Environmental Implications of United States Coal Exports: A Comparative Life Cycle Assessment of Future Power System Scenarios
Bibliographic record
Abstract
Stricter emissions requirements on coal-fired power plants together with low natural gas prices have contributed to a recent decline in the use of coal for electricity generation in the United States. Faced with a shrinking domestic market, many coal companies are taking advantage of a growing coal export market. As a result, U.S. coal exports hit an all-time high in 2012, fueled largely by demand in Asia. This paper presents a comparative life cycle assessment of two scenarios: a baseline scenario in which coal continues to be burned domestically for power generation, and an export scenario in which coal is exported to Asia. For the coal export scenario we focus on the Morrow Pacific export project being planned in Oregon by Ambre Energy that would ship 8.8 million tons of Powder River Basin (PRB) coal annually to Asian markets via rail, river barge, and ocean vessel. Air emissions (SOx, NOx, PM10 and CO2e) results assuming that the exported coal is burned for electricity generation in South Korea are compared to those of a business as usual case in which Oregon and Washington's coal plants, Boardman and Centralia, are retrofitted to comply with EPA emissions standards and continue their coal consumption. Findings show that although the environmental impacts of shipping PRB coal to Asia are significant, the combination of superior energy efficiency among newer South Korean coal-fired power plants and lower emissions from U.S. replacement of coal with natural gas could lead to a greenhouse gas reduction of 21% in the case that imported PRB coal replaces other coal sources in this Asian country. If instead PRB coal were to replace natural gas or nuclear generation in South Korea, greenhouse gas emissions per unit of electricity generated would increase. Results are similar for other air emissions such as SOx, NOx and PM. This study provides a framework for comparing energy export scenarios and highlights the importance of complete life cycle assessment in determining net emissions effects resulting from energy export projects and related policy decisions.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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".