Prediction of Direct Carbon Emissions of Chinese Provincial Residents under Artificial Neural Networks in Deep Learning Environment

Abstract

It is aimed to deepen the understanding of the consumption carbon emissions of Chinese provinces, establish an accurate and feasible carbon emission prediction model, develop an urban low-carbon economy, and ensure the sustainable development of Chinese cities. Through the national statistical data information, based on the artificial neural network model, mathematical statistics and deep learning methods are used to learn and analyze the carbon emission data of various provinces in China from 1999 to 2019. The neural network toolbox in Matlab is used to program separately to realize the prediction of carbon emissions by different neural network models. After comparing and analyzing the accuracy and prediction performance, the optimal model for the prediction effect is selected. Finally, based on ArcGIS Engine (Arc Geographic Information Science Engine) and C#.NET platform, the call to Matlab neural network toolbox is realized. The selected model is embedded in the prediction system to complete the development of the entire system. The results show that the carbon emissions of residents in the north are distinctly higher than those in the south. Also, with the passage of time, the rate of carbon emissions continues to accelerate. Compared with other models, Elman neural network has higher accuracy and smaller error in carbon emission prediction. Compared to BP (Back Propagation) neural network, the accuracy is improved by 55.93%, and the prediction performance is improved by 19.48%. The prediction results show that China is expected to reach the peak of carbon emissions from 2027 to 2032. This investigation will provide a theoretical basis to control and plan carbon emissions from Chinese urban residents.