Countermeasures of Double Carbon Targets in Beijing-Tianjin-Hebei Region by Using Grey Model

被引:2
作者
Liu, Zhenxiu [1 ]
Wang, Meng [2 ]
Wu, Lifeng [2 ]
机构
[1] Hebei Univ Technol, Coll Econ & Management, Tianjin 300401, Peoples R China
[2] Hebei Univ Engn, Coll Management Engn & Business, Handan 056038, Peoples R China
关键词
carbon peak period prediction; Beijing-Tianjin-Hebei region; FGM (1,1); analysis of temporal and spatial differences;
D O I
10.3390/axioms11050215
中图分类号
O29 [应用数学];
学科分类号
070104 ;
摘要
In this paper, by combining the development characteristics of the Beijing-Tianjin-Hebei region, the fractional accumulation GM (1,1) model was used to predict the peak time of the Beijing-Tianjin-Hebei region, and the carbon peak year was predicted to be 2044. Then, according to the urbanization level and the proportion of the added value of the secondary industry in different regions in 2018, regions were divided into four categories: the first to reach the peak, the peak on schedule (easy), the peak on schedule (general), and the peak may be delayed. The Beijing-Tianjin-Hebei region plans to achieve a carbon peak by 2044 and proposes specific suggestions to achieve carbon neutrality by 2060 to achieve coordinated development of Beijing-Tianjin-Hebei and high-quality development.
引用
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页数:15
相关论文
共 35 条
[1]   Analysis of the spatial association network structure of China's transportation carbon emissions and its driving factors [J].
Bai, Caiquan ;
Zhou, Lei ;
Xia, Minle ;
Feng, Chen .
JOURNAL OF ENVIRONMENTAL MANAGEMENT, 2020, 253 (253)
[2]   Predicting the carbon dioxide emission of China using a novel augmented hypo-variance brain storm optimisation and the impulse response function [J].
Boamah, Kofi Baah ;
Du, Jianguo ;
Adu, Daniel ;
Mensah, Claudia Nyarko ;
Dauda, Lamini ;
Khan, Muhammad Aamir Shafique .
ENVIRONMENTAL TECHNOLOGY, 2021, 42 (27) :4342-4354
[3]  
Cao J, 2021, STAT DECIS MAK, V37, P79, DOI [10.13546/j.cnki.tjyjc.2021.10.017, DOI 10.13546/J.CNKI.TJYJC.2021.10.017]
[4]   Driving forces for carbon emissions changes in Beijing and the role of green power [J].
Cui, Guangxin ;
Yu, Yadong ;
Zhou, Li ;
Zhang, Hongyu .
SCIENCE OF THE TOTAL ENVIRONMENT, 2020, 728
[5]  
Ding B., 2021, J CHINA AGR RESOUR R, P1
[6]   Estimating Chinese energy-related CO2 emissions by employing a novel discrete grey prediction model [J].
Ding, Song ;
Xu, Ning ;
Ye, Jing ;
Zhou, Weijie ;
Zhang, Xiaoxiong .
JOURNAL OF CLEANER PRODUCTION, 2020, 259
[7]   A novel forecasting approach based on multi-kernel nonlinear multivariable grey model: A case report [J].
Duan, Huiming ;
Wang, Di ;
Pang, Xinyu ;
Liu, Yunmei ;
Zeng, Suhua .
JOURNAL OF CLEANER PRODUCTION, 2020, 260 (260)
[8]   A novel method for carbon dioxide emission forecasting based on improved Gaussian processes regression [J].
Fang, Debin ;
Zhang, Xiaoling ;
Yu, Qian ;
Jin, Trenton Chen ;
Tian, Luan .
JOURNAL OF CLEANER PRODUCTION, 2018, 173 :143-150
[9]   A novel method for carbon emission forecasting based on Gompertz's law and fractional grey model: Evidence from American industrial sector [J].
Gao, Mingyun ;
Yang, Honglin ;
Xiao, Qinzi ;
Goh, Mark .
RENEWABLE ENERGY, 2022, 181 :803-819
[10]   A novel fractional grey Riccati model for carbon emission prediction [J].
Gao, Mingyun ;
Yang, Honglin ;
Xiao, Qinzi ;
Goh, Mark .
JOURNAL OF CLEANER PRODUCTION, 2021, 282