Statistical data-based prediction of carbon dioxide emission factors of China's power generation at carbon peak in 2030

被引:2
|
作者
Zhang, Xinxin [1 ,2 ]
Xu, Kaili [1 ,2 ]
机构
[1] Beijing Univ Technol, MOE Key Lab Enhanced Heat Transfer & Energy Conser, Beijing 100124, Peoples R China
[2] Beijing Univ Technol, Beijing Key Lab Heat Transfer & Energy Convers, Beijing 100124, Peoples R China
基金
北京市自然科学基金;
关键词
Carbon dioxide emission factors; Power generation; Fossil fuels; Carbon peak; SPSS; COAL-FIRED POWER; SCENARIO ANALYSIS; CO2; EMISSIONS;
D O I
10.1016/j.csite.2023.103633
中图分类号
O414.1 [热力学];
学科分类号
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.
引用
收藏
页数:13
相关论文
共 50 条
  • [1] REGIONAL FORECAST OF CHINA'S CARBON EMISSION PEAK IN 2030 AND ANALYSIS OF INFLUENCE FACTORS
    Wang, Dawei
    Zhou, Congyi
    Shen, Wenxing
    Zhang, Shengliang
    Sun, Hong
    ENVIRONMENTAL ENGINEERING AND MANAGEMENT JOURNAL, 2024, 23 (05):
  • [2] Study on How to Achieve 2030 Carbon Emission Peak in Power Sector
    Xing, Lu
    Cheng, Lu
    PROCEEDINGS OF THE 2015 INTERNATIONAL CONFERENCE ON ELECTRICAL, ELECTRONICS AND MECHATRONICS, 2016, 34 : 34 - 37
  • [3] Present and Prediction of Carbon Dioxide Emission Reduction of Coal Power Plant in China
    Du Yun-gui
    Wu Lin
    Yu Jiang-tao
    Qin Fu-chu
    Wang Hou-lin
    2011 INTERNATIONAL CONFERENCE ON MACHINE INTELLIGENCE (ICMI 2011), PT 1, 2011, 3 : 595 - +
  • [4] Carbon Dioxide Emission Peak Study of Transportation Industry in China
    Zhu C.-Z.
    Yang S.
    Liu P.-B.
    Wang M.
    Jiaotong Yunshu Xitong Gongcheng Yu Xinxi/Journal of Transportation Systems Engineering and Information Technology, 2022, 22 (06): : 291 - 299
  • [5] China's carbon dioxide emission and driving factors: A spatial analysis
    Yang, Yu
    Zhou, Yannan
    Poon, Jessie
    He, Ze
    JOURNAL OF CLEANER PRODUCTION, 2019, 211 : 640 - 651
  • [6] Prediction of Photovoltaic power generation and analyzing of carbon emission reduction capacity in China
    Liu, Bingchun
    Huo, Xiankai
    RENEWABLE ENERGY, 2024, 222
  • [7] China's Industrial Carbon Dioxide Emission Statistical Report Forms and Information System Based on WebGIS
    Zhou, Wei
    Ge, Xiaobo
    Dong, Wenfu
    CONFERENCE ON WEB BASED BUSINESS MANAGEMENT, VOLS 1-2, 2010, : 212 - +
  • [8] Carbon Emission Scenario Prediction and Peak Path Selection in China
    Liu, Xiaodie
    Wang, Xiangqian
    Meng, Xiangrui
    ENERGIES, 2023, 16 (05)
  • [9] Research on the Peak Carbon Dioxide Emission Strategy of Chinese Port Based on Carbon Emission Estimation
    Fan, Shenghai
    Lu, Ziai
    FRONTIERS IN ENVIRONMENTAL SCIENCE, 2022, 9
  • [10] Impacts of the carbon emission trading system on China’s carbon emission peak: a new data-driven approach
    Liangpeng Wu
    Qingyuan Zhu
    Natural Hazards, 2021, 107 : 2487 - 2515