Predictive analytics for climate-resilient timber supply chains: Integrating anomaly detection with carbon sequestration and risk scoring
気候変動に強い木材サプライチェーンのための予測分析：異常検知と炭素隔離・リスクスコアリングの統合 (AI 翻訳)
Emmanuel Hagan, Laurence Akakpo, Tadiwa Lennon Kasuwa, Tariro Lyan Nhemachena, Takudzwa Taanisa, Eric Wononuo Osman, Trish Tsveta, Munashe Naphtali Mupa
🤖 gxceed AI 要約
日本語
本論文は、気候変動下での木材サプライチェーンの強靭性と持続可能性を高めるため、予測分析、異常検知、炭素リスクスコアリングを統合する手法を提案する。1992～2020年の全球森林・炭素データを用い、ランダムフォレストによる予測モデルを構築し、炭素ストックや森林面積の異常を検出。さらに、調達地域の持続可能性リスクを評価する炭素リスクスコアリングシステムを設計した。
English
This paper proposes integrating predictive analytics, anomaly detection, and carbon risk scoring to enhance resilience and sustainability of timber supply chains under climate change. Using 1992-2020 global forest and carbon data, a Random Forest model predicts disruption, anomaly detection identifies deviations in carbon stocks and forest area, and a carbon risk scoring system evaluates sourcing regions' sustainability risks.
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
日本の木材サプライチェーンにおいても、気候変動によるリスク評価と炭素隔離の可視化は重要である。本論文の手法は、日本企業の森林管理や木質バイオマス調達における炭素アカウンティング・リスク管理に応用可能であり、特にサプライチェーン上のスコープ3排出量算定への示唆を含む。
In the global GX context
This paper contributes to global efforts in climate-resilient supply chain management by integrating carbon sequestration metrics with predictive analytics. While timber supply chains are sector-specific, the methodology for anomaly detection and risk scoring can be adapted to other agricultural or forestry-based value chains, relevant for ISSB and CSRD disclosure requirements.
👥 読者別の含意
🔬研究者:Researchers in supply chain analytics and carbon accounting will find a novel integration of anomaly detection with carbon risk scoring for forest-based supply chains.
🏢実務担当者:Corporate sustainability teams in forestry or timber industries can apply the risk scoring system for sourcing decisions and carbon footprint reporting.
📄 Abstract（原文）
This article examines the problem of applying predictive analytics, anomaly detection, and carbon risk scoring to make timber supply chains more resilient and sustainable in the climate change context. On a predictive model of possible disruption of atmospheric characteristics caused by climate-related outcomes like extreme weather and deforestation, we have utilized 1992-2020 data on Global Forest and Carbon Metrics and made a predictive model with the help of the Random Forest. Anomaly detection was used to detect any deviations of carbon stocks and forest area which disclosed that there were big anomalies in particular areas. Moreover, a carbon risk scoring system has been designed to identify carbon integrity in timber sourcing with more insights given to regions with more sustainability risks. The results indicate that the implementation of these methods in timber supply chain management system is likely to enhance the accuracy of forecasting, early detection of a disruption, and sustainability of sourcing timber. The project proposes additional composite incorporation of granular climatic information and rule structures to enhance wood forest management and forest timber supply mechanics.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.30574/wjarr.2026.30.2.1197first seen 2026-05-24 04:32:40 · last seen 2026-06-04 04:33:34
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