ALSTOM SUPPLIES GAS TURBINE TO TURKMENISTAN
Bibliographic record
Abstract
China relies heavily on coal for power generation, and the demand for coal in a country of this size makes China the world's largest carbon dioxide emitter; hence China is pursuing greener pathways for power generation. Importing shale gas in the form of LNG from Canada is one such pathway. It starts with the recovery of shale gas in Canada and its export to China. This paper quantifies well-to-wire (WTW) greenhouse gas (GHG) emissions per kilowatt hour (kWh) of Canadian shale gas-fuelled electricity in China through models. WTW emissions include emissions from recovery, processing, transmission, liquefaction, marine shipping, re-gasification, power plant operations, and electricity transmission and distribution. Four Canadian shale gas reserves – Montney, Horn River, Liard, and Cordova – are considered. The results show that the WTW GHG emissions of Canadian shale gas-fired combined cycle technology range from 567 to 610 gCO2/kWh (57–62% of the GHG emissions from China's present coal-fired electricity), and total well-to-port (WTP) GHG emissions (emissions from recovery, processing, and transmission to a liquefaction facility) range from 7.68 to 13.4 gCO2e/MJ. Sensitivity analysis results show that venting emissions during raw gas processing, flaring rates during well completion, and lifetime productivity of the gas significantly influence WTP 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.192 | 0.040 |
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".