gxceed
← 論文一覧に戻る

Carbon Credit Quantification Tool: Intelligence System for Coal Mines Simulation and Credit Forecasting

炭素クレジット定量化ツール：炭鉱シミュレーションとクレジット予測のためのインテリジェンスシステム (AI 翻訳)

Vaishnavi Bhavekar, Sakshi Ighe, Bhagyashri Ghadge, Saishri Mane, Kanchan Warke, Shital Pawar, Kiran Yesugade

Open MIND📚 査読済 / ジャーナル2026-05-01#炭素会計
DOI: 10.5281/zenodo.19951129
原典: https://www.ijert.org/carbon-credit-quantification-tool-intelligence-system-for-coal-mines-simulation-and-credit-forecasting

🤖 gxceed AI 要約

日本語

本論文は、石炭鉱山からの温室効果ガス排出量を正確に測定・管理するためのウェブベースの炭素クレジット定量化ツールを提案する。リアルタイムデータとユーザ入力を統合し、排出量予測、炭素クレジット計算、中立化経路シミュレーションなどのモジュールを備える。機械学習により98.5%の精度を達成し、意思決定とコンプライアンスを支援する。

English

This paper proposes a web-based carbon credit quantification tool for coal mines to accurately measure and manage GHG emissions. It integrates real-time data and user inputs, featuring modules for emission prediction, carbon credit calculation, neutralization pathway simulation, and more. Using machine learning, it achieves 98.5% prediction accuracy to aid decision-making and compliance.

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

本論文はインドの炭鉱を対象とするが、リアルタイム排出量計測とカーボンクレジット算定の自動化手法は、日本国内の排出量可視化にも応用可能。特に、サプライチェーン排出量の算定におけるIoT・ML活用の参考事例として示唆に富む。

In the global GX context

This paper presents a practical tool for carbon credit quantification and emissions prediction in coal mining, which could serve as a model for developing digital MRV systems under Article 6 and voluntary carbon markets. The integration of ML for emissions forecasting is a growing trend in climate tech.

👥 読者別の含意

🔬研究者:Demonstrates a modular approach to carbon credit quantification with real-time data integration and ML for prediction; may inform future tool development.

🏢実務担当者:The tool architecture (frontend/backend/ML) can be adapted for corporate carbon management platforms.

🏛政策担当者:Highlights the need for standardized digital MRV to support carbon credit markets and national inventories.

📄 Abstract（原文）

Mining coal produces significant amounts of greenhouse gases including CO₂ and CH₄, and plays a huge role in climate change. More precise measurement and mitigation processes are required for achieving carbon neutrality for India by 2070. Unfortunately, existing methods lack real-time accuracy and reliability as well as being highly dependent on human labor. In this paper, a Carbon Credit Quantification Web-based Tool is suggested in order to accurately calculate, analyze, and control carbon emission levels. Information gathering process is automated and involves usage of real-time data along with information entered by users (e.g. consumption of fuel, electricity, logistics, mining operations, etc.) for calculating emission values using emission factors. Several core modules are identified within the tool, among which are: Carbon Emission Prediction, Carbon Credit Calculation, Neutralization Pathway Simulation, Company Ranking System, Carbon Sink Calculation, and Analytics Dashboard. The development of the web application's frontend relies on React.js, SASS, and Redux technologies, and for backend Node.js & Express.js with Python (Flask) are used. For database purposes, MongoDB Atlas and MySQL are applied. Authentication is provided via OAuth2. Machine learning algorithms implementation uses Scikit-learn, TensorFlow, and PyTorch packages with estimation accuracy of 98.5%. The system provides real-time analytics and visualizations that help in making decisions and being compliant. The areas of application for future research include applying IoT sensors to collect data, along with developing better deep learning algorithms.

🔗 Provenance — このレコードを発見したソース

🔔 こうした論文の新着を逃したくない方は キーワードアラート に登録（無料・3キーワードまで）。

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。