AI-integrated renewable energy and data analytics platform for corporate ESG compliance
企業のESGコンプライアンスのためのAI統合再生可能エネルギー・データ分析プラットフォーム (AI 翻訳)
Shamsun Nahar, Florina Rahman, Mahrima Akter Mim
🤖 gxceed AI 要約
日本語
AIと機械学習を用いて再生可能エネルギーとデータ分析を統合し、エネルギー需要予測、排出量推定、ESG報告を自動化するプラットフォームを提案。シミュレーションにより効率向上と報告時間短縮を実証。
English
This paper presents an AI-integrated platform combining renewable energy and data analytics for corporate ESG compliance. It uses AI/ML for energy demand forecasting, carbon emission estimation, and automated ESG reporting aligned with international frameworks. Experimental results show improved energy efficiency, increased renewable penetration, and reduced reporting effort.
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
日本企業にとっても、SSBJやTCFD対応を含むESG報告の効率化は重要な課題。本プラットフォームのAIによる自動化アプローチは、日本の統合報告書作成や有報における非財務情報開示の負担軽減に寄与する可能性がある。
In the global GX context
This work aligns with global trends in automated ESG disclosure under ISSB, CSRD, and SEC climate rules. The platform's ability to integrate real-time data and produce standardized indicators addresses the need for auditable and efficient reporting systems worldwide.
👥 読者別の含意
🔬研究者:Researchers can explore the AI models for energy forecasting and emission estimation as applied to corporate ESG.
🏢実務担当者:Corporate sustainability teams can adopt the platform's approach to streamline ESG reporting and monitor renewable energy utilization.
🏛政策担当者:Policymakers may consider such integrated platforms to support reliable ESG data collection and standardize reporting across firms.
📄 Abstract（原文）
Corporate Environmental, Social, and Governance (ESG) compliance has become a mandatory requirement for organizations due to stricter regulations, investor expectations, and global sustainability goals. Despite growing adoption of renewable energy technologies, many corporations face challenges in effectively monitoring energy usage, carbon emissions, and ESG performance using fragmented and manual systems. This paper presents an AI-integrated renewable energy and data analytics platform designed to support corporate ESG compliance through continuous monitoring, predictive analysis, and automated reporting. The proposed platform integrates renewable energy sources, smart meters, and environmental sensors to collect real time operational data. Advanced artificial intelligence and machine learning models are applied to forecast energy demand, optimize renewable energy utilization, and estimate carbon emission trends. The analytics layer transforms raw energy data into standardized ESG indicators, enabling transparent and auditable sustainability assessment aligned with international reporting frameworks. The system also supports risk identification by detecting anomalies and potential noncompliance patterns in energy consumption and emissions. Experimental evaluation using simulated corporate energy datasets demonstrates that the proposed platform improves energy efficiency, increases renewable energy penetration, and significantly reduces the time and effort required for ESG reporting. The results highlight the effectiveness of AI driven data analytics in enabling data-based decision making for sustainability management. Overall, the proposed platform provides a scalable and intelligent solution for organizations seeking to achieve reliable ESG compliance while maintaining operational efficiency and long-term environmental responsibility.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.30574/wjaets.2026.18.1.0031first seen 2026-06-23 06:11:25
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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。