Governing Carbon Removal: Deploying Direct Air Capture Amidst Canada’s Energy 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 global temperature over the next century. Compared to other forms of CDR, DACCS requires little land and carries fewer environmental risks. Still, 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 natural resource management. DACCS policies will be built on the foundations of existing policies in these areas and will be influenced by the structure and content of this policy landscape. 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. This thesis addresses this deficit by identifying the key policy foundations for developing and deploying DACCS at scale in Canada. Drawing on socio-technical transitions theory, particularly the multi-level perspective, I identify the constituent policy areas that are likely to form the future DACCS policy regime. The purpose of this policy review and analysis is to show that the policies used to deploy DACCS will need to address systemic issues of social acceptability; financing climate mitigation innovations; energy system and resource constraints; coordinating and regulating carbon storage and transport; and establishing general climate policies that support the role of DACCS in the transition process. Using a database of Canadian climate policies (n=457), I populate these key policy areas with existing policies, which enables me to map and analyze the emergent DACCS policy landscape in Canada. The growing body of literature on the policies needed for scaling up DACCS provides a basis for analyzing the adequacy of Canada’s current policies while creating system maps has allowed me to identify the potential trajectories of the system by identifying potential niches and broader landscape influences within the system, as well as identifying gaps and potential barriers to the system transition process. This thesis contributes to our understanding of national-level DAC policy development by providing a framework for identifying components of the DAC system and linking those components to desired policy outcomes.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".