Predictability and Causal Identification of Carbon Prices Using Interpretable Variables: Evidence from China’s Hubei Carbon Market

He Jiang,Rongyu Cao, Xue-li Chen,Malin Song(通讯作者),Juntao Du

Abstract：Amid global climate governance transformations, accurate carbon price prediction and causal analysis have become critical. Existing studies predominantly examine linear impacts of single factors (e.g., policy or energy prices), neglecting complex nonlinear interactions among financial markets, climate variables, market heterogeneity, and behavioral responses. This study addresses these gaps by developing an integrated analytical framework incorporating four key drivers (policy, market, climate, behavioral indicators) for China’s Hubei carbon market. We propose a novel three-stage methodology involving feature selection, predictive model optimization, and causal identification. In the first stage, 66 variables from eight categories (macroeconomic indicators, financial markets, climate data, etc.) are narrowed to nine predictors via hybrid selection. In the second stage, we compare six machine learning models and find that Random Forest (RF) (RMSE=2.06) and XGBoost (RMSE=2.31) significantly outperform traditional time-series models. Finally, we use a double machine learning (DML)  approach to partial out high dimensional confounding and to obtain  orthogonalized estimates. Under the identification assumptions of the  DML framework and supported by the statistical evidence, the estimates  indicate that key financial indices and cryptocurrency related variables  have statistically significant causal effects on the carbon price in  the Hubei market. These empirical findings  provide valuable guidance for regulators in developing cross-market  risk warning systems and improving the efficiency of carbon allowance  allocation.