Life cycle assessment of a hypothetical Canadian pre-combustion carbon dioxide capture process system
Bibliographic record
Abstract
The methodology of life cycle assessment was applied for evaluating the environmental performance of a Saskatchewan lignite integrated gasification combined cycle (IGCC)-based electricity generation plant with and without the pre-combustion CO2 capture process. A comparison between the IGCC systems (with and without CO2 capture) and the competing lignite pulverized coal electricity generating station was conducted to reveal which technology offers more positive environmental effects. The results showed significant reduction of GHG emissions where both post- and pre-combustion CO2 capture processes are applied. With the application of the CO2 removal technology, GHG emissions were reduced by 27–86%. The performances of the IGCC systems were superior to those of the pulverized coal systems. However, in terms of other environmental impacts, multiple environmental trade-offs are involved depending on the capture technology. For the post-combustion CO2 capture process system, it was observed that the environmental impact was shifted from the air compartment to the soil and water compartments. The IGCC systems showed the same tendency of shifting from air pollution to soil and water pollution, but the amount of pollution is less significant. This is likely because the IGCC system operates at higher efficiencies; hence, it requires less fuel and produces fewer emissions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".