基于LSTM神经网络的中国省级碳达峰路径分析

doi:10.16381/j.cnki.issn1003-207x.2022.0097

Abstract

Abstract:

As the world’s largest carbon emitter and second largest economy， China has pledged that its carbon emissions will peak before 2030. Meanwhile， the intensity of carbon emissions will drop by 60%~65% compared to that of 2005. However， due to the varying carbon emissions trajectories of individual provinces， their carbon emissions paths cannot be one-size-fits-all. Three scenarios， i.e.， benchmark， green development， and high-carbon， are designed based on the “14th Five-Year Plan” of each province. Based on LSTM， the carbon emissions of each province from 2020—2040 are predicted under different scenarios. Finally， the appropriate peak paths for individual provinces are analyzed based simultaneously on carbon emission intensity， cumulative carbon emissions and peak time. The results show that the higher the growths rate is， the later the peak time will arrive； China will achieve its carbon emissions peak before 2030， with a peak level of 11884~11792Mt； 24 provinces can achieve the carbon emissions peak before 2030 under at least one scenario； Beijing， Shanghai， Zhejiang， etc. can achieve negative carbon emissions after 2035； An important reference is provided for national policy makers to allocate emission reduction tasks and optimize emission reduction policies.

Key words: carbon emissions peak path, LSTM, neural network, scenario analysis

CLC Number:

F205

Gaomin Zhang, Teng Wang, Yuanyu Lou, Zhongcheng Guan, Haijun Zheng, Qiang Li, Jiaqian Wu. Research on China's Provincial Carbon Emission Peak Path Based on a LSTM Neural Network Approach[J]. Chinese Journal of Management Science, 2025, 33(3): 339-350.

Figures/Tables 10

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增长速率	时期	GDP	城镇化率<80%	城镇化率>=80%	人口	R&D	能源消费	煤炭消费
中速	2020-2025	*	*	*	0.25	*	2	-2.5
	2025-2030	（-0.5）	1.3	0.2	0.05	（1）	2	-2.5
	2030-2035	（-0.5）	0.5	0.2	-0.15	（1）	1	-2
	2035-2040	（-0.5）	0.5	0.2	-0.35	（1）	1	-2

高速	2020-2025	*+1	*+0.2	*+0.2	0.35	*+1	2.5	-1.5
	2025-2030	（-0.5）	1.5	0.4	0.15	（1）	2.5	-1.5
	2030-2035	（-0.5）	0.5	0.4	-0.05	（1）	1.5	-1
	2035-2040	（-0.5）	0.5	0.4	-0.25	（1）	1.5	-1

低速	2020-2025	*-1	*-0.2	*-0.2	0.15	*-1	1.5	-3.5
	2025-2030	（-0.5）	1.1	0	-0.05	(1)	1.5	-3.5
	2030-2035	（-0.5）	0.5	0	-0.25	(1)	0.5	-3
	2035-2040	（-0.5）	0.5	0	-0.45	(1)	0.5	-3

组别	绿色发展情境	基准情境	高碳情境
第一组	北京、天津、上海、浙江、河北、山东	广东、湖南、安徽	重庆、福建、广西、江西、宁夏、黑龙江
第二组	江苏、河南、贵州、辽宁、山西、陕西	湖北、四川、吉林
第三组	内蒙古、新疆、云南		青海、甘肃、海南

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Research on China's Provincial Carbon Emission Peak Path Based on a LSTM Neural Network Approach

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