Cloud-Computing for Carbon Finance Decision Support System with Dynamic Machine Learning Repository Management Service
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Date
2025-06-20
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Publisher
Saudi Digital Library
Abstract
This thesis presents the development of a local machine learning-based decision support
system designed to predict future carbon prices. Carbon finance markets play a critical
role in supporting global climate change mitigation strategies, where market price volatility
poses substantial challenges for investors, policymakers, and carbon traders. This research
integrates and compares five advanced forecasting models — Extreme Gradient Boosting
(XGBoost), Light Gradient Boosting Machine (LightGBM), Categorical Boosting (CatBoost),
Facebook Prophet, and Autoregressive Integrated Moving Average (ARIMA)—by training
and evaluating them on historical carbon price datasets to identify the optimal predictive
approach. The system is implemented as a Spring Boot web application operating in a local
environment, serving as a functional proof-of-concept for potential future deployment on
scalable cloud infrastructure. The models are evaluated using performance metrics such
as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and the coefficient of
determination (R²). Results demonstrate that the selected best-performing model offers
superior forecasting accuracy and robustness under varying market conditions. This work
contributes to the intersection of carbon finance and artificial intelligence by delivering
an extensible, locally operable system that lays the groundwork for future cloud-based
deployment, supporting informed decision-making for stakeholders in the carbon trading
ecosystem.
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Keywords
Carbon Finance, Cloud Computing, Decision Support Systems (DSS), Carbon Pricing Prediction, Machine Learning, AI-Driven Policy Support, Carbon Market Analytics
Citation
IEEE