Spatio-temporal analysis of carbon footprints for urban public transport systems based on smart card data

碳足迹 北京 温室气体 公共交通 足迹 碳纤维 环境科学 环境工程 运输工程 计算机科学 地理 工程类 生态学 考古 中国 算法 复合数 生物
作者
Wen-Long Shang,Yishui Chen,Qing Yu,Xuewang Song,Yanyan Chen,Xiaolei Ma,Xiqun Chen,Zhijia Tan,Jianling Huang,Washington Ochieng
出处
期刊:Applied Energy [Elsevier BV]
卷期号:352: 121859-121859 被引量:28
标识
DOI:10.1016/j.apenergy.2023.121859
摘要

The increasing severity of global climate change has made reductions in carbon emissions an urgent global issue. The relative lack of carbon footprint analyses of urban public transportation systems (UPTS) is therefore surprising, given that UPTS is an important component of urban transportation and one that may play a crucial role in carbon emission reduction. This study conducts a spatio-temporal analysis of carbon footprints for UPTS during the COVID-19 pandemic based on smart card data in Beijing. Since the core of carbon footprint calculation is to estimate travellers' trip trajectories and the ridership of urban rail transit (URT) and buses, we construct a novel multi-layer urban rail network model to calculate passenger volume and travellers' trajectories through a traffic assignment model. Furthermore, we utilize the Generalized Additive Model (GAM) to analyse the correlation relationship between the carbon footprints of buses and URT. Additionally, we conduct statistical analysis of the carbon footprint of UPTS. The results of the spatio-temporal analysis of carbon footprints for UPTS show significantly lower carbon emissions during holidays compared to those on working days, and emissions during peak hours contribute approximately half of the total daily UPTS emissions, while there are notable variations in the distribution of the carbon footprint among different districts. Moreover, our analysis reveals a positive correlation between the carbon footprints of buses and URT. The statistical analysis reflects different patterns of carbon footprint distribution on different dates during the pandemic, but the carbon footprint distributions on selected dates all follow a power-law distribution. This study may facilitate the understanding to the impacts of UPTS on the environment during the COVID-19 pandemic, and also provide important guidance and reference for the development of carbon emission reduction strategies.

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
暗芒完成签到,获得积分10
1秒前
爆米花应助guoxihan采纳,获得10
2秒前
lindalin发布了新的文献求助10
3秒前
字斟句酌发布了新的文献求助20
4秒前
5秒前
鱼粉发布了新的文献求助10
5秒前
6秒前
ningmengcao完成签到,获得积分10
6秒前
6秒前
通科研发布了新的文献求助10
8秒前
痴情的阁完成签到,获得积分10
8秒前
F二次方应助沐风采纳,获得20
9秒前
认真的不评完成签到,获得积分10
10秒前
zyp发布了新的文献求助10
10秒前
hecarli完成签到,获得积分0
11秒前
11秒前
善学以致用应助科研人采纳,获得10
12秒前
柱zzz完成签到,获得积分10
15秒前
15秒前
sjmrcsj发布了新的文献求助10
18秒前
字斟句酌完成签到,获得积分10
19秒前
21秒前
lok发布了新的文献求助10
21秒前
溜溜蛋发布了新的文献求助10
24秒前
科研人发布了新的文献求助10
26秒前
典雅的醉柳完成签到,获得积分10
26秒前
田様应助高晨采纳,获得10
27秒前
研友_ZeqAxZ完成签到,获得积分0
29秒前
30秒前
慌慌完成签到 ,获得积分10
30秒前
30秒前
31秒前
娇1994完成签到,获得积分10
31秒前
骆凤灵发布了新的文献求助10
31秒前
科目三应助刘振扬采纳,获得10
33秒前
33秒前
BAI_1完成签到,获得积分10
33秒前
苹果元槐发布了新的文献求助10
35秒前
何y发布了新的文献求助10
36秒前
yaowei完成签到,获得积分10
37秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
PowerCascade: A Synthetic Dataset for Cascading Failure Analysis in Power Systems 2000
Signals, Systems, and Signal Processing 610
Unlocking Chemical Thinking: Reimagining Chemistry Teaching and Learning 555
Photodetectors: From Ultraviolet to Infrared 500
On the Dragon Seas, a sailor's adventures in the far east 500
Yangtze Reminiscences. Some Notes And Recollections Of Service With The China Navigation Company Ltd., 1925-1939 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 物理 内科学 复合材料 催化作用 物理化学 光电子学 电极 细胞生物学 基因 无机化学
热门帖子
关注 科研通微信公众号,转发送积分 6355929
求助须知:如何正确求助?哪些是违规求助? 8170753
关于积分的说明 17202051
捐赠科研通 5411996
什么是DOI,文献DOI怎么找? 2864440
邀请新用户注册赠送积分活动 1841940
关于科研通互助平台的介绍 1690226