AI-driven real-time carbon footprint tracking and autonomous reduction for sustainable enterprises
持続可能な企業のためのAI駆動型リアルタイムカーボンフットプリント追跡と自律的削減 (AI 翻訳)
Yuan Chai, Haytham F. Isleem, P Kumar, Ghanshyam G. Tejani, David Bassir
🤖 gxceed AI 要約
日本語
本論文はIoTテレメトリ、ERPログ、系統炭素強度データを統合したAIパイプラインを提案し、サブ時間単位でのScope1-2排出量推定と削減アクション推奨を行う。Transformer、CNN、BiLSTMのアンサンブルと多目的最適化（NSGA-III）を用い、SHAP/LIMEで説明可能性を確保。247組織の18ヶ月評価で、検証排出量との整合性が高かった（MAPE 3.2%対18.7%）が、因果関係の推定は観察的でありデータ品質に依存する。
English
This paper proposes an AI-driven pipeline integrating IoT telemetry, ERP logs, and grid carbon intensity for near-real-time Scope 1-2 emission tracking. Using attention-based multimodal fusion, an ensemble of Transformer/CNN/BiLSTM, and multi-objective optimization (NSGA-III), it achieves high accuracy (MAPE 3.2% vs 18.7%) in a 247-organization study. SHAP/LIME provide explainability. Limitations include observational causal inference and data quality dependence.
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
SSBJ基準がScope1-2の精度向上を求めており、リアルタイム排出量モニタリングは有報・統合報告書のデータ品質向上に貢献する。ただし、本論文の評価は中国・アフリカ・フランス等の組織を含んでおり、日本企業への直接適用にはデータ連携や規制環境の差異に注意が必要。
In the global GX context
This paper addresses the ISSB/TCFD demand for timely, reliable emission data. The AI pipeline's real-time capability and explainability align with CSRD and SEC climate disclosure requirements. However, its observational evaluation without causal attribution limits direct policy implications.
👥 読者別の含意
🔬研究者:Demonstrates state-of-the-art AI methods for real-time carbon accounting, with rigorous evaluation across diverse organizations.
🏢実務担当者:Provides a template for integrating IoT and ERP data to improve emission tracking accuracy and support operational reduction decisions.
🏛政策担当者:Highlights the potential of AI-driven monitoring for enhancing disclosure reliability, but also the need for robust causal verification.
📄 Abstract（原文）
Current practice in enterprise carbon reporting is heavily dependent on periodic emissions inventories and disconnected data streams. As a result, drivers of emissions are not detected quickly enough to enable responsive action. Here we build and validate a near-real-time carbon-intelligence pipeline that incorporates facility IoT telemetry, ERP event logs, grid carbon-intensity feeds, and optionally satellite proxies, for near-real-time estimation of Scope 1-2 emissions at sub-hourly granularity and associated recommendations for abatement actions. The system uses attention-based multimodal fusion, an ensemble of Transformer, CNN, and BiLSTM predictors with calibrated uncertainty, and a multi-objective optimizer (NSGA-III with PSO and simulated-annealing refinement) that balances emissions, cost, and operational disruption. SHAP and LIME provide audit-ready explanations for key predictors and recommended actions. An 18-month matched-pair observational evaluation (January 2023–June 2024) across 247 organizations in four sectors compared the proposed approach with conventional quarterly accounting baselines. The proposed pipeline showed stronger agreement with verified emissions (MAPE 3.2% vs. 18.7%; RMSE 0.087 vs. 0.524 tCO 2 e; Pearson r 0.94–0.97). On matched baseline comparisons, sites implementing recommended actions tended to have lower verified emission intensity on average, though results were still impacted by production mix, demand variation, implementation rigor and data quality. Economic impacts, such as estimated ROI, are also derived from observational comparisons without causal attribution. Overall, the findings suggest that explainable and time-resolved emissions intelligence may support enterprise decarbonization planning and operational monitoring.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.1016/j.esr.2026.102293first seen 2026-06-23 05:41:42
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