Accurate prognostication of carbon allowance valuation movements is essential for informing environmental policy formulation and enhancing the efficacy of market-driven regulatory instruments. Sophisticated predictive methodologies enable policymakers to calibrate carbon tax structures more effectively, optimize Emissions Trading Scheme (ETS) functionality, and channel capital toward sustainable, low-emission projects with increased confidence. This research focuses on the Tianjin Emissions Trading Scheme (TJTS) — an early provincial-level carbon exchange established as a component of China’s comprehensive national decarbonization agenda. We introduce a novel prediction framework utilizing Gaussian process regression (GPR), wherein model hyperparameters are determined via Bayesian optimization. This approach facilitates the estimator’s dynamic adaptation to unobserved market dynamics and latent trading patterns. Our empirical analysis employs daily settlement prices for Tianjin Emission Allowances (TJEA) spanning December 26, 2013, to March 23, 2021. This period encompasses significant regulatory reforms, distinct phases of market maturation, and evolving participant behavior within China’s progressively integrated national carbon pricing system. Model performance was rigorously evaluated using an out-of-sample testing window from June 8, 2020, to March 23, 2021. The results demonstrate exceptional predictive accuracy, quantified by a relative root-mean-square error (RRMSE) of 0.3818%, root-mean-square error (RMSE) of 0.0971, mean absolute error (MAE) of 0.0628, and a correlation coefficient (CC) reaching 99.838%. To our knowledge, this constitutes the inaugural application of Gaussian process regression within the context of China’s carbon trading exchanges. Beyond advancing theoretical insights into price discovery mechanisms within nascent emissions markets, the developed methodology offers a versatile analytical template readily transferable to comparable cap-and-trade systems globally.
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