The role of CCUS in accelerating Canada's transition to net-zero
Bibliographic record
Abstract
Canada has been an enthusiastic developer and implementer of carbon capture, utilization and storage (CCUS) technologies, currently accounting for nearly 20 per cent of installed CCUS capacity globally. Along with a steep hike in the federal carbon price announced in 2020, the government has crafted a hydrogen-centric strategy to support a decarbonized economic transition, with CCUS identified as a key enabler of both pathways. On the one hand, CCUS justifies continued investment in the oil and gas sector through the permanent sequestering of CO2. On the other hand, creating additional economic value from the blue hydrogen generated from CCUS can demonstrate the viability of carbon-negative hydrogen production using bioenergy with carbon capture and storage (BECCS) or from electrolysis with offsets from direct air capture with carbon storage (DACCS). Oil and gas firms, supported by their peers in heavy industry, have announced blue hydrogen, oilsands CCUS, and carbon transportation projects which – if implemented – could transform the province of Alberta and disrupt the Canadian economy. Despite the bold vision of the government’s strategy and the announced projects, there are potential challenges to widespread CCUS deployment, including technological scope, project finance, and regulatory assurance. Carbon pricing will support project economics, but only up to a certain point, especially given the volatility of commodity markets and declines in Canadian oil and gas sector investment. And federal and provincial regulations – with the allied components of social, Indigenous and environmental support – will require clarity if announced projects are to be implemented.
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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".