IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v189y2019ics0360544219319000.html
   My bibliography  Save this article

Cost-effectiveness analysis of energy efficiency measures for maritime shipping using a metamodel based approach with different data sources

Author

Listed:
  • Yuan, Jun
  • Nian, Victor
  • He, Junliang
  • Yan, Wei

Abstract

A large number of mitigation strategies including both technical and operational measures have been proposed to reduce ship energy consumption and hence carbon emissions. Under cost and other practical constraints, when prioritizing among mitigation measures, different data sources such as observed data from physical systems and simulated data from simulation models may be used. Data obtained from different sources have different characteristics in terms of accuracy and data volume. Therefore, it is important to integrate different data sources when evaluating alternative mitigation measures in a systematic and systemic manner. In response, a Gaussian process metamodel based method is proposed to evaluate energy saving measures when different data sources are combined synergistically. In addition, a cost-effectiveness analysis is used to prioritize the mitigation strategies based on cost considerations. A case study is developed to demonstrate the advantages of the proposed method in terms of accuracy and efficiency. All mitigation measures selected in the case study are found to have a negative cost which can translate to both energy and cost savings. Among the evaluated measures, speed reduction has shown to be the most plausible measure in terms of energy savings and marginal cost-effectiveness.

Suggested Citation

  • Yuan, Jun & Nian, Victor & He, Junliang & Yan, Wei, 2019. "Cost-effectiveness analysis of energy efficiency measures for maritime shipping using a metamodel based approach with different data sources," Energy, Elsevier, vol. 189(C).
    • Handle: RePEc:eee:energy:v:189:y:2019:i:c:s0360544219319000
    DOI: 10.1016/j.energy.2019.116205
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544219319000
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2019.116205?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 search for a different version of it.

