Is China’s National Carbon-Allowance Price Predictable? An Interpretable Machine Learning and Volatility Analysis Around the 2025 Market Expansion

Abstract

China’s national Emissions Trading System expanded from power to steel, cement and aluminum in March 2025. We examine daily carbon emission allowance price predictability using 1203 trading-day prices. Eleven models and a 26-predictor baseline undergo nested expanding-window validation. Separate common-sample sensitivities add pre-open GFS weather, an official ten-day coal price, a conservatively lagged national generation proxy and official macroeconomic first releases. Across 952 forecasts, the random walk has the lowest RMSE (1.242 CNY/t); the stabilized neural network reaches 1.251. The exact-release/first-public macro specification lowers random-forest RMSE from 1.289 to 1.266, whereas the public energy/weather specification records 1.275; neither beats the benchmark. Technical variables retain the largest model attribution, but even a technical-only forest records 1.253. Ljung–Box and BDS tests detect dependence, while sign runs do not reject sign independence and the variance-ratio null is rejected only at the two-day horizon. Rolling and multiple-break tests find no expansion-date shift. Volatility rankings remain loss-dependent. Statistical dependence therefore exists without stable point-forecast gains. The added energy measures do not represent observed national daily load, and execution returns are not inferred from daily OHLC data.