lynx   »   [go: up one dir, main page]

IDEAS home Printed from https://ideas.repec.org/p/ecr/col033/41812.html
   My bibliography  Save this paper

Promoting energy efficiency in government transportation systems: A transition roadmap and criteria for a readiness analysis

Author

Listed:
  • Flores, Adrián
  • Hidalgo Arellano, Marcos
  • Peralta Quesada, Leda
Abstract
The present study explores opportunities and challenges to increase energy efficiency in government vehicle fleets through electrification. It identifies international best practices in relation to fleet electrification, suggests the most suitable comprehensive approach for a fleet transition, and recommends the most immediate actions to deploy. Considering the leading role that the public sector plays in promoting the use of renewable energies and enhancing energy efficiency, the study presents a roadmap for government fleet transitions of vehicles that have equivalent alternatives in the market.

Suggested Citation

  • Flores, Adrián & Hidalgo Arellano, Marcos & Peralta Quesada, Leda, 2017. "Promoting energy efficiency in government transportation systems: A transition roadmap and criteria for a readiness analysis," Studies and Perspectives – ECLAC Subregional Headquarters for The Caribbean 41812, Naciones Unidas Comisión Económica para América Latina y el Caribe (CEPAL).
  • Handle: RePEc:ecr:col033:41812
    as

    Download full text from publisher

    File URL: http://repositorio.cepal.org/handle/11362/41812
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Cedric De Cauwer & Joeri Van Mierlo & Thierry Coosemans, 2015. "Energy Consumption Prediction for Electric Vehicles Based on Real-World Data," Energies, MDPI, vol. 8(8), pages 1-21, August.
    2. Guerra, Sergio, 2016. "Energy efficiency policies in the Caribbean: a manual to guide the discussion," Sede Subregional de la CEPAL para el Caribe (Estudios e Investigaciones) 40459, Naciones Unidas Comisión Económica para América Latina y el Caribe (CEPAL).
    Full references (including those not matched with items on IDEAS)

