MNPM: research on metabolic neural network prediction model for predicting carbon emission accuracy

Abstract The rapid development of the global economy and society relies on continuous energy demand, while the severe impact of carbon emissions on the ecological environment has garnered significant international attention. Accurately forecasting carbon emission trends is crucial for developing effective reduction strategies and ensuring sustainable green economic development. In this paper, we propose a Metabolic Neural Network Prediction Model (MNPM) for carbon emissions. This model comprehensively considers the impact of various carbon emission-related factors across different regions in China. By employing one-hot encoding, we address the difficulties traditional classifiers face when handling categorical data, enabling predictions under more realistic conditions. To optimize the nonlinear characteristics of the data and overcome the limitations of grey models, we utilize dynamic iterative time series training to address errors due to data inaccuracies effectively. Experimental results demonstrate that our approach outperforms other methods in filtering out irrelevant data, achieving an average relative residual of 0.055 and an average ratio bias of 0.049, translating to more precise prediction accuracy.