A global analysis of climate-driven reversal risks in forests
森林における気候駆動の逆転リスクの世界的分析 (AI 翻訳)
Chao Wu, Michael L. Goulden, James T. Randerson, Anna T. Trugman, Jonathan Wang, Linqing Yang, Nezha Acil, Susan C. Cook‐Patton, Danny Cullenward, Steven J. Davis, Christopher A. Williams, William R. L. Anderegg
🤖 gxceed AI 要約
日本語
本研究は、衛星データ、攪乱モデル、機械学習を用いて、気候変動下での森林炭素貯蔵の長期的な逆転確率を初めて空間明示的にマッピングした。北米針葉樹林、熱帯雨林、アジアの亜熱帯乾燥林が最もリスクが高く、100年間の逆転確率は31-42%である。森林ベースの気候ソリューションの効果を最大化し、炭素クレジットのバッファープール設計に貢献する。
English
Using satellite data, disturbance modeling, and machine learning, this study provides the first spatially explicit maps of long-term carbon loss probability in global forests under climate scenarios. North American conifer, tropical rainforests, and Asian subtropical dry forests face highest risks; globally, reversal probability is 31-42% over 100 years. The findings inform strategic project placement and buffer pool mechanisms for forest-based carbon credits.
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Jクレジットや自主的オフセットを通じた森林炭素クレジットの活用を拡大している。本論文のグローバルな逆転リスクマップは、日本企業やプロジェクト開発者が高 integrity な森林オフセットを選択する際の重要な判断材料となる。
In the global GX context
This paper provides the first global spatially explicit assessment of long-term reversal risks in forest carbon storage, directly supporting the integrity of carbon credits under Article 6 and voluntary markets. It is essential reading for stakeholders in nature-based solutions and carbon markets.
👥 読者別の含意
🔬研究者:Offers a global baseline for forest carbon storage risk assessment and a methodology combining satellite data and machine learning that can be extended to other ecosystems.
🏢実務担当者:Provides actionable maps for selecting forest carbon project locations and determining appropriate buffer pool sizes to account for reversal risks.
🏛政策担当者:Highlights the need for standardized reversal-risk accounting in carbon credit frameworks and can inform international standards such as those under Article 6 of the Paris Agreement.
📄 Abstract（原文）
The integrity of forest-based climate solutions and carbon credits requires persistent carbon storage, but climate change is increasing the risk of natural disturbances that release carbon back into the atmosphere. Using global satellite data, disturbance modeling, and machine learning, we provide the first spatially explicit and scenario-based maps of long-term probability of carbon loss in global forests under different disturbance severities and climate scenarios. We find that North American conifer forests, tropical rainforests, and Asian (sub)tropical dry forests face the greatest risks, and that Eurasian temperate forests, African (sub)tropical dry forests face the lowest. Globally, the likelihood of reversals over 100 years is 31%-42% across all scenarios. Our work helps to maximize the benefits of forest-based climate solutions by informing more strategic project placement and more robust reversal-risk compensation mechanisms, such as buffer pools, and highlights critical additional science to better understand and manage risks of these essential climate solutions.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.64898/2026.06.19.733404first seen 2026-06-24 05:02:06
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