Sustainable power system transition pathways: Regional decarbonization and resource conservation aided by small modular reactors
Bibliographic record
Abstract
Clean energy technologies offer promising pathways for low-carbon transitions, yet their feasibility remains uncertain, particularly in rapidly developing regions. This study develops a Factorial Multi-Stochastic Optimization-driven Equilibrium (FMOE) model to assess the economic and environmental impacts of clean power deployment. Using Small Modular Reactors (SMRs) in Guangdong, China, as a case study, the model reveals that SMRs can reduce system costs and alleviate GDP losses, supporting provincial-level Nationally Determined Contributions (NDCs). If offshore wind capital costs fall to 40 % of SMRs’, SMR deployment may no longer be necessary after 2030. Otherwise, SMRs could supply 22 % of capacity by 2040. The FMOE model provides a robust, adaptable framework for evaluating emerging technologies under uncertainty and supports sustainable power planning across diverse regional contexts. This study offers valuable insights into the resource and economic implications of clean energy strategies, contributing to global carbon neutrality and efficient energy system design.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".