城市等级划分的交通碳排放影响因素分析
Analysis of Factors Influencing Carbon Emissions from Transportation Considering City Classification
刘博威 1刘晓佳 1张亦弛1
作者信息
- 1. 集美大学航海学院,福建厦门 361021;集美大学海上交通安全研究所,福建厦门 361021
- 折叠
摘要
为分析城市交通碳排放规律,基于拓展STIRPAT模型并结合随机森林特征,选取新能源汽车销量占比、PGDP、公共充电桩数量、第三产业总值、公共汽车(电)营运数量、城市道路货运量6个指标作为城市交通碳排放的影响因素.通过发布的碳排放系数对130个市的城市交通碳排放量进行测算,并利用划分城市交通碳排放等级的方式,根据测算的碳排放量进行等级划分.利用2017-2022年各城市指标数据,建立RF-GS分类预测模型并对碳排放城市等级数量进行预测,同时通过调整交通运输维度影响因素年增长率的方式对城市交通碳排放因素进行分析.结果表明:当新能源汽车占比的年增长率由50％上升到70％时,交通碳排放低等级的城市数量增多8.99％;当公共充电桩建设的年增长率由55％下降到40％时,交通碳排放低等级的城市数量减少14.61％;当公路货运量的年增长率由5％下降到3％时,交通碳排放低等级的城市数量增加3.37％.
Abstract
In order to analyze the carbon emission pattern of urban transportation,this paper selects six indi-cators based on the expanded STIRPAT model combined with random forest feature screening as influencing fac-tors on carbon emissions from urban transportation:the percentage of new energy vehicle sales,PGDP,the number of public charging posts,the total value of tertiary industry,the number of public bus(electric)operations,and the amount of urban road freight.The carbon emissions from urban transportation of 130 cities are measured by the published carbon emission factors,and the carbon emission ranking of urban transportation is classified ac-cording to the measured carbon emissions using the classification of carbon emissions from urban transportation.The RF-GS classification prediction model was used to forecast the number of carbon emission city classes using the index data of each city from 2017-2022,while the influencing factors were analyzed by adjusting the annual growth rate of the influencing factors of transportation dimension.The results show that:the number of cities in low classes of carbon emissions from urban transportation increases 8.99％when the annual growth rate of the percentage of new energy vehicles increases from 50％to 70％;when the annual growth rate of public charging pile construction decreases from 55％to 40％,the number of cities in low classes of carbon emissions from urban transportation decreases by 14.61％;when the annual growth rate of road freight volume decreases from 5％to 3％,the number of cities in the same classes increases by 3.37％.
关键词
城市交通碳排放/STIRPAT模型/分类预测/随机森林Key words
carbon emissions from urban transportation/STIRPAT model/classification prediction/random forest引用本文复制引用
出版年
2024
国家科技期刊平台
NSTL
万方数据