Unlocking gas demand: the next wave of global LNG supply as a catalyst to drive Asia’s shift away from coal
Bibliographic record
Abstract
Global LNG liquefaction capacity at the end of 2023 stood at 490 Mtpa, with an 83% utilisation rate. The commissioning of new liquefaction capacity hit a multi-year low of 4 Mtpa in 2023, however, in 2024, new liquefaction capacity is expected to rise by 16 Mtpa. From 2025 onwards, global capacity is projected to surge by more than 40% during the remainder of this decade from projects that have already reached a final investment decision. This surge could result in a medium-term oversupply in the LNG market, putting downward pressure on spot LNG prices. However, this additional supply could stimulate global natural gas demand, particularly in price-sensitive Asian markets, helping to stabilise the global gas market. Between 2024 and 2029, global liquefaction capacity is projected to increase by 230 Mtpa, nearly 50% higher than the capacity at the end of 2023. Approximately 50 Mtpa of liquefaction capacity is expected to be commissioned annually between 2025 and 2028, marking a record high for capacity additions. The US is anticipated to account for 35% of this new capacity, followed by Qatar (21%), Russia (14%), and Canada (8%). Assuming global LNG exports from existing liquefaction terminals remain at 2023 levels and new terminals operate at 95% capacity, global LNG exports could reach 620 Mt by the end of the decade. This estimate excludes supply from liquefaction projects that may take FID in the near term and become operational by 2030. There is a significant risk that the expected strong increase in global LNG exports could lead to a decline in global spot LNG prices in the medium term, as supply growth may outpace demand growth. As seen in previous market cycles, a potential decline in spot LNG prices could stimulate demand in price-sensitive Asian markets, particularly in China and South/Southeast Asia. This could encourage a shift away from more polluting energy sources, such as coal and liquid petroleum products, towards cleaner natural gas. In Asia’s primary energy mix, coal dominates with a 51% share, followed by oil at 23%, while natural gas accounts for only 11%. Similarly, in the electricity mix, coal holds the largest share of 57%, while hydroelectric and natural gas each account for just 12% and 10%, respectively. The dominance of coal in Asia's primary energy mix and electricity sector highlights the pivotal role natural gas can play in the region's energy transition. To fully leverage the anticipated decline in global spot LNG prices and stimulate natural gas demand in Asia, the development of natural gas infrastructure and supportive regulatory frameworks are essential. The following short- to medium-term strategies could be considered: • Develop LNG import terminals, particularly floating storage regasification units (FSRUs). • Expand domestic pipeline infrastructure to enhance natural gas accessibility. • Cap electricity production from coal, replacing it with natural gas. • Implement carbon pricing to create a level playing field between coal and natural gas.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".