環境モニタリング、汚染制御、低炭素管理における人工知能
Artificial Intelligence in Environmental Monitoring, Pollution Control, and Low-Carbon Management (原題)
Jiaming Tan, Heshan Cai, Zekai Liu, Fang Yu, Guanxi Lao, Yongyang Liu, Yuanjie Yang, Zhuolin Xie, Haoze Jiang, Shuwen Han
🤖 gxceed AI 要約
日本語
本レビューは、監視・制御・管理の3層フレームワークで環境分野へのAI応用を整理する。機械学習・深層学習・強化学習・デジタルツイン・IoTが多媒体環境課題に果たす役割を概観し、LCAや多目的最適化との統合により環境影響評価が静的計算から動的予測・意思決定へ移行しつつあると指摘する。データ品質、解釈性、実規模検証、媒体横断的枠組みの欠如が今後の課題とされる。
English
This review organizes AI applications in environmental fields via a three-level framework of monitoring, control, and management. It surveys machine learning, deep learning, reinforcement learning, digital twins, and IoT for multi-media environmental challenges, noting that integration with LCA and multi-objective optimization is shifting environmental assessment from static accounting toward dynamic prediction and decision-making. Key bottlenecks remain in data quality, interpretability, field-scale validation, and cross-medium frameworks.
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
炭素排出管理やLCAとの統合に触れており、SSBJ・Scope3算定の高度化や排出量予測の自動化に関心を持つ日本企業・研究機関にとって参考になる。ただし開示制度や評価基準への直接的な示唆は限定的。
In the global GX context
While not a disclosure-focused paper, its discussion of AI-integrated LCA and dynamic environmental assessment speaks to the broader global push toward automated, predictive sustainability accounting relevant to ISSB/CSRD data infrastructure. It offers a systems-level view of how AI could support integrated environmental governance beyond static reporting.
👥 読者別の含意
🔬研究者:AIを環境モニタリング・制御・管理に統合する際の研究ギャップ（データ品質・解釈性・媒体横断枠組み）を整理する出発点となる。
🏢実務担当者:排出量予測やLCA自動化に関心のある企業にとって、AI導入の現状と限界を把握する参考になる。
🏛政策担当者:環境ガバナンスへのAI活用における検証・解釈性の課題を示し、制度設計上の留意点を提供する。
📄 Abstract（原文）
Air pollution, water contamination, soil degradation, solid waste accumulation, and carbon emissions are increasingly interconnected, posing common challenges to environmental engineering, including diverse monitoring targets, heterogeneous data sources, competing control objectives, and delayed management responses. This review examines the application progress of artificial intelligence in environmental fields through a three-level framework encompassing monitoring, control, and management. It systematically summarizes the roles of machine learning, deep learning, reinforcement learning, transfer learning, digital twins, and the Internet of Things in addressing multi-media environmental challenges. Current studies indicate that artificial intelligence has developed a relatively mature application basis in environmental monitoring as well as showing considerable potential for pollution-control processes. At the management level, the integration of machine learning with LCA, digital twins, and multi-objective optimization is gradually transforming environmental impact assessment from static accounting toward dynamic prediction, feedback, and decision-making. Despite these advances, key bottlenecks remain in data quality, model interpretability, field-scale validation, and the lack of cross-medium collaborative frameworks. Future research should therefore improve the reliability, generalizability, and interpretability of AI models while advancing AI from a task-specific modeling tool to a system-level decision-support technology for integrated environmental governance.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.3390/cleantechnol8050160first seen 2026-10-04 04:44:23
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gxceed は公開メタデータに基づく研究支援データセットです。要約・翻訳・解説は AI 支援で生成されています。 最終的な解釈・検証は利用者が原典資料に基づいて行うことを前提とします。