Country-Level Life Cycle Assessment of Greenhouse Gas Emissions from Liquefied Natural Gas Trade for Electricity Generation
Bibliographic record
Abstract
In the determination of the net impact of liquefied natural gas (LNG) on greenhouse gas emissions, life cycle assessments (LCA) of electricity generation have yet to combine the effects of transport distances between exporting and importing countries, country-level infrastructure in importing countries, and the fuel sources displaced in importing countries. To address this, we conduct a LCA of electricity generated from LNG export from British Columbia, Canada with a three-step approach: (1) a review of viable electricity generation markets for LNG, (2) the development of results for greenhouse gas emissions that account for transport to importing nations as well as the infrastructure required for power generation and delivery, and (3) emissions displacement scenarios to test assumptions about what electricity is being displaced in the importing nation. Results show that while the ultimate magnitude of the greenhouse gas emissions associated with natural gas production systems is still unknown, life cycle greenhouse gas emissions depend on country-level infrastructure (specifically, the efficiency of the generation fleet, transmission and distribution losses and LNG ocean transport distances) as well as the assumptions on what is displaced in the domestic electricity generation mix. Exogenous events such as the Fukushima nuclear disaster have unanticipated effects on the emissions displacement results. We highlight national regulations, environmental policies, and multilateral agreements that could play a role in mitigating 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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".