A Bayesian-Tuned Gaussian Process Approach to Forecasting Carbon Market Prices: A Case Study of China’s Emissions Trading Scheme

Precise forecasting of fluctuations in carbon allowance valuations is critical for shaping environmental policy and for bolstering the effectiveness of market-based regulatory mechanisms. Advanced statistical and machine-learning techniques afford regulators the capacity to fine-tune carbon taxation schemes, enhance the operational efficiency of emissions trading frameworks, and steer financial resources toward low-carbon development projects with greater assurance. This study examines the China Emissions Trading Scheme (CHNTS) one of China’s pioneering carbon markets established under the broader national decarbonization strategy and presents an innovative predictive model based on Gaussian Process Regression…