Abstract
Power generation industry is the key industry of carbon dioxide (CO2) emission in China. Assessing its future CO2 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 CO2 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 CO2 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 CO2 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 CO2 emission growth in the power generation industry will gradually slow down according to the prediction results.
Highlights
According to the International Energy Agency, even though the global economy continued to grow in 2016, carbon dioxide (CO2) emissions remained stable with no significant increase since 2015
According to the “13th Five-Year Plan” of power industry released by National Energy Administration of China, it is estimated that by 2020, clean energy power generation will further occupy space for thermal power generation and reduce CO2 emissions [1]
The output of the hidden layer node j is h(j), wij is the weight of the input and the hidden layer, the translation factor of the wavelet basis function hj is expressed by bj, and the scaling factor is expressed by aj
Summary
According to the International Energy Agency, even though the global economy continued to grow in 2016, carbon dioxide (CO2) emissions remained stable with no significant increase since 2015. Some scholars predict the CO2 emission from the power generation side from the perspective of the whole life cycle [3]. After the CO2 emission, population, per capita Gross Domestic Product (GDP), the corresponding level of technology data were regressed and fitted, the regression model of CO2 emission factors in this region can be obtained. This model can scientifically and quantitatively reflect the impact of each major factor on CO2 emissions [11]. The future CO2 emission expectation on the power generation industry is got by plugging the forecasted result into the STIRPAT model
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