Harmonizing Regulatory Frameworks: Unlocking Carbon Capture and Storage Potential under the Inflation Reduction Act
Bibliographic record
Abstract
This article analyzes the regulatory landscape for carbon capture and storage (CCS) in the United States, focusing on incentives introduced through the Inflation Reduction Act (IRA) and Section 45Q tax credits. This focus does not overlook other key related legislation. It does, however, underpin the core thrust of this article that while several federal policies offer significant financial support for and incentives to accelerate CCS deployment, the very multiplicity and fragmentation of these regulations and policies across federal and state levels poses barriers to scalable adoption. Additionally, CCS faces competition from other clean energy technologies, which are also incentivized under the IRA, potentially diverting resources and focus. Through a comprehensive policy review, this study identifies regulatory conflicts and financial disincentives that hinder CCS’s potential. It argues that, despite federal support, the absence of cohesive, standardized regulations continues to significantly limit CCS’s emission reduction capabilities, especially in industries where decarbonization is inherently difficult. The findings underscore the importance of harmonizing CCS regulations to streamline permitting processes and address jurisdictional inconsistencies. By aligning federal and state policies, policymakers can better support CCS in achieving the U.S.’s climate goals, particularly for industries with limited alternatives for emission reduction.
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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 | 0.030 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.001 | 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".