摘要
为探索河南省碳达峰路径,满足河南省碳达峰战略需求,选取河南省2001—2020年社会、经济、能耗、资源4个维度的12个指标,使用Lasso-BP神经网络方法,建立河南省碳排放量预测模型。基于12个指标数据的回归分析,设计了6条发展路径,对河南省2021—2035年的碳排放量进行预测。结果表明:1)12个维度的因素中,影响碳达峰的6大关键因素为煤炭消费占比、单位GDP能耗、森林覆盖率、能源消费总量、第二产业GDP比重和私人汽车拥有量;2)追求单因素发展的路径1—4均无法在2030年实现碳达峰;在路径5、6下,河南省将于2029年碳达峰,相较于路径5,路径6的峰值排放量低2.53 Mt,峰值为510.91 Mt CO_(2);3)为实现碳达峰,在“十四五”和“十五五”阶段,应分别将煤炭消费占比、单位GDP能耗、森林覆盖率、能源消费总量、第二产业GDP比重、私人汽车拥有量的年均增长率控制在-4.0%和-5.0%、-3.5%和-4.0%、2.0%和3.0%、0.5%和0.4%、-1.5%和-2.0%、7.5%和7.0%。
To explore the path to carbon emission peaking of Henan, China and meet the strategic need of the local authorities, in this paper, the data of twelve factors of social, economic, energy consumption, and resources in Henan from 2001 to 2020 were selected, and the Lasso-BP neural network method was used to establish a prediction model of carbon emission in Henan province. Based on the regression analysis of the data of the 12 indexes, six different development paths were designed to predict the carbon emission of Henan from 2021 to 2035. The results showed that: 1) among the twelve factors, six key factors affecting carbon peaking were the share of coal consumption, energy consumption per unit of GDP, forest coverage, total energy consumption, the share of secondary industry GDP, and private car ownership;2) paths 1 to 4 pursuing single-factor development were all unable to achieve carbon peaking in 2030. Under paths 5 and 6, Henan would reach peak carbon in 2029. Compared with path 5, the peak CO_(2)emission of path 6 was 2.53 Mt lower, with a peak of 510.91 Mt CO_(2);3) to achieve the carbon emission peak, the average annual growth rates of coal consumption, energy consumption per unit of GDP, forest coverage, total energy consumption, the share of secondary industry in GDP, and private car ownership should be controlled at-4.0% and-5.0%,-3.5% and-4.0%, 2.0% and 3.0%, 0.5% and 0.4%,-1.5% and-2.0%,7.5% and 7.0%, during the 14th Five-Year Plan and the 15th Five-Year Plan Period respectively.
作者
赵金辉
李景顺
王潘乐
侯高杰
ZHAO Jinhui;LI Jingshun;WANG Panle;HOU Gaojie(School of Mechanical and Power Engineering,Zhengzhou University,Zhengzhou 450001,China)
出处
《环境工程》
CAS
CSCD
北大核心
2022年第12期151-156,164,共7页
Environmental Engineering
基金
中国工程院重大咨询研究项目(2020-ZD-18-5)。
