IDEAS home Printed from https://ideas.repec.org/a/eee/enepol/v94y2016icp285-294.html

Life cycle costing of diesel, natural gas, hybrid and hydrogen fuel cell bus systems: An Australian case study

Author

Listed:
  • Ally, Jamie
  • Pryor, Trevor

Abstract

The transit authority in Perth, Western Australia, has put several alternative fuel buses, including diesel-electric hybrid and hydrogen fuel cell buses, into revenue service over the years alongside conventional diesel and natural gas buses. Primary data from this fleet is used to construct a Life Cycle Cost (LCC) model, providing an empirical LCC result. The model is then used to forecast possible scenarios using cost estimates for next generation technologies. The methodology follows the Australian/New Zealand Standard for Life Cycle Costing, AS/NZS 4536:1999. The model outputs a dollar value in real terms that represents the LCC of each bus transportation technology. The study finds that Diesel buses deliver the lowest Total Cost of Ownership (TCO). The diesel-electric hybrid bus was found to have a TCO that is about 10% higher than conventional diesel. The premium to implement and operate a hydrogen bus, even if industry targets are attained, is still substantially greater than the TCO of a conventional diesel bus, unless a very large increase in the diesel fuel price occurs. However, the hybrid and hydrogen technologies are still very young in comparison to diesel and economies of scale are yet to be realised.

Suggested Citation

  • Ally, Jamie & Pryor, Trevor, 2016. "Life cycle costing of diesel, natural gas, hybrid and hydrogen fuel cell bus systems: An Australian case study," Energy Policy, Elsevier, vol. 94(C), pages 285-294.
    • Handle: RePEc:eee:enepol:v:94:y:2016:i:c:p:285-294
    DOI: 10.1016/j.enpol.2016.03.039
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0301421516301422
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.enpol.2016.03.039?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Chen, Shikuan & Chang, Ming-Jen, 2015. "Capital control and exchange rate volatility," The North American Journal of Economics and Finance, Elsevier, vol. 33(C), pages 167-177.
    2. Li, Wu, 2015. "The Exchange Function and A Dynamic Exchange Model," MPRA Paper 68529, University Library of Munich, Germany.
    3. G. Elliott & C. Granger & A. Timmermann (ed.), 2013. "Handbook of Economic Forecasting," Handbook of Economic Forecasting, Elsevier, edition 1, volume 2, number 2.
    4. Colin J. Cockroft & Anthony D. Owen, 2007. "The Economics of Hydrogen Fuel Cell Buses," The Economic Record, The Economic Society of Australia, vol. 83(263), pages 359-370, December.
    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. Carstensen, Kai & Heinrich, Markus & Reif, Magnus & Wolters, Maik H., 2020. "Predicting ordinary and severe recessions with a three-state Markov-switching dynamic factor model," International Journal of Forecasting, Elsevier, vol. 36(3), pages 829-850.
    2. Janet L. Yellen, 2015. "Inflation Dynamics and Monetary Policy : A speech at the Philip Gamble Memorial Lecture, University of Massachusetts, Amherst, Amherst, Massachusetts, September 24, 2015," Speech 863, Board of Governors of the Federal Reserve System (U.S.).
    3. Beckmann, Joscha & Czudaj, Robert L. & Arora, Vipin, 2020. "The relationship between oil prices and exchange rates: Revisiting theory and evidence," Energy Economics, Elsevier, vol. 88(C).
    4. Shirai, Daichi, 2016. "Persistence and Amplification of Financial Frictions," MPRA Paper 72187, University Library of Munich, Germany.
    5. Aleksandra Riedl & Julia Wörz, 2018. "A simple approach to nowcasting GDP growth in CESEE economies," Focus on European Economic Integration, Oesterreichische Nationalbank (Austrian Central Bank), issue Q4/18, pages 56-74.
    6. Lindé, Jesper & Smets, Frank & Wouters, Rafael, 2016. "Challenges for Central Banks´ Macro Models," Working Paper Series 323, Sveriges Riksbank (Central Bank of Sweden).
    7. Goodness C. Aye & Mehmet Balcilar & Rangan Gupta, 2017. "International stock return predictability: Is the role of U.S. time-varying?," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 44(1), pages 121-146, February.
    8. Magnus Reif, 2020. "Macroeconomics, Nonlinearities, and the Business Cycle," ifo Beiträge zur Wirtschaftsforschung, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 87, October.
    9. Tomasz Woźniak, 2016. "Bayesian Vector Autoregressions," Australian Economic Review, The University of Melbourne, Melbourne Institute of Applied Economic and Social Research, vol. 49(3), pages 365-380, September.
    10. Michael Melvin & Frank Westermann, 2022. "Chinese Exchange Rate Policy: Lessons for Global Investors," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 54(1), pages 145-168, February.
    11. Ahmed, Shamim & Liu, Xiaoquan & Valente, Giorgio, 2016. "Can currency-based risk factors help forecast exchange rates?," International Journal of Forecasting, Elsevier, vol. 32(1), pages 75-97.
    12. Mikuláš Gangur & Miroslav Plevný, 2014. "Tools for Consumer Rights Protection in the Prediction of Electronic Virtual Market and Technological Changes," The AMFITEATRU ECONOMIC journal, Academy of Economic Studies - Bucharest, Romania, vol. 16(36), pages 578-578, May.
    13. Javier Ojea Ferreiro, 2018. "Contagion spillovers between sovereign and financial European sector from a Delta CoVaR approach," Documentos de Trabajo del ICAE 2018-12, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico.
    14. Harvey, David I. & Leybourne, Stephen J. & Whitehouse, Emily J., 2017. "Forecast evaluation tests and negative long-run variance estimates in small samples," International Journal of Forecasting, Elsevier, vol. 33(4), pages 833-847.
    15. Döpke Jörg & Fritsche Ulrich & Waldhof Gabi, 2019. "Theories, Techniques and the Formation of German Business Cycle Forecasts : Evidence from a survey of professional forecasters," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 239(2), pages 203-241, April.
    16. Ferrari, Davide & Ravazzolo, Francesco & Vespignani, Joaquin, 2021. "Forecasting energy commodity prices: A large global dataset sparse approach," Energy Economics, Elsevier, vol. 98(C).
    17. Christiane Baumeister & Lutz Kilian, 2015. "Forecasting the Real Price of Oil in a Changing World: A Forecast Combination Approach," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 33(3), pages 338-351, July.
    18. Tatevik Sekhposyan & Barbara Rossi, 2015. "Macroeconomic Uncertainty Indices for the Euro Area and Individual Member Countries," Working Papers 820, Barcelona School of Economics.
    19. Karlsson, Sune & Mazur, Stepan & Nguyen, Hoang, 2023. "Vector autoregression models with skewness and heavy tails," Journal of Economic Dynamics and Control, Elsevier, vol. 146(C).
    20. Manuel M. F. Martins & Fabio Verona, 2020. "Forecasting Inflation with the New Keynesian Phillips Curve: Frequency Matters," CEF.UP Working Papers 2001, Universidade do Porto, Faculdade de Economia do Porto.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;

    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:eee:enepol:v:94:y:2016:i:c:p:285-294. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/enpol .

    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.