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Energies 2018, 11(10), 2709; https://doi.org/10.3390/en11102709

Driving Factor Analysis and Forecasting of CO2 Emissions from Power Output in China Using Scenario Analysis and CSCWOA-ELM Method

1
Department of Economics and Management, North China Electric Power University, Baoding 071000, China
2
Shijiazhuang Power Supply Branch, Hebei Electric Power Co., Ltd., Shijiazhuang 050000, China
3
Hebei Electric Survey and Design Research Institute, Shijiazhuang 050000, China
*
Author to whom correspondence should be addressed.
Received: 22 August 2018 / Revised: 30 September 2018 / Accepted: 1 October 2018 / Published: 11 October 2018
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Abstract

With power consumption increasing in China, the CO2 emissions from electricity pose a serious threat to the environment. Therefore, it is of great significance to explore the influencing factors of power CO2 emissions, which is conducive to sustainable economic development. Taking the characteristics of power generation, transmission and consumption into consideration, the grey relational analysis method (GRA) is adopted to select 11 influencing factors, which are further converted into 5 main factors by hierarchical clustering analysis (HCA). According to the possible variation tendency of each factor, 48 development scenarios are set up from 2018–2025, and then an extreme learning machine optimized by whale algorithm based on chaotic sine cosine operator (CSCWOA-ELM) is established to predict the power CO2 emissions respectively. The results show that gross domestic product (GDP) has the greatest impact on the CO2 emissions from power output, of which the average contribution rate is 1.28%. Similarly, power structure and living consumption level also have an enormous influence, with average contribution rates over 0.6%. Eventually, the analysis made in this study can provide valuable policy implications for power CO2 emissions reduction, which can be regarded as a reference for China’s 14th Five-Year development plan in the future. View Full-Text
Keywords: power CO2 emissions; forecast; factor analysis; CSCWOA-ELM; scenario analysis power CO2 emissions; forecast; factor analysis; CSCWOA-ELM; scenario analysis
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Wang, W.; Peng, W.; Xu, J.; Zhang, R.; Zhao, Y. Driving Factor Analysis and Forecasting of CO2 Emissions from Power Output in China Using Scenario Analysis and CSCWOA-ELM Method. Energies 2018, 11, 2709.

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