Probabilistic, Measurement-Informed Greenhouse Gas Emissions from Global Liquefied Natural Gas Supply Chains Reveal Wide Country-Level Variation
確率的かつ実測に基づく世界の液化天然ガスサプライチェーンからの温室効果ガス排出量：国別の大きなばらつきを明らかに (AI 翻訳)
Haoming Ma, Yuanrui Zhu, Wennan Long, Mohammad Masnadi, Garvin Heath, Paul Balcombe, Fiji George, Selina Roman-White, Arvind Ravikumar
🤖 gxceed AI 要約
日本語
本論文は、世界のLNG貿易の90%以上をカバーする確率的・地理空間的・実測情報に基づくライフサイクルアセスメントモデルを開発し、サプライチェーン全体のGHG排出原単位を推定した。その結果、カタールの8.6 g CO2e/MJからアルジェリアの39 g CO2e/MJ以上まで約5倍の幅があることが判明。実測データを組み込まなかった従来の推定と比較して、世界のLNG貿易の排出原単位が最大31%過小評価されている可能性を示した。また、排出原単位の分布は裾野が重く、発生確率は低いが排出量の多いサプライチェーンの重要性を明らかにした。
English
This paper develops a probabilistic, geospatial, and measurement-informed life cycle assessment model covering over 90% of global LNG trade to estimate GHG emissions intensity (EI) of supply chains. It finds a ~5x range in EI from 8.6 g CO2e/MJ in Qatar to over 39 g CO2e/MJ in Algeria. The study shows that supply chain-weighted EI is underestimated by up to 31% compared to prior estimates without measurements. The heavy-tailed distribution highlights the importance of low-probability, high-emitting supply chains, emphasizing the need for direct measurements to avoid underestimation and support target-based methane policies.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
日本はLNGの主要輸入国であり、本論文の知見は日本のLNG調達における排出量算定の精度向上に直結する。特に、実測データを活用したサプライチェーン評価は、SSBJや有報でのGHG開示においてScope 3排出量の信頼性を高める手法として参考になる。
In the global GX context
This paper provides critical evidence for global methane reduction policies and voluntary initiatives (e.g., OGMP 2.0) by demonstrating that activity-based inventories significantly underestimate LNG supply chain emissions. The probabilistic approach and country-level variation offer actionable insights for investors and regulators assessing transition risk in LNG assets, aligning with ISSB and TCFD recommendations for scenario analysis and measurement-based disclosure.
👥 読者別の含意
🔬研究者:Provides a robust probabilistic LCA framework integrating direct measurements, useful for improving GHG accounting methodologies in natural gas supply chains.
🏢実務担当者:Highlights the need for measurement-informed supply chain assessments to avoid underestimation of emissions, relevant for corporate Scope 3 reporting and methane reduction targets.
🏛政策担当者:Demonstrates that target-based methane policies require direct measurements to be effective; offers country-level benchmarks for regulation and international negotiations.
📄 Abstract（原文）
Growth in liquefied natural gas (LNG) demand has been accompanied by debates about its greenhouse gas (GHG) emissions impact. Studies have shown that activity-based inventory accounting methods in the oil and gas sector significantly underestimates methane emissions. As a result, target-based policies and voluntary initiatives are moving towards adopting direct measurements to assess the GHG emissions intensity (EI) of LNG supply chains. Yet, most supply chain assessments of LNG do not incorporate measurement data. In this work, we develop a probabilistic, geospatial, and measurement informed life cycle assessment model to estimate the GHG EI of global LNG supply chains covering over 90% of LNG trade. We find a ~5x range in supply chain GHG EI from 8.6 g CO2e/MJ in Qatar to over 39 g CO2e/MJ in Algeria. Overall, our work suggests supply chain-weighted GHG EI of global LNG trade is underestimated by up to 31% compared to prior estimates that did not incorporate measurements. Probabilistic models of GHG EI of LNG exhibit a heavy tailed distribution, revealing the importance of low likelihood but high emitting supply chains. Incorporating direct measurements in supply chain assessments is necessary to avoid underestimation from activity-based inventories and increase confidence in target-based policies to address methane.
🔗 Provenance — このレコードを発見したソース
- openaire https://doi.org/10.26434/chemrxiv-2025-5tjjwfirst seen 2026-05-05 19:07:55
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