Machine Learning Approaches to Support Corporate Environmental Governance through Accurate Emission Forecasting, Carbon Offset Allocation, and Green Fund Optimization
機械学習による企業環境ガバナンス支援：正確な排出予測、カーボンオフセット配分、グリーンファンド最適化 (AI 翻訳)
Jasmine Selvakumari Jeya Israel, Aldrin Joan Pandian William, Pradeep Kumar Mishra, Muneeswaran Vasudevan
🤖 gxceed AI 要約
日本語
本研究は、Bi-LSTMを用いて鉄鋼、セメント、アルミニウム産業の排出予測、カーボンオフセット配分、グリーンファンド活用を最適化する手法を提案。Bi-LSTMは2050年までの長期予測で高い性能を示し、グリーン技術導入率や炭素回収効率などの変数が持続可能性に与える影響を明らかにした。MLの統合により透明性が向上し、データ駆動型の持続可能性フレームワーク構築を支援する。
English
This study proposes a Bi-LSTM-based approach to optimize emission forecasting, carbon offset allocation, and green fund utilization for steel, cement, and aluminum industries. Bi-LSTM demonstrates superior performance in long-term predictions up to 2050, highlighting the influence of green adoption rates and carbon capture efficiency. Integrating ML enhances transparency and supports data-driven sustainability 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
日本では鉄鋼・セメント・アルミ産業がGX政策の重点分野であり、SSBJ開示や排出削減目標達成に本手法が貢献可能。MLによる予測精度向上は、企業の環境ガバナンス強化と投資家対応に有用。
In the global GX context
Globally, this paper demonstrates how ML can enhance corporate environmental governance, aligning with TCFD/ISSB disclosure requirements. The Bi-LSTM framework offers scalable, data-driven tools for emission forecasting and carbon offset optimization, supporting net-zero strategies across industries.
👥 読者別の含意
🔬研究者:Provides a comparative evaluation of ML models (Bi-LSTM vs others) for emission forecasting, offering methodological insights for AI applications in sustainability.
🏢実務担当者:Offers practical tools for accurate emission forecasting, carbon offset allocation, and green fund optimization, directly applicable to corporate sustainability reporting.
🏛政策担当者:Highlights the potential of ML-driven frameworks to support regulatory compliance and accelerate net-zero targets through enhanced transparency and efficiency.
📄 Abstract（原文）
Net-zero emissions have become a critical strategic goal for corporations seeking alignment with global sustainability targets. This study aimed to explore the application of Machine Learning (ML) techniques, particularly Bi-LSTM (Bidirectional Long Short-Term Memory), to optimize emission forecasting, carbon offset allocation, and green fund utilization across industrial sectors. A Bi-LSTM model was trained on industry-specific data from the Steel, Cement, and Aluminum sectors, with key features including emission intensity, production volume, adoption of green technologies, and carbon capture practices. Additional ML models and carbon cycle simulations supported decision-making and comparative analysis. The Bi-LSTM demonstrated superior performance in balancing accuracy, robustness, and consistency, enabling confident long-term emission predictions up to 2050. Incorporating green adoption rates and carbon capture efficiency further highlighted the critical influence of emerging technologies on sustainability outcomes. The integration of ML models enhanced transparency in green fund usage and supported optimized investment strategies in sustainable technologies and carbon offsets. The results emphasize the transformative potential of ML in strengthening corporate environmental governance and enabling scalable, data-driven sustainability frameworks. Bi-LSTM emerged as the optimal model for industrial emission forecasting, offering actionable insights to accelerate emission reduction efforts and support corporations in achieving net-zero goals. This research underscores the need for transparent, scalable ML frameworks and highlights future directions, including interoperability with blockchain-based carbon credit systems and circular economy practices, to maximize global impact.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.2174/0124055204424228251125044111first seen 2026-06-23 06:12:49
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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。