A Comprehensive Analysis for Determining Key Influencing Factors on Carbon Credit Prices

Carbon credits are digital assets used in the fight against climate change. Understanding the complex interplay of factors that affect the carbon trading market is essential to determining the critical influences on carbon credit prices. In this study, three methods are employed to find the key factors that affect the price of carbon credits: correlation analysis, the Maximum Information Coefficient (MIC) and the proposed Carbon Credit Multi-Factor Identification (CCMFI) model. The CCMFI integrates Random Forest regression, SHAP values, and robust ranking mechanisms, providing a powerful tool to identify key price drivers. The results reveal that energy markets (crude oil and coal) and environmental factors (CO2 and GHG emissions) significantly influence carbon credit prices. Non-ferrous metal product manufacturing (NONF), which represents product prices, further highlights the critical role of economic factors. Patents on environmental technologies (PT) are also critical drivers, highlighting the importance of green technological ad-vancements in determining carbon credit prices. European Union Allowances (EUAs) also exerted a strong influence, reflecting the impact of international carbon markets. The performance evaluation of the proposed CCMFI model resulted in an MSE of 1.44, RMSE of 1.20, MAE of 0.44, and an R2 of 0.98, which confirms its effectiveness. Our results indicate that the key factors vary depending on the method applied. Therefore, we recommend using multiple explanation techniques for more reliable results.