Baseload Coal Investment Decisions under Uncertain Carbon Legislation
Bibliographic record
Abstract
More than 50% of electricity in the U.S. is generated by coal. The U.S. has large coal resources, the cheapest fuel in most areas. Coal fired power plants are likely to continue to provide much of U.S. electricity. However, the type of power plant that should be built is unclear. Technology can reduce pollutant discharges and capture and sequester the CO2 from coal-fired generation. The U.S. Energy Policy Act of 2005 provides incentives for large scale commercial deployment of Integrated Coal Gasification Combined Cycle (IGCC) systems (e.g., loan guarantees and project tax credits). This analysis examines whether a new coal plant should be Pulverized Coal (PC) or IGCC. Do stricter emissions standards (PM, SO2, NOx, Hg) justify the higher costs of IGCC over PC? How does potential future carbon legislation affect the decision to add carbon capture and storage (CCS) technology? Finally, can the impact of uncertain carbon legislation be minimized? We find that SO2, NOx, PM, and Hg emission standards would have to be far more stringent than twice current standards to justify the increased costs of the IGCC system. A C02 tax less than $29/ton would lead companies to continuing to choose PC, paying the tax for emitted CO2. The earlier a decision-maker believes the carbon tax will be imposed and the higher the tax, the more likely companies will choose IGCC w/CCS. Having government announce the date and level of a carbon tax would promote more sensible decisions, but government would have to use a tax or subsidy to induce companies to choose the technology that is best for society.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".