A study of improved neural network methods applied to carbon emission prediction

Carbon emission prediction is very important for the development of climate change countermeasures and environmental policies. While BP neural network, as a powerful nonlinear model, can better capture the nonlinear relationships in the data, the PSO algorithm can optimise the weights and biases of the BP neural network by finding the optimal solution to improve the accuracy and performance of the prediction model. Therefore, this paper utilizes the PSO-BP prediction method to forecast the data of carbon emissions and analyze the future development trend of carbon emissions from the provincial perspective. Firstly, the data of carbon emissions are obtained and the data are preprocessed. Then, the PSO algorithm is used to optimise the initial weights and thresholds of BP to improve the prediction performance. Finally, case simulation is used to compare different methods to verify the effectiveness of the model. The simulation results show that the prediction method based on PSO-BP can automatically learn and extract the laws and relationships in the data, reduce the dependence on domain knowledge, and be more flexible and adaptable to different data characteristics, and the prediction method improves the prediction accuracy and stability, which can provide important references for the government and enterprises to formulate the carbon emission reduction policy and assess the environmental risks.