Global liquefied natural gas expansion exceeds demand for coal-to-gas switching in paris compliant pathways
Bibliographic record
Abstract
Abstract The shift from coal to natural gas in the power sector has led to significant reductions in carbon emissions. The shale revolution that led to this shift is now fueling a global expansion in liquefied natural gas (LNG) export infrastructure. In this work, we assess the viability of global LNG expansion to reduce global carbon emissions through coal-to-gas switching in the power sector under three temperature targets—Paris compliant 1.5 °C and 2 °C, and business-as-usual 3 °C. In the near to medium term (pre-2035), LNG-derived coal-to-gas substitution reduces global carbon emissions across all temperature targets as there is significantly more coal power generation than the LNG required to substitute it. However, we find that long-term planned LNG expansion is not compatible with the Paris climate targets of 1.5 °C and 2 °C—here, the potential for emissions reductions from LNG through coal-to-gas switching is limited by the availability of coal-based generation. In a 3 °C scenario, high levels of coal-based generation through mid-century make LNG an attractive option to reduce emissions. Thus, expanding LNG infrastructure can be considered as insurance against the potential lack of global climate action to limit temperatures to 1.5 °C or 2 °C. In all scenarios analyzed, low upstream methane leakage and high coal-to-gas substitution are critical to realize near-term climate benefits. Large-scale availability of carbon capture technology could significantly extend the climate viability of LNG. Investors and governments should consider stranded risk assets associated with potentially shorter lifetimes of LNG infrastructure in a Paris-compatible world.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".