Greenhouse gases emissions in liquified natural gas as a marine fuel: Life cycle analysis and reduction potential
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Substantial increases in shale gas production due to advances in hydraulic fracturing have created tremendous monetization and sustainable development opportunities, one of which is liquified natural gas (LNG). The International Maritime Organization (IMO) has targeted reducing greenhouse gas (GHG) emissions from shipping by 50% by 2050. Conventional shipping fuels currently used are heavy fuel oil (HFO) and marine gas/diesel oil (MGO/MDO). There has been growing interest in using LNG as a shipping fuel because of its competitive cost, availability, and the presence of bunkering infrastructure. A thorough literature review of LNG life cycle GHG emissions shows variation depending on the following factors: shale gas extraction, pretreatment, pipeline transportation distance, liquefaction plant capacity/technology, and ship propulsion system. Compared to conventional fuels, LNG can reduce life cycle emissions up to 18%. Incorporating renewables‐based power generation in liquefaction can reduce emissions by a further 5%–10% (renewable‐assisted LNG). The reduction potential and economic effects of this modification on LNG cost are examined and it is shown that low wind‐based electricity prices can make renewable‐assisted LNG competitive. A comprehensive understanding of the factors impacting LNG emissions help identify the current and future potential of LNG in reducing shipping industry emissions and providing a short‐term transitional fuel until it is supplanted with decarbonized fuels. This paper also uses water‐energy nexus to examine the impact of responsible water management on the carbon footprint of LNG.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it