Research on Price Influencing Factors and Machine Learning Pricing Prediction in Carbon Trading Market

As an important tool for addressing climate change and realizing the transition to a low-carbon economy, the carbon trading market has received increasing attention in the study of its price formation mechanism and influencing factors. However, most of the existing studies focus on theoretical discussions and lack empirical analysis of the seven major carbon trading markets in China. In this paper, differential econometric models and machine learning algorithms (including decision trees, random forests, and Xgboost) are used to analyze carbon trading market data between 2014 and 2023 to explore the impact of macroeconomic factors on carbon trading prices. The results of the study show that there is a significant negative correlation between macroeconomic factors and carbon trading prices, and the machine learning model outperforms the traditional linear regression model in predicting carbon trading prices. The research in this paper provides an important reference for improving the efficiency of the carbon trading market and formulating related policies.