Carbon sink price prediction based on radial basis kernel function support vector machine regression model

Abstract

Abstract At present, the main pricing methods for carbon sink trading are marginal cost pricing, shadow price pricing and physical option pricing. The most serious defect of these three methods is that their theoretical basis is the extremely idealized effective market hypothesis, which obviously does not conform to the fractal and chaotic behavior characteristics of the actual carbon sink trading market. It is particularly important to study new pricing technologies and methods for a complex, nonlinear and dissipative system. Therefore, this paper constructs a carbon sink price prediction method based on the radial basis kernel function support vector machine (RBF-SVM) model. The main findings are the following: • Compared with the build nonlinear support vector machine (SVM) models by the three kernel functions: linear kernel function (LF), radial basis function (RBF) and sigmoid kernel function, the RBF-SVM has the highest prediction accuracy. • The adaptability of the RBF-SVM model is tested by using the price fluctuation data of EU allowance (EUA). It is proved that the adaptability of the RBF-SVM model for the price predictions of heterogeneous carbon products is also excellent. • The forecast results of the RBF-SVM model for carbon sink price for the period of September 2021 to August 2022 show that the lowest carbon sink price of 20 euros is expected to occur around November 2021 and the highest price of 38.5 euros around August 2022. Therefore, the established RBF-SVM model can be used for carbon sink trading market price prediction, which is more scientific and applicable than the previous three methods.