Predicting Carbon Credit Prices Using Advanced Machine Learning Techniques

Abstract

Accurate forecasting of carbon credit prices supports risk management, investment decisions, and policy assessment in the context of climate action. EU ETS carbon prices exhibit volatility, non-linearity, and non-stationarity, which reduces the effectiveness of traditional forecasting models. This dissertation proposes and evaluates a three-stage hybrid machine learning model for one-day-ahead forecasting of EU Emissions Trading System (EU ETS) carbon prices. The architecture follows a divide-and-conquer strategy. First, Wavelet Packet Decomposition (WPD) decomposes the carbon price signal into multiple frequency components. Second, a Gated Recurrent Unit (GRU) network models temporal dependencies and forecasts the trend component. Third, an Extreme Gradient Boosting (XGBoost) model predicts and corrects the GRU residual errors using wavelet-derived detail components as input features. The model was trained and tested on a dataset covering January 2018 to December 2024. The dataset includes EU ETS carbon prices, Brent crude oil prices, and electricity prices, while the forecasting model is univariate and uses the carbon price series only. On an unseen test set of 510 days, the model achieved a Mean Absolute Percentage Error (MAPE) of 1.66%, a Root Mean Squared Error (RMSE) of 4.86 EUR/ton, and a Mean Absolute Error (MAE) of 4.41 EUR/ton. The results indicate that combining signal decomposition, deep learning, and gradient boosting provides stable forecasting performance for EU ETS carbon prices under realistic evaluation conditions.