Greenhouse Gas Estimates of LNG Exports Must Include Global Market Effects
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide We conduct a consequential lifecycle analysis (LCA) of greenhouse gas (GHG) emissions from North American liquefied natural gas (LNG) export projects, estimating the change in global natural gas and coal use resulting from the market effects of increased LNG trade. We estimate that building a 2.1 billion cubic feet per day (Bcfd) LNG export facility, equivalent to one of the larger LNG projects under development in the US today, will change global GHG emissions −39 to 11 Mt CO 2 e (90% range) with a median value of −8 Mt CO 2 e. Previous attributional LCA methods for electricity generation with LNG replacing coal find a much larger benefit of LNG exports, a median value of −36 Mt CO 2 e for this size project. The smaller decrease in GHGs is attributable to higher domestic coal use and a smaller decrease in international coal use than assumed by previous methods. Net global emission change estimates are most sensitive to the uncertainty in economic elasticities outside of North America. Given the scale of planned and proposed LNG export terminals, project regulators and policymakers must account for market effects to more accurately estimate the global net change in GHG 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".