Forecasting Carbon Prices: What Is the Role of Technology?

ABSTRACT We examine the role of the technology in predicting carbon prices using a large set of machine learning models. The predictors are selected from technological, environmental, financial, energy, and geopolitical aspects. Our sample covers the daily period from August 1, 2014, to March 4, 2024. We find that technology factors (Information Technology Index, AEX Technology Index, and Tech All Share Index) significantly improve the prediction accuracy of carbon prices, both when included in the prediction model individually and simultaneously. Furthermore, the Diebold–Mariano and Clark–West tests highly reject the null of equal predictive accuracy between the technology model and the baseline model (without technology variables). Moreover, results show that XGBoost outperforms the alternative machine learning models for all forecasting horizons (1, 5, 22, and 250 days). We present significant policy implications useful for investors, companies, and policymakers.