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伊洛河流域における土地利用変化が植生の総炭素固定量に与える影響

Effects of Land Use Change on Vegetation Total Carbon Capture in the Yiluo River Basin (原題)

Mingjie Yang, Peng Zhang, Yuanhong Liu, Lianqing Xue, Tao Lin

Land📚 査読済 / ジャーナル2026-10-03#気候科学Origin: CN対象セクター: agriculture
DOI: 10.3390/land15101863
原典: https://doi.org/10.3390/land15101863

🤖 gxceed AI 要約

日本語

中国・伊洛河流域を対象に、SWATモデルと光利用効率（LUE）モデルを統合し、2000年と2020年の土地利用変化が植生の総炭素固定（GPP）に与える影響を解析した。農地から森林・草地への転換はLUEを高め年平均GPPを59.03%・29.62%増加させた一方、未利用地の都市・工業用地化は25.39%減少させた。温度制約がGPP変動の主要因であることを示し、生態系回復と土地利用最適化、地域カーボンニュートラル治理に示唆を与える。

English

Integrating SWAT and a light use efficiency (LUE) model, this study analyzes how 2000–2020 land use change affected vegetation gross primary productivity (GPP) in China's Yiluo River Basin. Farmland-to-forest and farmland-to-grassland conversions raised annual GPP by 59.03% and 29.62%, while conversion of unused land to urban/industrial use cut it by 25.39%. Temperature constraint was the dominant driver of GPP variation, informing ecological restoration and regional carbon-neutral governance.

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

日本企業のGX開示に直接資する内容ではないが、土地利用・生態系炭素吸収の定量評価手法は、SBTiのFLAG（森林・土地・農業）目標や自然関連財務情報開示（TNFD）を検討する日本企業にとって参考となる。流域単位の炭素収支モデリングは、Scope 3上流の土地利用由来排出・吸収評価にも示唆を与える。

In the global GX context

This basin-scale study sits at the science base of land-sector carbon accounting, relevant to emerging FLAG (Forest, Land and Agriculture) target-setting under SBTi and to TNFD nature-related disclosure. It offers a modeling template for quantifying how land use transitions alter carbon capture, though it does not engage TCFD/ISSB disclosure frameworks directly.

👥 読者別の含意

🔬研究者:SWATとLUEモデルを統合し、水熱制約を通じた土地利用変化のGPPへの影響経路を定量化した手法が参考になる。

🏢実務担当者:FLAG目標やTNFD対応で土地利用由来の炭素吸収を評価する際の流域モデリング手法として活用可能。

🏛政策担当者:農地転換と都市拡張が炭素固定に与える影響を定量化し、土地利用計画と地域カーボンニュートラル政策の根拠を提供する。

📄 Abstract（原文）

Land use change is a critical anthropogenic factor influencing hydrological processes and vegetation carbon capture in watersheds. Few studies have integrated outputs from the Soil and Water Assessment Tool (SWAT) model with a light use efficiency (LUE) model to elucidate the pathways through which land use change modulates gross primary productivity (GPP) via water and temperature limiting factors. This study focuses on the Yiluo River Basin in the Middle Yellow River and conducts SWAT scenario simulations for 2000 and 2020. By integrating the LUE model, this study analyzed the spatiotemporal variations of GPP and clarified the mechanisms of land use change affecting total vegetation carbon capture in the basin. The results indicate that land use changes in the Yiluo River Basin during 2000–2020 were primarily characterized by the conversion of farmland to forest and grassland, as well as urban expansion. The temperature constraint on maximum LUE constituted the primary driver of spatiotemporal variations in basin GPP. Farmland-to-forest and farmland-to-grassland conversions significantly improved LUE, increasing the average annual GPP by 59.03% and 29.62%, respectively. Conversely, the conversion of unused land to urban, industrial, mining, and residential land reduced vegetation cover and LUE, resulting in a 25.39% decrease in average annual GPP. Vegetation carbon capture exhibits obvious seasonal variations, with maximum GPP in summer and the lowest values in winter driven by vegetation dormancy. This study clarifies how hydrothermal factors modulate vegetation carbon capture under land use change, offering support for ecological restoration, land use optimization, and regional carbon-neutral governance across the Middle Yellow River Basin.

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