Options, Costs and Strategies for CO2 Reductions in the European Power Sector
Bibliographic record
Abstract
Given its high share of total CO2 emissions power generation is a key sector for seeking CO2 reduction options. The purpose of this paper is to provide a power generator eye view of the European powersector'sCO2 compliance decision process under a mandatory emissions reduction program. The analysis indicates that in the medium term many European generators are likely to seriously consider options that are based on traditional power technologies such as converting existing coal-fired capacity to bum gas as well, extending the lives of nuclear capacity, and replacing old inefficient coal-fired plants with more efficient gas- or even coal-fired units. In the long-term the economic potential of future mitigation options are highly uncertain, and generators are likely to respond to this uncertainty by maintaining flexibility in fuel choices and avoiding large investments that lock them into a specific compliance method before new, more efficient technologies and fuels, have crystallized. Most notably, if the costs of carbon sequestration are expected to go down coal can be considered a sustainable energy source, and there may be weak incentives for generators to switch from coal to other fuels in the medium term. Given the multitude of possible CO2 mitigation options in the power sector, there is a strong case for emissions trading and for refraining from policies that build on mandatory fuel requirements, higher rates of capital stock turnover and technology standards.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".