Electric bus fleet transition: assessment approach considering economic and environmental impacts, and its application

Zimeng Ye, Ziling Huang, Shuyuan Yang, Yuze Du, Hongmei Zhao

Smart Construction and Sustainable Cities ›› 2024, Vol. 2 ›› Issue (1) : 17.

Smart Construction and Sustainable Cities ›› 2024, Vol. 2 ›› Issue (1) : 17. DOI: 10.1007/s44268-024-00040-8
Research

Electric bus fleet transition: assessment approach considering economic and environmental impacts, and its application

Author information +
History +

Abstract

In our society, global warming is considered one of the most serious problems. According to scientists, the world has been warmed by 3 degrees per year, which will be catastrophic to our world. To reduce CO2 emission, an electric bus is one way to solve the problem. In this article, we use four different models: Multiple Linear Regression (MLR), Autoregressive Integrated Moving Average Model (ARIMA), Ecological Assessment Model, Bus Fleet Replacement Financial Model, and Integer Programming Model to determine the number of carbon emissions, the least money that government need to spend on transitions, and future blueprint; these help to predict the overall benefits for countries turn into absolutely electric bus society. Our research stands from the sustainable point of view; we view better environment as the goal. By applying these models to three different countries: London, and Toronto, and Philadelphia which is our main focus, we find out that the air quality will be increased by reducing different kinds of pollution. Moreover, by constructing a ten-year blueprint, we find out the best way to spend least money and make the environment gradually become better.

Cite this article

Download citation ▾
Zimeng Ye, Ziling Huang, Shuyuan Yang, Yuze Du, Hongmei Zhao. Electric bus fleet transition: assessment approach considering economic and environmental impacts, and its application. Smart Construction and Sustainable Cities, 2024, 2(1): 17 https://doi.org/10.1007/s44268-024-00040-8

