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Study on Low-Carbon Optimization of Sustainable Aviation Fuel Supply Chain and Industry Cluster Layout in China

中国における持続可能な航空燃料サプライチェーンと産業クラスター配置の低炭素最適化に関する研究 (AI 翻訳)

Feiyin Wang, Wen-Kang Sui, Peng-Tao Wang, Mao Xu, Hui Li

Atmosphere📚 査読済 / ジャーナル2026-05-24#サプライチェーンOrigin: CN
DOI: 10.3390/atmos17060542
原典: https://doi.org/10.3390/atmos17060542

🤖 gxceed AI 要約

日本語

本研究は、サステナブル航空燃料（SAF）のサプライチェーンをデータ駆動型で最適化するフレームワークを提案。ライフサイクルアセスメント（LCA）と生成的敵対ネットワーク（GAN）を統合し、原料、精製所、空港、輸送手段などの経路を生成・評価する。その結果、構成により炭素強度が30%以上変動し、局所的な供給構造と輸送距離の短縮が排出削減に最も効果的であることを示した。

English

This study proposes a data-driven framework integrating LCA and GAN to optimize sustainable aviation fuel (SAF) supply chains. By generating and evaluating pathways with various feedstocks, refineries, airports, and transport modes, it finds that carbon intensity varies by over 30% across configurations, with localized supply and shorter transport distances being key to emission reduction.

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

日本でもSAF導入が急務とされる中、本論文はサプライチェーン全体の最適化手法を示しており、国産SAFの普及に向けた産業クラスター配置や輸送効率化の検討に示唆を与える。

In the global GX context

As global aviation seeks to meet CORSIA and net-zero targets, this paper presents a scalable method for SAF supply chain optimization, highlighting the importance of localized production and logistics—a key consideration for international climate policy and infrastructure planning.

👥 読者別の含意

🔬研究者:Provides a novel GAN+LCA framework for SAF pathway modeling that can be adapted to regional contexts.

🏢実務担当者:Offers insights into supply chain configuration for SAF projects, particularly the carbon impact of feedstock and transport choices.

🏛政策担当者:Informs aviation decarbonization policy by quantifying how supply chain structure affects life-cycle emissions.

📄 Abstract（原文）

Sustainable aviation fuel (SAF) is widely recognized as a critical pathway for aviation decarbonization; however, its life-cycle carbon performance is highly sensitive to supply chain configurations. This study proposes a data-driven framework integrating life-cycle assessment (LCA) with a generative adversarial network (GAN) to model and optimize SAF supply chain pathways under structural constraints. A rule-constrained synthetic dataset comprising feasible pathways is constructed, incorporating feedstock sources, refinery locations, airport demand nodes, conversion technologies, transport modes, and distances. Each pathway is encoded into a numerical feature vector, and a GAN model is trained to learn the distribution of feasible configurations. Generated pathways are further validated through LCA-based post-processing to ensure physical feasibility and emission consistency. The results show that pathway-level carbon intensity varies significantly across configurations, with differences exceeding 30% under varying feedstock–transport combinations. The model successfully captures the multimodal distribution of carbon emissions and identifies structurally consistent low-carbon pathways. In particular, localized supply structures and reduced transport distances are found to play a dominant role in minimizing emissions. This study provides a scalable methodological framework for SAF pathway modeling and offers insights into supply chain design and spatial configuration for achieving aviation carbon reduction targets.

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