Real-Time Carbon Footprint Monitoring and Predictive Reporting Cloud Solutions for Sustainability
持続可能性のためのリアルタイム炭素フットプリント監視と予測レポートを実現するクラウドソリューション (AI 翻訳)
Harish Narne
🤖 gxceed AI 要約
日本語
本論文は、AIとデータ分析を活用したクラウド基盤を構築し、IoTセンサーとビッグデータを用いて炭素排出をリアルタイムで監視・予測するシステムを提案する。産業間の排出を継続的に観測し、予測レポートを生成することで、排出削減とエネルギーコスト削減を支援する。
English
This paper proposes a cloud-based system that uses AI, IoT sensors, and big data analytics to monitor carbon footprints in real time and generate predictive reports. The scalable infrastructure enables continuous observation of inter-industry emissions, supporting better decisions and energy cost savings while advancing sustainability goals.
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
日本の企業はSSBJやTCFDに基づく排出量開示が進む中、本システムのようなAIによるリアルタイム排出監視はデータ収集の効率化に寄与し、省エネとコスト削減にもつながる。
In the global GX context
This aligns with ISSB and CSRD requirements for timely and reliable climate disclosure, offering a practical approach for companies to automate GHG data collection and improve reporting accuracy.
👥 読者別の含意
🔬研究者:A useful system architecture reference for applying AI/ML to real-time carbon accounting and predictive emission reporting.
🏢実務担当者:Corporate sustainability teams can adopt this approach to automate GHG data collection and generate forward-looking emission targets.
🏛政策担当者:Policymakers may consider promoting real-time monitoring infrastructure to improve the accuracy and timeliness of national emission inventories.
📄 Abstract（原文）
Climate change demands time-sensitive carbon footprint computations that need systems for continuous observation and predictive analytics. The paper builds a time-sensitive cloud infrastructure using artificial intelligence and data analytical techniques, enabling carbon footprint observation alongside predictive reporting features. Industrial carbon emission monitoring is possible through IoT sensors that process big data while applying machine learning models for permanent observation of inter-industry emissions to generate accurate predictions for better decision outcomes. The system depends on the extensive data collection capabilities of a scalable cloud infrastructure to process information without interruption. Organizations achieve carbon prevention goals and sustainable objectives and decrease energy expenses by implementing AI-based predictive analysis. Cloud-based carbon monitoring demonstrates its ability to effectively reduce emissions, improve procedural standards, and enable environment-conscious decision support. The current sustainable computing research reveals a powerful real-time solution to environmental issues.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.1109/icetm63734.2025.11051559first seen 2026-08-02 18:46:21
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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。