To the relevant policies of the carbon trading market in China, this paper adds the optimization algorithm that longicorn must use based on the traditional BP neural network, which increases the convergence speed and global optimization ability of the original BP neural network. The empirical study takes the carbon trading price of Shenzhen city, Guangdong Province, China, from 2015-to 2022 as an example. The results show that the base-BP neural network can significantly reduce the probability of local convergence in the iterative process and reduce the mean square error of prediction by 3.08% compared with the traditional BP neural network.
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