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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知能システム (AI 翻訳)

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

Zenodo (CERN European Organization for Nuclear Research)📚 査読済 / ジャーナル2026-05-01#炭素会計
DOI: 10.5281/zenodo.19951130
原典: https://doi.org/10.5281/zenodo.19951130

🤖 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を正確に計測・分析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。機械学習により98.5%の精度で排出量を予測し、炭素クレジット計算や中立化経路シミュレーションを提供する。インドの2070年カーボンニュートラル目標達成に向けた実用的なツールである。

English

This paper proposes a web-based carbon credit quantification tool for coal mines to accurately measure and analyze greenhouse gas emissions. Using machine learning with 98.5% accuracy, it predicts emissions, calculates carbon credits, and simulates neutralization pathways. It supports India's 2070 carbon neutrality goal.

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J-クレジット制度があるが、本ツールはインドの石炭鉱山に特化しており、直接的な関連性は低い。ただし、炭素会計の自動化や機械学習活用の事例として参考になる。

In the global GX context

While this tool is specific to Indian coal mines, it demonstrates a practical application of machine learning for carbon accounting and credit forecasting, which could be adapted for other industries and regions. It highlights the need for accurate emission measurement in hard-to-abate sectors.

👥 読者別の含意

🏢実務担当者:A web-based tool for real-time carbon credit calculation and emission prediction, useful for companies in the mining sector.

📄 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.

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