Work

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

Year: 2026

Type: article

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.

Source: Sustainability

DOI: 10.3390/su18178967

Authors: Shichao Li, Heng Wu, Abu Sufian Abu Bakar

Institutions: Universiti Utara Malaysia, Jilin University, State Key Laboratory of Automotive Simulation and Control

Language: English

Topic: Stock Market Forecasting Methods

Subfield: Management Science and Operations Research

Field: Decision Sciences

Domain: Social Sciences

SDG: Climate action

Open Access status: gold

Estimated APC paid: $2,896