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

Shang, Wen-Long1,11; Chen, Yishui2; Yu, Qing3; Song, Xuewang2; Chen, Yanyan4; Ma, Xiaolei5,6; Chen, Xiqun7,8; Tan, Zhijia9; Huang, Jianling10; Ochieng, Washington11

2023-12-15

10.1016/j.apenergy.2023.121859

APPLIED ENERGY   影响因子和分区

0306-2619

1872-9118

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.

COVID-19 Spatial-temporal analysis Public transport Carbon footprint Carbon neutrality

SCI ; EI

英语

其他

Beijing Natural Science Foundation["9232003","L211027"] ; National Natural Science Foundation of China[72242102] ; R & D Program of Beijing Municipal Education Commission[KM202210005001]

Energy & Fuels ; Engineering

Energy & Fuels ; Engineering, Chemical

WOS:001082061100001

ELSEVIER SCI LTD

20233814761919

Bus transportation ; Buses ; Carbon footprint ; Climate change ; Emission control ; Light rail transit ; Network layers ; Smart cards ; Statistical methods ; Urban transportation

Highway Transportation:432 ; Passenger Highway Transportation:432.2 ; Railroad Transportation:433 ; Railroad Transportation, General:433.1 ; Atmospheric Properties:443.1 ; Air Pollution Control:451.2 ; Environmental Engineering:454 ; Health Care:461.7 ; Heavy Duty Motor Vehicles:663.1 ; Digital Computers and Systems:722.4 ; Computer Software, Data Handling and Applications:723 ; Mathematical Statistics:922.2

ENGINEERING

2-s2.0-85171567052

Web of Science

1.Beijing Univ Technol, Imperial Coll London, Coll Metropolitan Transportat, Beijing Key Lab Traff Engn, London, England
2.Beijing Univ Technol, Coll Metropolitan Transportat, Beijing, Peoples R China
3.Southern Univ Sci & Technol, Res Inst Trustworthy Autonomous Syst, Shenzhen, Peoples R China
4.Beijing Univ Technol, Coll Metropolitan Transportat, Beijing Key Lab Traff Engn, Beijing, Peoples R China
5.Beihang Univ, Sch Transportat Sci Engn, Beijing, Peoples R China
6.Minist Educ, Key Lab Intelligent Transportat Technol & Syst, Beijing, Peoples R China
7.Zhejiang Univ, Inst Intelligent Transportat Syst, Coll Civil Engn & Architecture, Hangzhou, Peoples R China
8.Zhejiang Univ, Univ Illinois Urbana Champaign Inst, Haining, Peoples R China
9.Huazhong Univ Sci & Technol, Wuhan, Peoples R China
10.Beijing Smart Transportat Dev Ctr, Beijing, Peoples R China
11.Imperial Coll London, Ctr Transport Studies, Dept Civil & Environm Engn, London, England

Shang, Wen-Long,Chen, Yishui,Yu, Qing,et al. Spatio-temporal analysis of carbon footprints for urban public transport systems based on smart card data[J]. APPLIED ENERGY,2023,352.

Shang, Wen-Long.,Chen, Yishui.,Yu, Qing.,Song, Xuewang.,Chen, Yanyan.,...&Ochieng, Washington.(2023).Spatio-temporal analysis of carbon footprints for urban public transport systems based on smart card data.APPLIED ENERGY,352.

Shang, Wen-Long,et al."Spatio-temporal analysis of carbon footprints for urban public transport systems based on smart card data".APPLIED ENERGY 352(2023).