    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. Katsaprakakis, Dimitris Al & Voumvoulakis, Manolis, 2018. "A hybrid power plant towards 100% energy autonomy for the island of Sifnos, Greece. Perspectives created from energy cooperatives," Energy, Elsevier, vol. 161(C), pages 680-698.
    2. Huang, Hai-chao & He, Hong-di & Peng, Zhong-ren, 2024. "Urban-scale estimation model of carbon emissions for ride-hailing electric vehicles during operational phase," Energy, Elsevier, vol. 293(C).
    3. Cox, Brian & Bauer, Christian & Mendoza Beltran, Angelica & van Vuuren, Detlef P. & Mutel, Christopher L., 2020. "Life cycle environmental and cost comparison of current and future passenger cars under different energy scenarios," Applied Energy, Elsevier, vol. 269(C).
    4. Manfred Dollinger & Gerhard Fischerauer, 2023. "Physics-Based Prediction for the Consumption and Emissions of Passenger Vehicles and Light Trucks up to 2050," Energies, MDPI, vol. 16(8), pages 1-29, April.
    5. Jin-Young Kim & Sung-Bae Cho, 2019. "Electric Energy Consumption Prediction by Deep Learning with State Explainable Autoencoder," Energies, MDPI, vol. 12(4), pages 1-14, February.
    6. Nils Hooftman & Luis Oliveira & Maarten Messagie & Thierry Coosemans & Joeri Van Mierlo, 2016. "Environmental Analysis of Petrol, Diesel and Electric Passenger Cars in a Belgian Urban Setting," Energies, MDPI, vol. 9(2), pages 1-24, January.
    7. Zhang, Zhaosheng & Wang, Ruiyang & Liu, Peng & Wang, Zhenpo & Lin, Ni & Liang, Yiqiang & Tang, Chaoyang & Xia, Ling, 2025. "Research on energy consumption law and charging strategies design of electric buses," Energy, Elsevier, vol. 322(C).
    8. Jiangbo Wang & Kai Liu & Toshiyuki Yamamoto, 2017. "Improving Electricity Consumption Estimation for Electric Vehicles Based on Sparse GPS Observations," Energies, MDPI, vol. 10(1), pages 1-12, January.
    9. Karla Schröder & Gonzalo Farias & Sebastián Dormido-Canto & Ernesto Fabregas, 2024. "Comparative Analysis of Deep Learning Methods for Fault Avoidance and Predicting Demand in Electrical Distribution," Energies, MDPI, vol. 17(11), pages 1-13, June.
    10. Teresa Pamuła & Wiesław Pamuła, 2020. "Estimation of the Energy Consumption of Battery Electric Buses for Public Transport Networks Using Real-World Data and Deep Learning," Energies, MDPI, vol. 13(9), pages 1-17, May.
    11. Sunil Kumar Mohapatra & Sushruta Mishra & Hrudaya Kumar Tripathy & Akash Kumar Bhoi & Paolo Barsocchi, 2021. "A Pragmatic Investigation of Energy Consumption and Utilization Models in the Urban Sector Using Predictive Intelligence Approaches," Energies, MDPI, vol. 14(13), pages 1-28, June.
    12. Miao, Hongzhi & Jia, Hongfei & Li, Jiangchen & Qiu, Tony Z., 2019. "Autonomous connected electric vehicle (ACEV)-based car-sharing system modeling and optimal planning: A unified two-stage multi-objective optimization methodology," Energy, Elsevier, vol. 169(C), pages 797-818.
    13. Li, Pengshun & Zhang, Yuhang & Zhang, Yi & Zhang, Yi & Zhang, Kai, 2021. "Prediction of electric bus energy consumption with stochastic speed profile generation modelling and data driven method based on real-world big data," Applied Energy, Elsevier, vol. 298(C).
    14. Robert Pietracho & Christoph Wenge & Stephan Balischewski & Pio Lombardi & Przemyslaw Komarnicki & Leszek Kasprzyk & Damian Burzyński, 2021. "Potential of Using Medium Electric Vehicle Fleet in a Commercial Enterprise Transport in Germany on the Basis of Real-World GPS Data," Energies, MDPI, vol. 14(17), pages 1-23, August.
    15. Zhao, Li & Ke, Hanchen & Huo, Weiwei, 2023. "A frequency item mining based energy consumption prediction method for electric bus," Energy, Elsevier, vol. 263(PD).
    16. Qiu, K. & Ribberink, H. & Entchev, E., 2022. "Economic feasibility of electrified highways for heavy-duty electric trucks," Applied Energy, Elsevier, vol. 326(C).
    17. Xing, Yang & Lv, Chen & Cao, Dongpu & Lu, Chao, 2020. "Energy oriented driving behavior analysis and personalized prediction of vehicle states with joint time series modeling," Applied Energy, Elsevier, vol. 261(C).
    18. Fath U Min Ullah & Noman Khan & Tanveer Hussain & Mi Young Lee & Sung Wook Baik, 2021. "Diving Deep into Short-Term Electricity Load Forecasting: Comparative Analysis and a Novel Framework," Mathematics, MDPI, vol. 9(6), pages 1-22, March.
    19. Roman Michael Sennefelder & Rubén Martín-Clemente & Ramón González-Carvajal, 2023. "Energy Consumption Prediction of Electric City Buses Using Multiple Linear Regression," Energies, MDPI, vol. 16(11), pages 1-14, May.
    20. Hamza Mediouni & Amal Ezzouhri & Zakaria Charouh & Khadija El Harouri & Soumia El Hani & Mounir Ghogho, 2022. "Energy Consumption Prediction and Analysis for Electric Vehicles: A Hybrid Approach," Energies, MDPI, vol. 15(17), pages 1-17, September.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    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:ecr:col033:41812. 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: Biblioteca CEPAL (email available below). General contact details of provider: https://edirc.repec.org/data/eclaccl.html .

    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.
    Лучший частный хостинг