Carbon Capture and Storage (CCS) as a Pillar for Balancing Energy Transition and Climate Goals
Bibliographic record
Abstract
The global pursuit of climate change mitigation and energy transition presents a formidable challenge, demanding innovative solutions to significantly reduce carbon emissions while ensuring the continued stability of global energy systems. Carbon Capture and Storage (CCS) stands out as a pivotal technology capable of bridging the gap between fossil fuel reliance and the widespread adoption of renewable energy sources. This paper explores the role of CCS in achieving energy transition and climate goals, focusing on its technical mechanisms, applications, and potential to decarbonize hard-to-abate sectors. By capturing carbon dioxide (CO2) emissions from industrial processes, fossil fuel power plants, and even directly from the atmosphere, CCS can prevent large amounts of CO2 from entering the atmosphere, thereby supporting the global effort to limit temperature rise as outlined in the Paris Agreement. The paper provides a detailed review of the three core stages of CCS: capture, transportation, and storage, explaining the technological innovations behind each stage. Various CCS methods, including pre-combustion, post-combustion, and oxy-fuel combustion, are discussed, illustrating the flexibility of CCS technologies across diverse industrial applications, from power generation to heavy industries such as cement, steel, and chemicals. Furthermore, the integration of CCS with renewable energy systems is analyzed, demonstrating how CCS can complement intermittent renewable sources, contributing to grid stability and enhancing energy security. Despite its potential, several barriers hinder the large-scale deployment of CCS, including technological challenges, high costs, and societal concerns about CO2 storage safety. The paper also emphasizes the importance of supportive policies, including carbon pricing, incentives for early-stage projects, and international collaboration, to facilitate the wide-scale adoption of CCS. Global case studies, including successful projects in Canada and Norway, provide valuable insights into best practices for overcoming these barriers and scaling up CCS infrastructure. Finally, the paper explores the future directions for CCS research and policy, emphasizing the importance of ongoing innovation and international investment in advancing CCS technologies. As nations strive to meet their net-zero emissions targets, CCS will likely play a critical role in enabling industries to decarbonize without compromising economic stability or energy access.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".