R<scp>esource</scp>U<scp>se under</scp>C<scp>limate</scp>S<scp>tabilization</scp>: C<scp>an</scp>N<scp>uclear</scp>P<scp>ower</scp>P<scp>rovide</scp>C<scp>lean</scp>E<scp>nergy</scp>?
Bibliographic record
Abstract
Abstract The long‐term goal of the Intergovernmental Panel on Climate Change (IPCC) is the stabilization of carbon concentration in the atmosphere. In this paper, we impose a carbon target concentration on a partial equilibrium model of the global energy sector. Specifically, we ask whether nuclear power can provide carbon‐free energy as fossil fuel resources become costly due to scarcity and externality costs. We find that nuclear power can reduce the cost of generating clean energy significantly and relatively quickly. However, beyond a few decades the role of nuclear power may be considerably reduced as uranium becomes scarce and renewables become economical. The cost of carbon when nuclear power supplies a significant share of energy is much lower than that of other studies. A policy implication is that current political and regulatory impediments to the expansion of nuclear generation may prove to be costly if large volumes of clean energy need to be supplied over a relatively short period of time.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.459 | 0.206 |
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".