Scenario Analysis of Carbon Peak Based on PSOBP
DOI:
https://doi.org/10.54097/hset.v67i.11547Keywords:
China, Carbon peak, Scenario analysis, PSO-BPNN.Abstract
To investigate the carbon peak scenario and corresponding time in China, this paper firstly employed the grey correlation test to determine the factors influencing carbon emissions; Secondly, PSO-BPNN neural network combined with scenario analysis was used to construct a unique scenario analysis model of China's carbon emissions; Results revealed that: the actual amount of foreign capital utilized, diesel consumption and China's urbanization rate are the three most influential factors on China's carbon emissions. The "double carbon" goals have a long way to go and achieving the carbon peak ahead of schedule requires accelerating the low-carbon energy transition and actively optimizing and upgrading industries but also relies on technological innovation and the promotion of advanced technologies.
Downloads
References
Liu Yanhui, Li Yang. Carbon emission measurement and decomposition of influencing factors of transportation industry in Hubei Province[J/OL]. Statistics and Decision Making,2022(15):88-92[2022-1004].
Xu Guoquan, Liu Zeyuan, Jiang Zhaohua. Factor decomposition model and empirical analysis of carbon emissions in China:1995-2004[J]. China Population, Resources and Environment, 2006(06):158-161.
Liu Yihong, Zhang Zhen, Chen Hongwei, Zou Jianan, Hu Bo. Evaluation of the impact of future vegetation changes on "carbon peaking and carbon neutral" in Jiangsu Province[J]. Meteorology and Disaster Mitigation Research,2022,45(01):46-52.
Hu Jianbo, Luo Zhipeng, Li Feng. Carbon emission intensity prediction in China under the "carbon peak" target: an analysis based on LSTM and ARIMA-BP models[J]. Finance and Economics Science,2022(02):89-101.
Wang Jingjing, Wei Jiajia. The characteristics and scenario simulation of carbon emission change of building operation in Beijing under time series[J]. Journal of Beijing University of Technology,2022,48(03):220-229.
Jin Lin, Ma Zhongyun, Wang Honghong. Carbon emission trading price prediction based on gray B P neural network[J]. Journal of Hebei Institute of Environmental Engineering,2020,30(01):27-32+41.
Wang Huiru,Feng Xuejun,Shen Jinxing,Zhang Yan,Wang Haipeng. SVR-based prediction of carbon emission peaking in transportation industry in Jiangsu Province[J]. Environmental Science and Technology,2022,35(03): 58-63.
Li, J., Cheng, Jh., Shi, Jy., Huang, F. (2012). Brief Introduction of Back Propagation (BP) Neural Network Algorithm and Its Improvement. In: Jin, D., Lin, S. (eds) Advances in Computer Science and Information Engineering. Advances in Intelligent and Soft Computing, vol 169. Springer, Berlin, Heidelberg.
Xu Dingjian. An improved PSO-BP neural network red tide prediction model based on principal component analysis[J]. Mapping Bulletin, 2021(000-S02).
Liu X, Liu Z, Liang Z, Zhu SP, Correia JAFO, De Jesus AMP. PSO-BP Neural Network-Based Strain Prediction of Wind Turbine Blades. Materials (Basel). 2019 Jun 12;12(12):1889.
Huang R, Wang Z. 2013. Influencing factors of carbon emissions from energy consumptions in Chongqing based on STIRPAT model [J]. Acta Scientiae Circumstantiae, 33(2) : 602-608
CHEN Cao-cao,LIU Chun-lan,WANG Hao,GUAN Jing,CHEN Long,WANG Hai-hua,ZHANG Ji-ping,LI Zheng,LIU Xiao-jie. Analysis of influencing factors of carbon footprint of energy consumption in Beijing——Based on STIRPAT model and small squares model[J]. China Environmental Science,2014,34(06):1622-1632.
ZHANG Cuiju,QIN Mingfeng. Cointegration analysis of influencing factors of carbon emission intensity in China based on time series data[J]. Ecological Economics,2017,33(03):53-56.
ZHANG Jian,LIU Jingyang,DONG Li,QIAO Qi. Influencing factors and scenario analysis of China's energy consumption CO_(2) [J/OL].Journal of Environmental Engineering and Technology:1-11[2022-10-02].
Li Shanshan,Siu Yam Wing,Zhao Guoqin. Driving Factors of CO2 Emissions: Further Study Based on Machine Learning[J]. Frontiers in Environmental Science,2021,9.
WANG Libing,ZHANG Yun. Decomposition and scenario prediction of energy carbon emission factors in China[J].Electric Power Construction,2021,42(09):1-9.
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
