A Survey of Carbon Emission Forecasting Methods Based on Neural Networks

Abstract

The emission of a large amount of carbon dioxide has led to the greenhouse effect. With further research into the hazards of the greenhouse effect, the world’s major economies have started implementing energy-saving and emission-reduction plans. Predicting carbon emissions is important for the formulation of effective energy-saving and emission-reduction policies. This paper mainly introduces methods for predicting carbon emissions by using BP neural networks, recurrent neural networks, and a combination of traditional forecasting models with neural networks. Firstly, the characteristics of different neural networks are compared for carbon emission prediction. Secondly, the LSTM network is used for carbon emission prediction, and the evaluation results show the effect of the LSTM network. Finally, conclusion is drawn that BP and recurrent neural network are not ideal for long sequences. Joseph Junior NKOU NKOU