A hopeless pursuit? National efforts to promote small modular nuclear reactors and revive nuclear power
Bibliographic record
Abstract
Abstract Nuclear power plant construction has historically been challenged by problems of high cost, cost escalation, and construction delays. The newest set of large reactor projects have also been overbudget and overtime. This has prompted interest in new reactor technologies that proponents claim would not suffer these problems, specifically small modular reactors (SMRs), a class that encompasses a wide range of technologies. This article examines national efforts in three countries, Canada, the UK, and the United States, which are pursuing SMRs vigorously and where the government has funded their development generously. We compare the different strategies and foci of these national strategies, analyzing the various forms of support offered by the separate agencies of the government, and the private companies that are trying to develop SMRs. We also offer an overview of the different types of reactor technologies being pursued in these different countries. Following these, we outline the main challenge confronting SMR technologies: their ability to generate electricity in an economically competitive manner, highlighting the problems resulting from economies of scale being lost. By examining the experience so far, we find that even designs based on well‐tested technology cannot be deployed till after 2030 and the more radical designs might never be. This article is categorized under: Policy and Economics > Research and Development Policy and Economics > Regional and International Strategies Energy and Power Systems > Energy Infrastructure
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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 | 0.009 | 0.007 |
| 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 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".