Scaling carbon removal systems: deploying direct air capture amidst Canada’s low-carbon transition
Bibliographic record
Abstract
Carbon dioxide removal (CDR) technologies, such as direct air carbon capture and storage (DACCS), will be critical in limiting the rise of the average global temperature over the next century. Scaling up DACCS technologies requires the support of a complex array of policies and infrastructure across multiple overlapping policy areas, such as climate, energy, technology innovation and resource management. While the literature on DACCS and other CDR technologies acknowledges the path-dependent nature of policy development, it has tended to focus on abstract policy prescriptions that are not rooted in the specific political, social and physical (infrastructural) context of the implementing state. To address this gap, this paper provides a country-level study of the emerging DACCS policy regime in Canada. Drawing on the existing literature that identifies idealized (acontextual) policy objectives that support DACCS development and effective regulation, we identify the actionable policy objectives across six issue domains: general climate mitigation strategies; energy and resource constraints; carbon storage and transport regulation and infrastructure; financing scale-up and supporting innovation; removal and capture technology availability and regulation; and addressing social acceptability and public interest. Using a database of Canadian climate policies (n = 457), we identify policies within the Canadian (federal and provincial) policy environment that map to the idealized policy objectives within each of these domains. This exercise allows us to analyze how key policy objectives for DACCS development are represented within the Canadian system, and enables us to identify potential niches, and landscape influences within the system, as well as gaps and potential barriers to the system transition process. This paper contributes to our understanding of national DACCS policy development by providing a framework for identifying components of the DAC system and linking those components to desired policy outcomes and may provide a basis for future cross-country comparisons of national-level DACCS policy.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".