Prediction of CO 2 Emission in China’s Power Generation Industry with Gauss Optimized Cuckoo Search Algorithm and Wavelet Neural Network Based on STIRPAT model with Ridge Regression

Weibo Zhao and Dongxiao Niu
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Weibo Zhao: School of Economics and Management, North China Electric Power University, Beijing 102206, China
Dongxiao Niu: School of Economics and Management, North China Electric Power University, Beijing 102206, China

Sustainability, 2017, vol. 9, issue 12, 1-15

Abstract: Power generation industry is the key industry of carbon dioxide (CO 2 ) emission in China. Assessing its future CO 2 emissions is of great significance to the formulation and implementation of energy saving and emission reduction policies. Based on the Stochastic Impacts by Regression on Population, Affluence and Technology model (STIRPAT), the influencing factors analysis model of CO 2 emission of power generation industry is established. The ridge regression (RR) method is used to estimate the historical data. In addition, a wavelet neural network (WNN) prediction model based on Cuckoo Search algorithm optimized by Gauss (GCS) is put forward to predict the factors in the STIRPAT model. Then, the predicted values are substituted into the regression model, and the CO 2 emission estimation values of the power generation industry in China are obtained. It’s concluded that population, per capita Gross Domestic Product (GDP), standard coal consumption and thermal power specific gravity are the key factors affecting the CO 2 emission from the power generation industry. Besides, the GCS-WNN prediction model has higher prediction accuracy, comparing with other models. Moreover, with the development of science and technology in the future, the CO 2 emission growth in the power generation industry will gradually slow down according to the prediction results.

Keywords: CO 2; STIRPAT; power generation industry; wavelet neural network; Gauss optimization cuckoo algorithm (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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