気候感応的・空間明示的な多目的間伐最適化：林分構造と生体バイオマス炭素のトレードオフ
Climate-sensitive, spatially explicit multi-objective thinning for stand-structure and living-biomass-carbon trade-offs (原題)
Pengcheng Ma, Zhifeng Guo, Zhirui Guo, Yue Pang, Zhidong Zhang
🤖 gxceed AI 要約
日本語
個体木成長モデル、近接性に基づく5つの空間指標、樹種別バイオマス式、モンテカルロ最適化を統合した空間明示的な多目的間伐フレームワークを開発した。中国北部のシラカンバ二次林、カラマツ混合林、カラマツ人工林15プロットに適用し、2025〜2030年をSSP1-2.6とSSP5-8.5で投影した。最適間伐強度は炭素保持目的で5.0〜12.0%、均衡目的で32.1〜41.0%、構造優先目的で44.2〜49.3%となり、管理目的間の差が気候経路間の差より大きいことを示した。
English
A spatially explicit multi-objective framework integrating individual-tree growth modeling, five neighborhood spatial metrics, species-specific biomass equations, and Monte Carlo optimization was developed for thinning decisions. Applied to 15 plots of birch, larch-birch mixed, and larch plantation forests in northern China under SSP1-2.6 and SSP5-8.5 (2025-2030), optimal thinning intensity rose from 5.0-12.0% (carbon-retention) to 32.1-41.0% (balanced) and 44.2-49.3% (structure-priority). Objective choice outweighed climate pathway in driving outcomes, with stand composition mediating climate effects.
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
森林吸収源は日本のGX推進（カーボンニュートラル戦略、J-クレジット森林分野）で重要だが、本論文は中国林業の施業最適化が主題で、日本の制度・企業実務への直接的接続は弱い。森林炭素の計測・モニタリング手法として参考価値はある。
In the global GX context
Forest-based removals are increasingly central to corporate net-zero and nature-related disclosure (SBTi FLAG, TNFD, CSRD E1/E4), yet this paper stays at silvicultural optimization without bridging to carbon accounting or disclosure frameworks. Its spatially explicit biomass modeling could inform MRV methodology for forest carbon credits.
👥 読者別の含意
🔬研究者:森林炭素と林分構造のトレードオフを空間明示的に最適化する手法として、吸収源モデリング研究者に有用。
🏢実務担当者:森林関連の炭素クレジットや自然資本開示を検討する企業にとって、施業と炭素保持の関係を理解する参考になる。
🏛政策担当者:森林吸収源政策やクレジット制度設計において、施業強度と炭素保持のトレードオフを考慮する根拠となりうる。
📄 Abstract（原文）
Thinning can improve stand spatial structure but removes living biomass carbon, creating a trade-off that may vary among stand types and future climate pathways. We developed a spatially explicit, climate-sensitive multi-objective computational framework that integrates an individual-tree growth model, five neighborhood-based spatial metrics, species-specific biomass equations, and Monte Carlo optimization. The framework was applied to 15 mapped plots comprising secondary Betula platyphylla forests, Larix principis-rupprechtii–B. platyphylla mixed forests, and L. principis-rupprechtii plantations in northern China. Stand development was projected from 2025 to 2030 under SSP1-2.6 and SSP5-8.5, and thinning was optimized under carbon-retention, balanced, and structure-priority objectives. Across stand types, the best-found thinning intensity increased from 5.0–12.0% under the carbon-retention objective to 32.1–41.0% under the balanced objective and 44.2–49.3% under the structure-priority objective. Mixed stands showed higher absolute spatial-structure improvement efficiency per unit of living biomass carbon removed than larch plantations. Under the balanced objective, SSP5-8.5 reduced best-found thinning intensity by 3.9 and 6.0 percentage points in secondary birch and larch stands, respectively, but increased it by 4.6 percentage points in mixed stands. Across the evaluated scenarios, variation among management objectives was greater than variation between climate pathways, while stand composition mediated the direction of climate effects. The framework provides spatially explicit, stand-specific decision support for evaluating thinning intensity and candidate harvest-tree locations while reconciling structural diversification with short-term living biomass carbon retention.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.3389/fpls.2026.1996132first seen 2026-11-06 04:47:10
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