Catalyzing the transition to a climate-neutral industry with carbon contracts for difference [Commentary]
Bibliographic record
Abstract
To achieve global emission reduction targets, speeding up industrial decarbonization is crucial, as it accounts for around a quarter of global energy-related CO2 emissions. Net-zero goals imply that deep decarbonization technologies must be deployed comprehensively across industry. Addressing this issue, Germany opened the first tender round for its carbon contracts for difference (CCfD) program (in German "Klimaschutzverträge") on March 12, 2024. The scheme is among the first large-scale programs worldwide to structurally support, drive technological learning in, and derisk innovative deep decarbonization projects in industry. It supports clean solutions in energy-intensive industries such as steel, chemicals, cement, glass, and pulp-and-paper but also cross-cutting applications such as process heat. The first auction has closed and is expected to conclude contracts worth up to 4 billion euros (i.e., the maximum total payments). The second auction may reach 19 billion euros, expected in 2024/2025, with further tenders planned.
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 | 0.005 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.057 | 0.044 |
| Insufficient payload (model declined to judge) | 0.026 | 0.008 |
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".