機械学習駆動型の炭素排出予測
Machine Learning–Driven Carbon Emission (原題)
Leevesh Kumar
🤖 gxceed AI 要約
日本語
建設ライフサイクル（資材製造・輸送・現場活動・運用）全体の炭素排出予測に機械学習を適用する手法を概観する章。回帰・ANN・SVM・アンサンブル学習を比較し、非線形データへの適応性を検討。BIM・IoT・デジタルツインとの統合によるリアルタイム監視と意思決定支援の枠組みを提示し、ケース例と今後の課題を示す。
English
This chapter reviews machine learning approaches for predicting carbon emissions across the construction lifecycle—material manufacturing, transport, on-site activities, and operations. It examines regression, ANN, SVM, and ensemble models for handling complex nonlinear data, and explores integration with BIM, IoT, and digital twins for real-time monitoring and decision support. A conceptual framework for ML-driven carbon mitigation is proposed with case examples and future research directions.
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
建設セクターのScope 3排出（資材・輸送）算定高度化はSSBJ・有報開示対応で重要性が増す。ML×BIM/IoTによる排出予測は日本企業の建設・不動産開示の精緻化に示唆を与える。
In the global GX context
As ISSB/CSRD push for more granular Scope 3 building-sector accounting, ML-based lifecycle carbon prediction tied to BIM/IoT offers a pathway to auditable, real-time emissions data—relevant to global disclosure infrastructure debates.
👥 読者別の含意
🔬研究者:建設ライフサイクル炭素予測におけるMLモデル選択とBIM/IoT統合の研究課題を整理できる。
🏢実務担当者:建設・不動産の開示担当がScope 3算定や排出削減モニタリングにML/BIM活用を検討する材料になる。
🏛政策担当者:建設セクターの排出算定高度化に向けたデジタル技術活用の政策的示唆を得られる。
📄 Abstract（原文）
Since the building sector contributes significantly to carbon emissions worldwide, sophisticated methods are required for precise forecasting and efficient reduction. The use of machine learning (ML) approaches for carbon emission prediction across the construction lifecycle—including material manufacturing, transportation, on-site activities, and operations—is examined in this chapter. Regression, artificial neural networks, support vector machines, and ensemble learning are among the machine learning models that are rigorously examined, with an emphasis on how well they can handle complicated, nonlinear datasets. In order to facilitate real-time monitoring and decision assistance, the chapter delves deeper into the integration of machine learning (ML) with digital technologies, such as building information modeling (BIM), the Internet of Things (IoT), and digital twins. Supported by case examples, a conceptual framework is put forth to direct ML-driven carbon mitigation methods. Future research directions and challenges are also covered.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.4018/979-8-2600-4947-1.ch003first seen 2026-10-31 05:04:23 · last seen 2026-11-06 04:56:48
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