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.
Highlights
In recent years, with the frequent occurrence of extremely severe weather due to global warming, countries around the world have begun to pay attention to the imbalance of carbon emissions caused by the emissions of greenhouse gases such as carbon dioxide (CO2 ) [1]
The performance of cuckoo optimization algorithm neural network (COANN) is evaluated by the mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), and correlation coefficient (CC) between the model output and the actual data set
The support vector regression (SVR) model is used to perform non-linear regression classification and fitting on
Summary
With the frequent occurrence of extremely severe weather due to global warming, countries around the world have begun to pay attention to the imbalance of carbon emissions caused by the emissions of greenhouse gases such as carbon dioxide (CO2 ) [1]. It is believed that it is important to objectively evaluate the impact of relevant factors on carbon emissions They proposed a modified production theory decomposition analysis (PDA) model under the semi-disposable hypothesis, and correspondingly decomposed the carbon emission changes of China’s thermal power generation industry [8]. Some scholars have used the Lasso regression model to screen out eight significant factors affecting carbon emissions, and used the BP neural network model to predict the carbon emissions of Jiangsu Province from 2019 to 2030. They used artificial neural networks (ANN) to develop carbon emission intensity prediction models for Australia, Brazil, China, India, and the United States.
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