The Marks of Ownership: The Promotion of Carbon Capture and Storage in France
Bibliographic record
Abstract
Carbon capture and storage (CCS) is a bundle of technologies aiming to capture carbon dioxide (CO2) on industrial sites and store it underground in depleted oil and gas reservoirs or saline aquifers. The literature describes a three-step history of CCS: a first period of development based on research and development (R&D) and demonstrators (1990–2009); then a crisis period (2009–14); followed by a potential revival from 2015 (Markusson et al 2012; Minx et al 2018). CCS was developed initially to reduce carbon emissions in the energy sector, but with the rise of renewables, CCS now targets heavy industrial activities such as cement, steel, and chemical plants. The Global CCS Institute describes France as a second-tier actor in CCS development. The country is recognized as having a lower domestic ‘inherent interest’ in CCS than countries such as Australia, Canada, China, and the United States (US), since its ‘propensity towards fossil fuel production and consumption’ is lower (in particular in the electricity production sector because of the high proportion of nuclear power), and since it has undergone considerable de-industrialization (Global CCS Institute 2017, p 37). While France seems to have only a limited strategic interest in developing CCS and while only one injection facility has been commissioned so far, publicly funded researchers continue to develop new R&D projects, and CCS was integrated in the 2020 National Low Carbon Strategy as a contribution to the 2050 carbon neutrality goal (MTES 2020). This constitutes a paradox that this chapter aims to address. Why is there a significant CCS coalition in France despite the low ‘inherent interest’ for CCS? How has this coalition adapted its CCS proposal according to critiques so as to sustain political attention for 20 years, despite achieving none of its previous promises? Investigating the French coalition of CCS promoters, this chapter sheds light on the role of the owners of the proposal, how they shape it, and how they benefited from the promotion of the CCS solution for combating climate change.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".