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Soil Carbon Dynamics and Greenhouse Gas Reduction Potential of Arundo donax-Based Sustainable Aviation Fuel in China’s Bohai Rim Region

中国渤海沿岸地域におけるArundo donaxベースの持続可能な航空燃料の土壌炭素動態と温室効果ガス削減可能性 (AI 翻訳)

Wei Li, J. Li, Xinyuan Wang, Zongwei Zhang

Sustainability📚 査読済 / ジャーナル2026-04-13#再生可能エネルギーOrigin: CN
DOI: 10.3390/su18083848
原典: https://doi.org/10.3390/su18083848
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🤖 gxceed AI 要約

日本語

本研究は、中国渤海沿岸地域の塩類アルカリ土壌で栽培したArundo donax由来の持続可能な航空燃料(SAF)について、CENTURYモデルとLSTM時系列予測を統合し、土壌炭素貯留(SCS)とライフサイクルGHG排出を評価した。その結果、平均SCS速度は0.032 t C/ha/年、総SCSは0.615 Tg Cと推定され、Fischer-Tropsch SAFは炭素ネガティブ(-32.1 g CO2e/MJ)となる可能性が示された。ただし、この結果はシナリオベースの探索的評価である。

English

This study integrates the CENTURY model with LSTM time series forecasting to assess long-term soil carbon sequestration (SCS) and life-cycle GHG emissions of Arundo donax-based SAF in China's Bohai Rim. Estimated mean SCS rate is 0.032 t C/ha/yr, total SCS 0.615 Tg C, and Fischer-Tropsch SAF could achieve net-negative emissions (-32.1 g CO2e/MJ, 136.1% reduction vs fossil fuel). Results are exploratory and scenario-based.

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導入が進む中、塩類アルカリ土壌でのエネルギー作物栽培と炭素貯留の可能性を示す事例として参考になる。ただし、中国特有の条件に基づくため直接適用には注意が必要。

In the global GX context

This study provides a novel integration of LSTM and CENTURY models to assess soil carbon dynamics for SAF feedstock, offering insights for global marginal land utilization and carbon-negative aviation strategies. However results are scenario-based and region-specific.

👥 読者別の含意

🔬研究者:LSTMとCENTURYモデルの統合手法は、土壌炭素動態の長期予測に応用可能であり、気候シナリオに依存する不確実性の定量化にも貢献する。

🏢実務担当者:SAF生産企業は、原料栽培による炭素クレジットの可能性を検討する際に本評価手法を参考にできる。

🏛政策担当者:土地利用政策と航空脱炭素戦略の連携を検討する際、塩類アルカリ土壌の活用と炭素貯留の相乗効果を考慮する根拠となる。

📄 Abstract(原文)

The development of bioenergy crops on saline–alkaline land has been recognized as a potential pathway for both land restoration and combating global warming. However, the role of soil organic carbon (SOC) dynamics under such conditions remains insufficiently quantified in long-term assessments. In this study, an exploratory assessment was conducted to evaluate the long-term soil carbon sequestration (SCS) potential and life-cycle greenhouse gas (GHG) emissions of sustainable aviation fuel (SAF) produced from Arundo donax in the Bohai Rim region of China. The CENTURY model was integrated with Long Short-Term Memory (LSTM) time series forecasting to simulate SOC dynamics under future climate scenarios (2024–2035). Compared with the original CENTURY simulation, the LSTM model yielded a substantially more conservative estimate of SOC accumulation, with an Ensemble Mean SCS rate of 0.032 t C/ha/a and a 95% confidence interval ranging from −0.079 to 0.143 t C/ha/a. This result indicates a positive regional average tendency toward soil carbon sequestration, while also suggesting that some locations may behave as carbon sources under less favorable climatic conditions. The total SCS potential across the study area was estimated at 0.615 Tg C. When these soil carbon benefits were incorporated into the life-cycle assessment of Fischer–Tropsch (F-T) SAF, the pathway could become potentially net-negative under the adopted assumptions, reaching −32.1 g CO2e/MJ, which corresponds to a potential reduction of 136.1% relative to fossil aviation fuel. These results should be interpreted as exploratory and scenario-based, given that large-scale cultivation of Arundo donax has not yet been established in the Bohai Rim region and the assessment therefore relies on assumptions. Beyond GHG mitigation, the cultivation of Arundo donax on degraded saline–alkaline soils may also have potential relevance to broader sustainability objectives, including SDG 13 (Climate Action) and SDG 15 (Life on Land). These findings highlight the possible synergies among energy crop cultivation, soil restoration, and climate neutrality goals, and provide preliminary insights for integrating marginal land utilization into sustainable land management and low-carbon aviation strategies.

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gxceed は公開メタデータに基づく研究支援データセットです。要約・翻訳・解説は AI 支援で生成されています。 最終的な解釈・検証は利用者が原典資料に基づいて行うことを前提とします。