IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v13y2021i22p12501-d677650.html
   My bibliography  Save this article

Simulating the Impacts of Hybrid Campus and Autonomous Electric Vehicles as GHG Mitigation Strategies: A Case Study for a Mid-Size Canadian Post-Secondary School

Author

Listed:
  • Bijoy Saha

    (School of Engineering, Civil Engineering, Okanagan Campus, The University of British Columbia, Kelowna, BC V1V 1V7, Canada)

  • Mahmudur Rahman Fatmi

    (School of Engineering, Civil Engineering, Okanagan Campus, The University of British Columbia, Kelowna, BC V1V 1V7, Canada)

Abstract

This paper presents how a post-secondary institution like University of British Columbia’s Okanagan (UBCO) campus can reduce its carbon footprint and be aligned with the government’s target through promoting virtual campus and autonomous electric vehicles (AEVs). Different virtual campus scenarios are developed: online classes only, working-from-home only, and a hybrid of both. In the case of AEVs, alternative penetration rates for levels 2 and 5 are considered. A total of 50 scenarios are tested using a sub-area transport simulation model for UBCO, which is extracted from the regional travel demand forecasting model. The results suggest that a 40% AEV penetration rate coupled with fully in-person classes reduces GHG by ~36% compared to the 2018-level, which will help UBCO to achieve their 2030 emission reduction target and be aligned with the provincial target. The 50% AEV and 10% hybrid virtual campus reduces emissions by ~48%, which is aligned with the 2040 provincial target. A fully virtual campus will help to reach the 2050 provincial target by reducing GHG by ~76%. The results further demonstrate that level 5 AEVs produce lesser emissions than level 2 at a lower AEV penetration rate for the fully in-person campus scenario. At higher penetration rates, level 5 performs better only if it is coupled with 10% of students, faculties and staffs attending virtual campus scenario.

