capture, utilization and storage (CCUS) technology. Viewed as the best way to reduce emissions from heavy industry, CCUS can also enable the value chain for technologies like direct air capture (DAC) which are seen as the future of carbon capture. Successful CCUS projects such as Shell's Quest and the Alberta Carbon Trunk Line have demonstrated that the operational expertise exists in Canada. To support the broad adoption of this technology, the government has introduced two fiscal and regulatory levers - carbon pricing and a CCUS investment tax credit (ITC). Federal output-based pricing system (OBPS) for carbon, introduced in 2018, will see the cost of CO2 escalate from CA$65/tCO2e in 2023 to CA$170/tCO2e by 2030. Despite some structural differences, there has been strong alignment on carbon pricing and CCUS incentives at the provincial and federal levels. In the province of Alberta, the likely hub of CCUS activity in Canada, the TIER regulation for industrial emitters has been deemed sufficient to avoid the federal large emitter program being applied as a backstop. On the other end of the carrot-stick dynamic, the ITC provides a rebate - approximately 20-30% - of project costs associated with CCUS implementation. The formation of the Pathways Alliance reflects the oilsands sector's trend towards collaboration as a way of supporting the sector's economic future. If successful, the alliance will see sharing of common costs like transportation and storage, thus reducing the risk for individual facilities and driving down the levelized cost of CCUS. The ITC in combination with carbon pricing provides enough of an incentive for firms to deploy CCUS. It may not be as lucrative for investors as the 45Q tax credit in the United States, but it does offer long-term value to heavy emitters when avoided costs of carbon are considered. To sustain momentum and ensure project delivery, additional economic levers may need to be pulled to narrow the investment gap. More importantly, it is crucial that federal and provincial governments offer carbon price certainty, for example through carbon contracts for differences (CCfDs). In addition, whether through programs like TIER or the federal OBPS, tightening rates and the expiry term for offsets and credits may need to be adjusted as required to balance supply and demand. With the government's carbon management strategy about to be released, there is CCUS momentum in Canada - delivering on it will require continued collaboration, project excellence and consistent fiscal and regulatory frameworks.
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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 | 0.016 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.012 | 0.016 |
| Science and technology studies | 0.013 | 0.005 |
| Scholarly communication | 0.016 | 0.003 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".