The Prediction of Carbon Emission Information in Yangtze River Economic Zone by Deep Learning

Huafang Huang, Xiaomao Wu and Xianfu Cheng
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Huafang Huang: School of Geography and Tourism, Anhui Normal University, Wuhu 241002, China
Xiaomao Wu: Department of Chemical and Biological Engineering, University of Sheffield, Sheffield S10 2TN, UK
Xianfu Cheng: School of Geography and Tourism, Anhui Normal University, Wuhu 241002, China

Land, 2021, vol. 10, issue 12, 1-23

Abstract: This study aimed to respond to the national “carbon peak” mid-and long-term policy plan, comprehensively promote energy conservation and emission reduction, and accurately manage and predict carbon emissions. Firstly, the proposed method analyzes the Yangtze River Economic Belt as well as its “carbon peak” and carbon emissions. Secondly, a support vector regression (SVR) machine prediction model is proposed for the carbon emission information prediction of the Yangtze River Economic Zone. This experiment uses a long short-term memory neural network (LSTM) to train the model and realize the experiment’s prediction of carbon emissions. Finally, this study obtained the fitting results of the prediction model and the training model, as well as the prediction results of the prediction model. Information indicators such as the scale of industry investment, labor efficiency output, and carbon emission intensity that affect carbon emissions in the “Yangtze River Economic Belt” basin can be used to accurately predict the carbon emissions information under this model. Therefore, the experiment shows that the SVR model for solving complex nonlinear problems can achieve a relatively excellent prediction effect under the training of LSTM. The deep learning model adopted herein realized the accurate prediction of carbon emission information in the Yangtze River Economic Zone and expanded the application space of deep learning. It provides a reference for the model in related fields of carbon emission information prediction, which has certain reference significance.

Keywords: carbon emission; SVR; LSTM neural network; carbon emission prediction (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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