Cloud-Computing for Carbon Finance Decision Support System with Dynamic Machine Learning Repository Management Service

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.