Regulatory limitations and global stakeholder mapping of carbon capture and storage technology – a legal and multi-level perspective analysis
Bibliographic record
Abstract
Carbon Capture and Sequestration Technology (CCS) is propounded as one of the key bridging technologies and temporary abatement measure in the battle against climate change. Not only is it based on well-established technology, used and improved upon for decades in the fossil fuels industry, but it also has the potential to remove vast quantities of CO2 from the atmosphere giving much needed alleviation away from climate tipping points. Despite these advantages, CCS has been slow to start and easy to stall, with financial risk and uncertainty, lack of regulatory cohesion and a disjointed policy mix all playing a part in impeding its commercialization. Systems Thinking and Transition Theory in particular have been widely adopted as methodologies which have the potential to elucidate the barriers to development in socio-technical systems of the likes of CCS. Using one such theory - Multi-Level Perspective Analysis - as an analytical framework, an in-depth investigation was performed of both the ‘Niche’ and ‘Regime’ of CCS. This was undertaken through a comprehensive legal and regulatory analysis and a global survey of 604 stakeholders involved in research and development throughout the technology chain. The combined examination of the legal and stakeholder system boundaries is used to set the ‘chessboard’ and ‘pieces’ upon which further analysis of the ‘combinations’ of moves open to CCS will be revealed. In essence, the regulatory and stakeholder configurations, which most lend themselves to CCS technology development, are explored and elucidated. This is done with the aim to address the knowledge gaps in the legal and regulatory requirements necessary for implementing CCS on a wider scale, as identified by the Intergovernmental Panel on Climate Change (IPCC, 2005).
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".