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作者:

Zhang, Xinxin (Zhang, Xinxin.) | Xu, Kaili (Xu, Kaili.)

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摘要:

China has promised to reach its peak carbon dioxide emissions by 2030. Power generation emits the most carbon dioxide in China. Fossil fuels are currently still the largest sources of energy for electricity generation in China. As the most commonly used method for estimating emissions from thermal power generation, emission factor data has a statistical lag, which brings inconvenience to carbon emission accounting and the formulation of energy conservation and emission reduction policies. It is of great significance to predict the emission factors of fossil fuel-fired power generation in China. In this paper, the correlations between the data of coal-fired, gas-fired, and oil-fired power generation from 2012 to 2019 and the emission factors of China's power generation and the correlations between the data of fossil fuel-fired power generation from 2006 to 2019 and the emission factors of China's power generation are evaluated using SPSS. On this basis, two models for predicting emission factors of China's power generation from 2020 to 2030 are established and the relevant data are obtained. The calculation results show that when the carbon peak reaches in 2030, the emission factor of China's power generation may be 0.6290 and 0.7363 corresponding to the two prediction models.

关键词:

Carbon dioxide emission factors Carbon peak Power generation Fossil fuels SPSS

作者机构:

  • [ 1 ] [Zhang, Xinxin]Beijing Univ Technol, MOE Key Lab Enhanced Heat Transfer & Energy Conser, Beijing 100124, Peoples R China
  • [ 2 ] [Xu, Kaili]Beijing Univ Technol, MOE Key Lab Enhanced Heat Transfer & Energy Conser, Beijing 100124, Peoples R China
  • [ 3 ] [Zhang, Xinxin]Beijing Univ Technol, Beijing Key Lab Heat Transfer & Energy Convers, Beijing 100124, Peoples R China
  • [ 4 ] [Xu, Kaili]Beijing Univ Technol, Beijing Key Lab Heat Transfer & Energy Convers, Beijing 100124, Peoples R China

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来源 :

CASE STUDIES IN THERMAL ENGINEERING

ISSN: 2214-157X

年份: 2023

卷: 51

6 . 8 0 0

JCR@2022

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