Analyzing Regulatory Framework for Carbon Capture and Storage (CCS) Technology Development: A case study approach
Bibliographic record
Abstract
Aim: This article provides insight into the portfolio of regulations advancing Carbon Capture and Storage (CCS) deployment. Using a taxonomy of policy portfolio tools adapted for regulations specific to CCS, this research identifies regulatory gaps as well as supports for CCS projects. Design / Research methods: Through a case study approach, this article analyzes the regulatory provisions in six jurisdictions (Texas, North Dakota, the U.S, Saskatchewan, Alberta and Canada) which have a successful CCS facility. Analyzing the provisions and content of regulations in these jurisdictions, this article highlights regulatory supports or areas of gaps for CCS projects in each jurisdiction. Conclusions / findings: There is no uniform definition or categorization of CO2 as a hazard, waste, pollutant or commodity across jurisdictions. This has serious impact on CO2 transport, especially across jurisdictions. It also impacts the administration of storage systems for CCS facilities. Regulations focusing primarily on technical aspects of CCS including capture, transport, and liability predominate while there are less regulatory provisions for the financial aspects of CCS technology as well as public engagement and support. While capital grants and emission and tax credits are the predominant financial issues covered in regulations, contract for differences, streamlining emission trading across borders and enhancing cooperation and multilevel engagement in CCS warrant more attention. Originality / value of the article: Many scenarios to maintain global warming below 2 degrees Celsius require combinations of new technology including CCS. The focus on CCS cost as a barrier to deployment overshadows the needs for regulatory support as a means of reducing uncertainties and de-risking CCS investments.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".