References

[1.]
HuangHC, HeHD, PengZR. Urban-scale estimation model of carbon emissions for ride-hailing electric vehicles during operational phase. Energy, 2024, 293: 130665
CrossRef Google scholar
[2.]
ZhaoHM, HeHD, LuDN, ZhouD, LuCX, FangXR, PengZR. Evaluation of CO2 and NOx emissions from container diesel trucks using a portable emissions measurement system. Building and Environment., 2024, 252: 111266
CrossRef Google scholar
[3.]
Dahlman, R. L. A. L. (2024). Climate change: Global temperature. NOAA Climate gov. https://www.climate.gov/news-features/understanding-climate/climate-change-global-temperature
[4.]
LimLK, Ab MuisZ, HashimH, HoWS, IdrisMNM. Potential of electric bus as a carbon mitigation strategies and energy modelling: a review. Chemical Engineering Transactions, 2021, 89: 529-534
CrossRef Google scholar
[5.]
Shahariar G M H , Sajjad M , Suara K A ,et al (2022). On-road CO2 and NOx emissions of a diesel vehicle in urban traffic. Transportation research, Part D. Transport and environment, 107, 103326. https://doi.org/10.1016/j.trd.2022.103326
[6.]
Electric buses: Definition and benefits. Enel X. (n.d.). https://corporate.enelx.com/en/question-and-answers/what-is-electric-bus
[7.]
Huang, H.C, Li, B.W, Wang, Y.Z, Zhang Z. He, H.D (2024). Analysis of factors influencing energy consumption of electric vehicles: Statistical, predictive, and causal perspectives. 375, 124110. https://doi.org/10.1016/j.apenergy.2024.124110
[8.]
[9.]
XiaoGN, XiaoY, ShuYQ, NiAN, JiangZR. Technical and economic analysis of battery electric buses with different charging rates. Transportation Research Part D: Transport and Environment., 2024, 132: 104254
CrossRef Google scholar
[10.]
Boak, J. (2023). The US government is awarding $1.7 billion to buy electric and low-emission buses. AP News. https://apnews.com/article/biden-bus-electric-grant-battery-buttigieg-434960af2b57fb33996288052c616230
[11.]
Tomczuk, J. (2023). SEPTA gets federal money to begin transition to electric buses. Metro Philadelphia. https://metrophiladelphia.com/septa-federal-money-electric-buses/
[12.]
ZhouY, OngGP, MengQ. The road to electrification: Bus fleet replacement strategies. Applied Energy, 2023, 337: 120903
CrossRef Google scholar
[13.]
HamurcuM, ErenT. Electric bus selection with multicriteria decision analysis for Green Transportation. Sustainability, 2020, 12(7): 2777
CrossRef Google scholar
[14.]
KonečnýV, GnapJ, SetteyT, PetroF, SkrúcanýT, FiglusT. Environmental sustainability of the vehicle fleet change in Public City Transport of Selected City in Central Europe. Energies, 2020, 13(15): 3869
CrossRef Google scholar
[15.]
Caballini, C., Sacone, S., & Saeednia, M. (2023). Transition to zero emissions: Cooperative planning of carriers’ trips using electric and Diesel Vehicles. 2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC). https://doi.org/10.1109/itsc57777.2023.10421833
[16.]
HellwegS, Milà i CanalsL. Emerging approaches, challenges and opportunities in life cycle assessment. Science, 2014, 344(6188): 1109-1113
CrossRef Google scholar
[17.]
HellwegS, BenettoE, HuijbregtsMAJ, VeronesF, WoodR. Life-cycle assessment to guide solutions for the triple planetary crisis. Nature Reviews Earth & Environment., 2023, 4(7): 471-486
CrossRef Google scholar
[18.]
Liu J. P. (2015). Analysis on energy conservation and emissions reduction and the development path of new energy electric vehicle in China. Master thesis of Beijing Institute of Technology. (in Chinese)
[19.]
Zhang A. L. (2008). Life cycle analysis of automotive alternative fuel. Tsinghua University Press. ISBN: 9787302182818. (in Chinese)
[20.]
UyanıkGK, GülerN. A study on multiple linear regression analysis. Procedia - Social and Behavioral Sciences, 2013, 106: 234-240
CrossRef Google scholar
[21.]
LiaoY. Ride-sourcing compared to its public-transit alternative using big trip data. Journal of Transport Geography., 2021, 95: 103135
CrossRef Google scholar
[22.]
LiuX, ShiXQ, LiXB, PengZR. Quantification of multifactorial effects on particle distributions at urban neighborhood scale using machine learning and unmanned aerial vehicle measurement. Journal of cleaner production., 2022, 378: 134494
CrossRef Google scholar
[23.]
HeHD, LuDN, ZhaoHM, PengZR. Characterizing CO2 and NOx emission of vehicles crossing toll stations in highway. Transportation Research Part D, 2024, 126: 104024
CrossRef Google scholar
[24.]
KoyuncuK, TavacioluL, GkmenN, AricanUE. Forecasting COVID-19 impact on RWI/ISL container throughput index by using SARIMA models. Maritime Policy & Management., 2021, 48: 1-13
CrossRef Google scholar
[25.]
Siami-Namini, S., Tavakoli, N., & Siami Namin, A. (2018). A comparison of ARIMA and LSTM in forecasting time series. 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA). https://doi.org/10.1109/icmla.2018.00227
[26.]
DuH, KommalapatiRR. Environmental sustainability of public transportation fleet replacement with electric buses in Houston, a megacity in the USA. International Journal of Sustainable Engineering, 2021, 14(6): 1858-1870
CrossRef Google scholar
[27.]
AlvesMJ, ClímacoJ. A review of interactive methods for multiobjective integer and mixed-integer programming. European Journal of Operational Research, 2007, 180(1): 99-115
CrossRef Google scholar
[28.]
MaXL, YanHY, MiaoR. Optimization Model of Electric Bus Fleet Replacement Considering Financial Subsidies. Journal of Transportation Systems Engineering and Information Technology, 2021, 21(3): 200-205(in Chinese)
[29.]
PelletierS, JabaliO, MendozaJE, LaporteG. The Electric Bus Fleet Transition Problem. Transportation Research Part C: Emerging Technologies, 2019, 109: 174-193
CrossRef Google scholar
[30.]
DelialiA, ChhanD, OliverJ, SayessR, Godri PollittKJ, ChristofaE. Transitioning to zero-emission bus fleets: State of practice of implementations in the United States. Transport Reviews, 2020, 41(2): 164-191
CrossRef Google scholar

Accesses

Citations

Detail

Sections
Recommended

/