Accelerating the deployment of carbon capture and storage technologies by strengthening the innovation system
Bibliographic record
Abstract
In order to take up the twin challenge of reducing carbon dioxide (CO2) emissions, while meeting a growing energy demand, the potential deployment of carbon dioxide capture and storage (CCS) technologies is attracting a growing interest of policymakers around the world. In this studywe evaluate and compare national approaches towards the development of CCS in the United States, Canada, Norway, the Netherlands, and Australia. The analysis is done by applying the functions of innovation systems approach. This approach posits that new technology is developed, demonstrated and deployed in the context of a technological innovation system. The performance assessment of the CCS innovation system shows that the extensive knowledge base and knowledge networks, which have been accumulated over the past years, have not yet been utilized by entrepreneurs to explore the market for integrated CCS concepts linked to power generation. This indicates that the build-up of the innovation system has entered a critical phase that is decisive for a further thriving development of CCS. In order to move the CCS innovation system through this present difficult episode and deploymore advanced CCS concepts at a larger scale; it is necessary to direct policy initiatives at the identified weak system functions, i.e. \nentrepreneurial activity, market creation and the mobilization of resources. Moreover, in some specific countries it is needed to provide more regulatory guidance and improve the legitimacy for the technology.We discuss how policymakers and technology managers can use these insights to develop a coherent policy strategy that would accelerate the deployment of CCS.
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 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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".