Intelligent Data-Driven Models for Accurate Multi-factors Prediction of Carbon Credit Prices

Abstract

This thesis addresses the challenge of accurately predicting carbon credit prices, which are non-linear, non-stationary, and influenced by multiple correlated external factors such as energy prices, environmental indicators, and economic conditions. Accurate pricing is vital for transparency and effectiveness in carbon markets. A systematic literature review identified research gaps, leading to the development of a Carbon Credit Multi-Factor Prediction (CCMFP) model integrating factor identification and optimized prediction algorithms. The proposed Carbon Credit Multi-Factor Identification (CCMFI) model combines random forest regression with explainable AI to identify the most influential factors among 22 external variables. Feature reduction and extraction techniques, independent component analysis (ICA), nonlinear ICA (NLICA), and principal component analysis (PCA), were then applied, with extracted components used as inputs to SVR and MLP models. Using daily Australian Carbon Credit Units (ACCUs) prices as a case study, experiments evaluated the impact of different factor sets on prediction accuracy. The models achieved an R2 of over 97%, with optimal performance from factors including environmental technology patents, CO2 emissions, renewable energy adoption, global carbon allowances, coal and crude oil prices. These findings enhance market confidence, reduce financial risks, and support global climate change mitigation through effective carbon credit utilization.