Investigating Long-Term Commitments to Replace Electricity Generation with SMRs and Estimates of Climate Change Impact Costs Using a Modified VENSIM Dynamic Integrated Climate Economy (DICE) Model
Bibliographic record
Abstract
During the last few years, nuclear energy has received great attention due to the increase in climate change awareness. According to the Paris agreement, global temperature is to be kept below 2 °C and preferably below 1.5 °C by 2050. This approach has been substantially confirmed in the recent COP 26 in Glasgow. This research investigates the effects of integrating SMR nuclear power plants (small modular reactors) into the Nordhaus Dynamic Integrated Climate Economy (DICE) model for reducing the CO2 emissions in the atmosphere by substituting all existing fossil-fueled power plants (FPPP). The software is based on the VENSIM dynamic systems modeling platform. Simulations were carried out from the year 2019 to 2100 using 10-year increments. Several scenarios were thus simulated replacing roughly 70,000 FPPPs operating at this time in the world. Simulations indicate a CO2 reduction of approximately 12.63% relative to the initial conditions used and using 87,830 SMR core units of 80 MWe electric each to meet such demand. The DICE model further predicts the cost of climate damage impacting the upper ocean and atmospheric temperatures, and the deep ocean temperature as USD 1.515 trillion (US Dollar; (US) trillion = 1,000,000,000,000 (1 × 1012)) by the end of this century. From a modified section of the model, a cost of USD 1.073 trillion is predicted as the toll on human health costs. This is thus equal to a USD 2.59 trillion loss in the economy.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".