基于LSTM模型的中国交通运输业碳排放预测

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摘要为助力交通运输业实现碳达峰、碳中和目标,本研究基于拓展的STIRPAT模型选取人口、机动车保有量和能源强度等8个变量作为中国交通运输业碳排放量影响因素,并根据1990-2019年指标数据建立LSTM碳排放模型,在低碳、基准及高碳3种情景下对交通运输业碳排放进行预测.结果表明:1990-2019年间中国交通运输业碳排放量总体呈现上升趋势.低碳、基准及高碳情景下,碳排放达峰时间分别为2033年、2035年及2038年,峰值量分别为1145.64,1218.68,1308.40百万t.中国应积极采取节能降碳措施,优化交通运输业结构,推进清洁能源应用,促进中国交通碳排放向低碳情景发展,助力达峰目标早日实现.

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	刘淳森
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	唐稷兴
	高欣悦
	刘莉娜

关键词 ：交通运输, 碳排放预测, LSTM, 情景分析

Abstract：To further achieve the goal of Carbon Peaking and Carbon Neutrality in China's transportation sector, eight variables were selected in this study as key factors driving carbon emissions from the transportation sector, including population, motor vehicle ownership and energy intensity. An LSTM carbon emission model was established with the index data from 1990 to 2019 to forecast the carbon emissions under three scenarios of low-carbon, baseline, and high-carbon. It was shown that the carbon emissions from China's transportation sector exhibited an overall upward trend from 1990 to 2019. Under the low-carbon, baseline and high-carbon scenarios, the carbon peaks would be reached in 2033, 2035 and 2038, with the peaks of 1145.64, 1218.68 and 1308.40 million tons, respectively. China should actively adopt energy-saving and carbon-reducing measures, optimize the structure of the transportation sector, and promote the application of clean energy, so as to achieve the low-carbon scenario and the carbon peak targets as soon as possible.

Key words： transportation carbon emission forecast LSTM scenario analysis

收稿日期: 2022-09-18

PACS:

X73

基金资助:国家自然科学基金资助项目(42171300)

通讯作者: 曲建升,研究员,jsqu@lzb.ac.cn E-mail: jsqu@lzb.ac.cn

作者简介: 刘淳森(1998-),男,河南信阳人,兰州大学硕士研究生,主要从事气候变化研究.liu812081640@163.com.

引用本文:

刘淳森, 曲建升, 葛钰洁, 唐稷兴, 高欣悦, 刘莉娜. 基于LSTM模型的中国交通运输业碳排放预测[J]. 中国环境科学, 2023, 43(5): 2574-2582. LIU Chun-sen, QU Jian-sheng, GE Yu-jie, TANG Ji-xing, GAO Xin-yue, LIU Li-na. LSTM model-based prediction of carbon emissions from China's transportation sector. CHINA ENVIRONMENTAL SCIENCECE, 2023, 43(5): 2574-2582.

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http://www.zghjkx.com.cn/CN/ 或 http://www.zghjkx.com.cn/CN/Y2023/V43/I5/2574

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