Modifying the EPA’s New Power Plant Rules to Eliminate Unnecessary Reliability Risks
Bibliographic record
Abstract
W hen the Environmental Protection Agency (EPA)proposed new rules 1 governing emissions from coal and natural gas power plants on May 11, 2023, it was the federal government's third attempt (along with the Clean Power Plan and the Affordable Clean Energy rule) in the past decade to reduce carbon dioxide (CO 2 ) emissions by requiring changes at existing coal and natural gas power plants.Historically, the United States has used nuclear power, coal, and natural gas�sources that can be operated on demand, without regard for whether the wind is blowing or the sun is shining�to generate reliable electricity.The 2030 decade, however, is expected to see a sizable portion of the nation's aging nuclear power plants reach retirement age. 2 Now, without careful modification, the EPA's new proposal would push many existing coal and natural gas generators to retire as early as 2030, imperiling grid reliability.Although we write primarily about the United States, we note a similar dynamic is playing out around the world.Countries such as Canada, 3 the United Kingdom, 4,5 Australia, 6 and China [7][8][9] have also taken recent steps to incentivize CO 2 reduction beyond renewable energy using both market-based and regulatory approaches.These efforts are increasingly being made mindful of the impact of carbon-reduction mandates on electric-system reliability and in light of the fact that geopolitical issues in Ukraine have snarled energy supply chains in many parts of the world.Renewable energy sources, such as wind and solar power, have become increasingly cost-effective on their own and emit no CO 2 , rightfully securing them a place at the table.Society, however, values reliability in electricity generation, and renewables�however clean and cost-effective�are simply not reliable without costly battery storage systems as backup, 10 without employing clean hydrogen, 11 or without greatly expanding the transmission grid.12 When people flip on their light switches, they expect the power to be there.How, then, are we to maintain reliability while reducing CO 2 ?Let us tackle one source of conflict up front: the reality is that the country's coal power plants are aging out.Roughly one-third of U.S. coal-generating capacity has already retired over the past 20 years (for economic, environmental, and physical reasons), and the remaining coal power plants are, on
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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 | 0.013 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".