    References listed on IDEAS

    as
    1. Magnus S. Eide & Øyvind Endresen & Rolf Skjong & Tore Longva & Sverre Alvik, 2009. "Cost-effectiveness assessment of CO 2 reducing measures in shipping," Maritime Policy & Management, Taylor & Francis Journals, vol. 36(4), pages 367-384, August.
    2. Rodgers, Mark D. & Coit, David W. & Felder, Frank A. & Carlton, Annmarie, 2018. "Generation expansion planning considering health and societal damages – A simulation-based optimization approach," Energy, Elsevier, vol. 164(C), pages 951-963.
    3. Wang, Chunlin & Liu, Yang & Zheng, Song & Jiang, Aipeng, 2018. "Optimizing combustion of coal fired boilers for reducing NOx emission using Gaussian Process," Energy, Elsevier, vol. 153(C), pages 149-158.
    4. Breuer, Péter & Major, Péter, 1983. "Central limit theorems for non-linear functionals of Gaussian fields," Journal of Multivariate Analysis, Elsevier, vol. 13(3), pages 425-441, September.
    5. Baldi, Francesco & Gabrielii, Cecilia, 2015. "A feasibility analysis of waste heat recovery systems for marine applications," Energy, Elsevier, vol. 80(C), pages 654-665.
    6. Ko, Chia-Nan & Lee, Cheng-Ming, 2013. "Short-term load forecasting using SVR (support vector regression)-based radial basis function neural network with dual extended Kalman filter," Energy, Elsevier, vol. 49(C), pages 413-422.
    7. Kalogirou, Soteris A., 2000. "Applications of artificial neural-networks for energy systems," Applied Energy, Elsevier, vol. 67(1-2), pages 17-35, September.
    8. Ahmad, Tanveer & Chen, Huanxin & Huang, Ronggeng & Yabin, Guo & Wang, Jiangyu & Shair, Jan & Azeem Akram, Hafiz Muhammad & Hassnain Mohsan, Syed Agha & Kazim, Muhammad, 2018. "Supervised based machine learning models for short, medium and long-term energy prediction in distinct building environment," Energy, Elsevier, vol. 158(C), pages 17-32.
    9. Yuan, Jun & Ng, Szu Hui, 2017. "Emission reduction measures ranking under uncertainty," Applied Energy, Elsevier, vol. 188(C), pages 270-279.
    10. Marc C. Kennedy & Anthony O'Hagan, 2001. "Bayesian calibration of computer models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 63(3), pages 425-464.
    11. Fouilloy, Alexis & Voyant, Cyril & Notton, Gilles & Motte, Fabrice & Paoli, Christophe & Nivet, Marie-Laure & Guillot, Emmanuel & Duchaud, Jean-Laurent, 2018. "Solar irradiation prediction with machine learning: Forecasting models selection method depending on weather variability," Energy, Elsevier, vol. 165(PA), pages 620-629.
    12. Peter N. Hoffmann & Magnus S. Eide & Øyvind Endresen, 2012. "Effect of proposed CO 2 emission reduction scenarios on capital expenditure," Maritime Policy & Management, Taylor & Francis Journals, vol. 39(4), pages 443-460, July.
    13. Yuan, Jun & Nian, Victor & Su, Bin & Meng, Qun, 2017. "A simultaneous calibration and parameter ranking method for building energy models," Applied Energy, Elsevier, vol. 206(C), pages 657-666.
    14. Nian, Victor & Yuan, Jun, 2017. "A method for analysis of maritime transportation systems in the life cycle approach – The oil tanker example," Applied Energy, Elsevier, vol. 206(C), pages 1579-1589.
    15. White, Halbert, 1982. "Maximum Likelihood Estimation of Misspecified Models," Econometrica, Econometric Society, vol. 50(1), pages 1-25, January.
    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. Jun Yuan & Haowei Wang & Szu Hui Ng & Victor Nian, 2020. "Ship Emission Mitigation Strategies Choice Under Uncertainty," Energies, MDPI, vol. 13(9), pages 1-20, May.
    2. Jun Yuan & Jiang Zhu & Victor Nian, 2020. "Neural Network Modeling Based on the Bayesian Method for Evaluating Shipping Mitigation Measures," Sustainability, MDPI, vol. 12(24), pages 1-14, 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. Jun Yuan & Haowei Wang & Szu Hui Ng & Victor Nian, 2020. "Ship Emission Mitigation Strategies Choice Under Uncertainty," Energies, MDPI, vol. 13(9), pages 1-20, May.
    2. Hao Wu & Michael Browne, 2015. "Random Model Discrepancy: Interpretations and Technicalities (A Rejoinder)," Psychometrika, Springer;The Psychometric Society, vol. 80(3), pages 619-624, September.
    3. Jun Yuan & Jiang Zhu & Victor Nian, 2020. "Neural Network Modeling Based on the Bayesian Method for Evaluating Shipping Mitigation Measures," Sustainability, MDPI, vol. 12(24), pages 1-14, December.
    4. Jean-Laurent Duchaud & Cyril Voyant & Alexis Fouilloy & Gilles Notton & Marie-Laure Nivet, 2020. "Trade-Off between Precision and Resolution of a Solar Power Forecasting Algorithm for Micro-Grid Optimal Control," Energies, MDPI, vol. 13(14), pages 1-16, July.
    5. Gairaa, Kacem & Voyant, Cyril & Notton, Gilles & Benkaciali, Saïd & Guermoui, Mawloud, 2022. "Contribution of ordinal variables to short-term global solar irradiation forecasting for sites with low variabilities," Renewable Energy, Elsevier, vol. 183(C), pages 890-902.
    6. Kornelis Blok & Angélica Afanador & Irina van der Hoorn & Tom Berg & Oreane Y. Edelenbosch & Detlef P. van Vuuren, 2020. "Assessment of Sectoral Greenhouse Gas Emission Reduction Potentials for 2030," Energies, MDPI, vol. 13(4), pages 1-24, February.
    7. Si Chen & Daniel Friedrich & Zhibin Yu & James Yu, 2019. "District Heating Network Demand Prediction Using a Physics-Based Energy Model with a Bayesian Approach for Parameter Calibration," Energies, MDPI, vol. 12(18), pages 1-19, September.
    8. Hao Wu & Michael Browne, 2015. "Quantifying Adventitious Error in a Covariance Structure as a Random Effect," Psychometrika, Springer;The Psychometric Society, vol. 80(3), pages 571-600, September.
    9. Zhang, Qiang & Tian, Zhe & Ma, Zhijun & Li, Genyan & Lu, Yakai & Niu, Jide, 2020. "Development of the heating load prediction model for the residential building of district heating based on model calibration," Energy, Elsevier, vol. 205(C).
    10. Zhu, Chuanqi & Tian, Wei & Yin, Baoquan & Li, Zhanyong & Shi, Jiaxin, 2020. "Uncertainty calibration of building energy models by combining approximate Bayesian computation and machine learning algorithms," Applied Energy, Elsevier, vol. 268(C).
    11. Ahmad, Tanveer & Zhang, Dongdong & Huang, Chao, 2021. "Methodological framework for short-and medium-term energy, solar and wind power forecasting with stochastic-based machine learning approach to monetary and energy policy applications," Energy, Elsevier, vol. 231(C).
    12. Hu, Hongtao & Yuan, Jun & Nian, Victor, 2019. "Development of a multi-objective decision-making method to evaluate correlated decarbonization measures under uncertainty – The example of international shipping," Transport Policy, Elsevier, vol. 82(C), pages 148-157.
    13. Hou, D. & Hassan, I.G. & Wang, L., 2021. "Review on building energy model calibration by Bayesian inference," Renewable and Sustainable Energy Reviews, Elsevier, vol. 143(C).
    14. Chen, Jianli & Gao, Xinghua & Hu, Yuqing & Zeng, Zhaoyun & Liu, Yanan, 2019. "A meta-model-based optimization approach for fast and reliable calibration of building energy models," Energy, Elsevier, vol. 188(C).
    15. Yuan, Jun & Nian, Victor & Su, Bin, 2019. "Evaluation of cost-effective building retrofit strategies through soft-linking a metamodel-based Bayesian method and a life cycle cost assessment method," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    16. Gatt, Damien & Yousif, Charles & Cellura, Maurizio & Camilleri, Liberato & Guarino, Francesco, 2020. "Assessment of building energy modelling studies to meet the requirements of the new Energy Performance of Buildings Directive," Renewable and Sustainable Energy Reviews, Elsevier, vol. 127(C).
    17. Nuchturee, Chalermkiat & Li, Tie & Xia, Hongpu, 2020. "Energy efficiency of integrated electric propulsion for ships – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 134(C).
    18. P.A.V.B. Swamy & I-Lok Chang & Jatinder S. Mehta & William H. Greene & Stephen G. Hall & George S. Tavlas, 2016. "Removing Specification Errors from the Usual Formulation of Binary Choice Models," Econometrics, MDPI, vol. 4(2), pages 1-21, June.
    19. Carlo Altavilla & Raffaella Giacomini & Giuseppe Ragusa, 2017. "Anchoring the yield curve using survey expectations," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(6), pages 1055-1068, September.
    20. Fernando Rios-Avila & Gustavo Canavire-Bacarreza, 2018. "Standard-error correction in two-stage optimization models: A quasi–maximum likelihood estimation approach," Stata Journal, StataCorp LP, vol. 18(1), pages 206-222, March.

    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:energy:v:189:y:2019:i:c:s0360544219319000. 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.journals.elsevier.com/energy .

    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.