Pore Space as a Resource: A Discussion of the Policy and Regulatory Framework for Carbon Capture, Utilization, and Storage
Bibliographic record
Abstract
Reinforced by the International Energy Agency (IEA), carbon capture, utilization, and storage (CCUS) is currently the only available group of technologies that reduce emissions in key hard to abate sectors and capture CO2 emissions that enable low carbon value chains such as hydrogen. Further, CCUS and carbon management play a critical role in achieving future global climate and energy goals. In fact, the Intergovernmental Panel on Climate Change and the IEA state that there is no viable path to net zero emissions without CCUS and other carbon management technologies. Due to concerns regarding energy security and an increase in energy demand, generation of energy from conventional hydrocarbon resources continues to be vital. In Alberta, CCUS is a necessary tool to align provincial climate change goals with the responsible and competitive market of energy production. Canada’s oil and gas sector has been an early innovator and adopter of CCUS. Given the petroleum and natural gas resources available in the Western Canadian Sedimentary Basin, and the decades-long energy industry expertise established in connection therewith, there is significant potential to further utilize CCUS to create a CCUS-based value chain. This article provides an overview of the current Canadian regulatory frameworks enabling CCUS, with a focus on the regulatory framework and development in Alberta. Specific topics include: (1) an overview of the regulatory frameworks governing CCUS in key jurisdictions in Canada, including Alberta; (2) an overview of the frameworks for the generation of offset credits from environmental attributes associated with a given project or activity, including both federal and provincial carbon credits and clean fuel credits; (3) a discussion of gaps in policy and legislation; (4) options for regulating “open access” CCUS hubs and CO2 pipelines; and (5) an overview of the various governmental incentives for CCUS projects, including federal and provincial tax credits.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".