Suggested Citation

  • Bijoy Saha & Mahmudur Rahman Fatmi, 2021. "Simulating the Impacts of Hybrid Campus and Autonomous Electric Vehicles as GHG Mitigation Strategies: A Case Study for a Mid-Size Canadian Post-Secondary School," Sustainability, MDPI, vol. 13(22), pages 1-14, November.
  • Handle: RePEc:gam:jsusta:v:13:y:2021:i:22:p:12501-:d:677650
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/13/22/12501/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/13/22/12501/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Zhang, Runsen & Zhang, Junyi, 2021. "Long-term pathways to deep decarbonization of the transport sector in the post-COVID world," Transport Policy, Elsevier, vol. 110(C), pages 28-36.
    2. Van Zuylen, Henk J. & Willumsen, Luis G., 1980. "The most likely trip matrix estimated from traffic counts," Transportation Research Part B: Methodological, Elsevier, vol. 14(3), pages 281-293, September.
    3. Tejaswini Eregowda & Pritha Chatterjee & Digvijay S. Pawar, 2021. "Impact of lockdown associated with COVID19 on air quality and emissions from transportation sector: case study in selected Indian metropolitan cities," Environment Systems and Decisions, Springer, vol. 41(3), pages 401-412, September.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Dilshad Mohammed & Balázs Horváth, 2023. "Travel Demand Increment Due to the Use of Autonomous Vehicles," Sustainability, MDPI, vol. 15(11), pages 1-20, June.
    2. Nuri C. Onat & Jafar Mandouri & Murat Kucukvar & Burak Sen & Saddam A. Abbasi & Wael Alhajyaseen & Adeeb A. Kutty & Rateb Jabbar & Marcello Contestabile & Abdel Magid Hamouda, 2023. "Rebound effects undermine carbon footprint reduction potential of autonomous electric vehicles," Nature Communications, Nature, vol. 14(1), pages 1-13, December.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Canabarro, N.I. & Silva-Ortiz, P. & Nogueira, L.A.H. & Cantarella, H. & Maciel-Filho, R. & Souza, G.M., 2023. "Sustainability assessment of ethanol and biodiesel production in Argentina, Brazil, Colombia, and Guatemala," Renewable and Sustainable Energy Reviews, Elsevier, vol. 171(C).
    2. Shen, Wei & Wynter, Laura, 2012. "A new one-level convex optimization approach for estimating origin–destination demand," Transportation Research Part B: Methodological, Elsevier, vol. 46(10), pages 1535-1555.
    3. M. Bierlaire & F. Crittin, 2004. "An Efficient Algorithm for Real-Time Estimation and Prediction of Dynamic OD Tables," Operations Research, INFORMS, vol. 52(1), pages 116-127, February.
    4. Castillo, Enrique & Menéndez, José María & Sánchez-Cambronero, Santos, 2008. "Predicting traffic flow using Bayesian networks," Transportation Research Part B: Methodological, Elsevier, vol. 42(5), pages 482-509, June.
    5. Thompson, C.A. & Saxberg, K. & Lega, J. & Tong, D. & Brown, H.E., 2019. "A cumulative gravity model for inter-urban spatial interaction at different scales," Journal of Transport Geography, Elsevier, vol. 79(C), pages 1-1.
    6. Inese Mavlutova & Dzintra Atstaja & Janis Grasis & Jekaterina Kuzmina & Inga Uvarova & Dagnija Roga, 2023. "Urban Transportation Concept and Sustainable Urban Mobility in Smart Cities: A Review," Energies, MDPI, vol. 16(8), pages 1-16, April.
    7. Shao, Hu & Lam, William H.K. & Sumalee, Agachai & Chen, Anthony & Hazelton, Martin L., 2014. "Estimation of mean and covariance of peak hour origin–destination demands from day-to-day traffic counts," Transportation Research Part B: Methodological, Elsevier, vol. 68(C), pages 52-75.
    8. Michel Bierlaire & Frank Crittin, 2006. "Solving Noisy, Large-Scale Fixed-Point Problems and Systems of Nonlinear Equations," Transportation Science, INFORMS, vol. 40(1), pages 44-63, February.
    9. Walpen, Jorgelina & Mancinelli, Elina M. & Lotito, Pablo A., 2015. "A heuristic for the OD matrix adjustment problem in a congested transport network," European Journal of Operational Research, Elsevier, vol. 242(3), pages 807-819.
    10. Li, Baibing & Moor, Bart De, 2002. "Dynamic identification of origin-destination matrices in the presence of incomplete observations," Transportation Research Part B: Methodological, Elsevier, vol. 36(1), pages 37-57, January.
    11. Anselmo Ramalho Pitombeira-Neto & Carlos Felipe Grangeiro Loureiro & Luis Eduardo Carvalho, 2020. "A Dynamic Hierarchical Bayesian Model for the Estimation of day-to-day Origin-destination Flows in Transportation Networks," Networks and Spatial Economics, Springer, vol. 20(2), pages 499-527, June.
    12. Flurin S. Hänseler & Nicholas A. Molyneaux & Michel Bierlaire, 2017. "Estimation of Pedestrian Origin-Destination Demand in Train Stations," Transportation Science, INFORMS, vol. 51(3), pages 981-997, August.
    13. Esteve Codina & Lídia Montero, 2006. "Approximation of the steepest descent direction for the O-D matrix adjustment problem," Annals of Operations Research, Springer, vol. 144(1), pages 329-362, April.
    14. Doblas, Javier & Benitez, Francisco G., 2005. "An approach to estimating and updating origin-destination matrices based upon traffic counts preserving the prior structure of a survey matrix," Transportation Research Part B: Methodological, Elsevier, vol. 39(7), pages 565-591, August.
    15. Chi Xie & Jennifer Duthie, 2015. "An Excess-Demand Dynamic Traffic Assignment Approach for Inferring Origin-Destination Trip Matrices," Networks and Spatial Economics, Springer, vol. 15(4), pages 947-979, December.
    16. Martin, Peter T., 1995. "Turning Movement Estimation In Real Time (TMERT)," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt3rp1v8fs, Institute of Transportation Studies, UC Berkeley.
    17. Wojciech Kazimierz Szczepanek & Maciej Kruszyna, 2022. "The Impact of COVID-19 on the Choice of Transport Means in Journeys to Work Based on the Selected Example from Poland," Sustainability, MDPI, vol. 14(13), pages 1-9, June.
    18. Juha-Matti Kuusinen & Janne Sorsa & Marja-Liisa Siikonen, 2015. "The Elevator Trip Origin-Destination Matrix Estimation Problem," Transportation Science, INFORMS, vol. 49(3), pages 559-576, August.
    19. Kumarage, Sakitha & Yildirimoglu, Mehmet & Zheng, Zuduo, 2023. "A hybrid modelling framework for the estimation of dynamic origin–destination flows," Transportation Research Part B: Methodological, Elsevier, vol. 176(C).
    20. Bagdatli, Muhammed Emin Cihangir & Ipek, Fatima, 2022. "Transport mode preferences of university students in post-COVID-19 pandemic," Transport Policy, Elsevier, vol. 118(C), pages 20-32.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:13:y:2021:i:22:p:12501-:d:677650. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.