Predicting EU Emissions Allowance Prices Using Macroeconomic Indicators and Hybrid AI Models

Abstract

Predicting carbon allowance prices has grown more crucial in relation to carbon market regulation, financial strategy, and environmental policy development. This study examines a hybrid forecasting system that combines deep learning with ensemble machine learning models to forecast the price fluctuations of EU Emissions Allowance (EUAs) within the European Union Emissions Trading System (EU ETS). By leveraging a dataset that includes past EUA prices alongside macroeconomic factors like exchange rates, stock indices, natural gas, and crude oil prices, we evaluate the forecasting capabilities of long short-term memory (LSTM) neural networks, random forest (RF), and extreme gradient boosting (XGBoost) models. These models are assessed using commonly recognized metrics: root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). The findings suggest that LightGBM and XGBoost outshine Random Forest and LSTM in performance, with XGBoost emerging as the best predictor model. XGBoost specializes in capturing complex connections within structured data by managing nonlinear connections and interactions. Considering that carbon markets operate in phases, with each phase bringing its own set of reforms, this study offers novel results. In an integrated market prediction of evolving asset class series, LSTM models that are considered more adept at handling sequential data like time series might not yield superior forecasting performance. Our results emphasize the capability of ensemble learning approach forecasting systems to improve prediction accuracy in emissions trading markets, providing important information for policymakers, financial analysts, and sustainability strategists. The hybrid method discussed here demonstrates how AI-based analytics can enhance more resilient and data-informed environmental decision-making.