VayuCredit: A Data-Driven and Machine Learning Framework for Carbon Emission Monitoring and Credit Quantification Using Ensemble Learning
VayuCredit：アンサンブル学習を用いた炭素排出監視とクレジット定量化のためのデータ駆動型機械学習フレームワーク (AI 翻訳)
Dharni Patel, Urvi Deore, Y. A. Vishwa Priya, M. M. Sameer Ali, Luigi Orlando Freire Martínez, Jaime Alfonso Flores Navas
🤖 gxceed AI 要約
日本語
インドの炭素クレジット取引制度（CCTS）に対応するため、機械学習を用いた炭素排出監視・クレジット定量化フレームワークVayuCreditを提案。3つのMLモデル（傾向予測、異常検出、回帰）を組み合わせ、企業レベルの排出量を予測し、CCTS準拠の推奨と罰則・利益計算を提供。1990-2022年の235カ国・7,748件のデータで訓練し、GroupKFoldで汎化性能を確保。セメント等7業種のGEI目標達成を支援する。
English
VayuCredit is a machine learning framework for carbon emission monitoring and credit quantification, tailored to India's upcoming Carbon Credit Trading Scheme (CCTS). It uses three models—trend prediction, anomaly detection, and regression—to forecast company-level emissions and provide compliance recommendations. Trained on global data (1990-2022, 235 countries) with GroupKFold to avoid leakage, it supports seven industries in meeting India's GEI targets and calculates penalties and profits in rupees.
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
インドのCCTS制度に特化した研究だが、日本でもSSBJ開示やカーボンクレジット市場整備が進む中、MLによる排出量予測とコンプライアンス評価の枠組みは参考になる。特に、企業レベルの排出量を国別データから推計する手法は、日本のScope 3算定やサプライチェーン排出量把握に応用可能。
In the global GX context
This paper contributes to global GX scholarship by demonstrating an AI-driven approach to carbon credit compliance, relevant as many countries develop similar trading schemes. The methodology for cross-country generalization and company-level inference offers insights for emission monitoring in emerging economies. It aligns with global trends toward data-driven climate disclosure and carbon pricing mechanisms.
👥 読者別の含意
🔬研究者:Provides a novel ensemble ML approach for carbon emission prediction and anomaly detection, with robust validation methods.
🏢実務担当者:Offers a framework for automating carbon compliance and credit quantification, useful for companies in CCTS-regulated sectors.
🏛政策担当者:Highlights the potential of ML in enforcing carbon trading schemes and setting emission targets.
📄 Abstract（原文）
The economic development sector's industrial operations have struggled with carbon emission monitoring and regulatory compliance, especially as India prepares to implement its Carbon Credit Trading Scheme (CCTS) by October 2026. VayuCredit analyses Indian companies' carbon emissions in different CCTS-regulated regions using three machine learning models. The World in Data Global Carbon Project trained all three models: Scale-invariant classifiers predict emission trends and make CCTS-aligned recommendations, Copula-Based Outlier Detection models find unsupervised anomalies, and XGBoost regressors with log-transformed targets and Optuna hyperparameters. From 1990 to 2022, this collection has 7,748 records from 235 nations. Researchers avoid cross-country data leakage by generalising across unobserved national emission trends using GroupKFold cross-validation. VayuCredit's real-time CCTS compliance module meets MoEFCC's October 2025 and January 2026 GEI targets for cement, aluminium, chlor-alkali, pulp and paper, petroleum refining, petrochemicals, and textiles. GHG Protocol Tier 1 emission parameters for India are based on CEA 2023 grid data and BEE benchmark. The compliance engine calculates Indian Rupee profits and penalties for Carbon Credit Certificates (CCCs) at a market price of ₹830-1,000 per tonne CO₂e. Double market-rate BEE fines. Emission computation, ML prediction, anomaly detection, trend recommendation, and CCTS compliance evaluation are FastAPI REST API endpoints. All three multi-models combined national-scale training data with company-scale industrial inference to exceed accuracy standards in industrial carbon monitoring and compliance management testing.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.69888/ftsess.2026.000693first seen 2026-08-05 05:16:20
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