IDEAS home Printed from https://ideas.repec.org/a/wly/mgtdec/v45y2024i1p539-553.html
   My bibliography  Save this article

A prospect theory extension of data envelopment analysis model for wave‐wind energy site selection in New Zealand

Author

Listed:
  • Chia‐Nan Wang
  • Hoang‐Kha Nguyen
  • Nhat‐Luong Nhieu
  • Hsien‐Pin Hsu

Abstract

Renewable energy sources have been utilized by countries worldwide, but offshore energy exploitation projects remain highly promising for both the world and New Zealand specifically. The selection of equipment installation locations can be challenging and requires significant investment, prompting the need for optimization in offshore energy exploitation. Wind and wave location (WWL) integrated sites offer a highly potential solution for reducing installation costs and enhancing efficiency in renewable energy exploitation. However, the decision‐making process for selecting equipment locations is crucial and directly affects the outcome. This study aims to evaluate and consider sustainable and effective equipment installation sites based on both quantitative indicators and behavioral psychology of decision making. To achieve this goal, the study utilizes the prospect theory Data Envelopment Analysis (PT‐DEA) model, which builds on PT to analyze and evaluate the effectiveness of offshore installation site selection with consideration for human psychology. By combining the results of quantitative indicators and decision‐making behavior, the study identifies sustainable and effective offshore installation sites, recommending appropriate equipment installation locations WWL. The study is also compared and referenced with relevant plans, projects, and development policies in New Zealand for confirmation.

Suggested Citation

  • Chia‐Nan Wang & Hoang‐Kha Nguyen & Nhat‐Luong Nhieu & Hsien‐Pin Hsu, 2024. "A prospect theory extension of data envelopment analysis model for wave‐wind energy site selection in New Zealand," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 45(1), pages 539-553, January.
    • Handle: RePEc:wly:mgtdec:v:45:y:2024:i:1:p:539-553
    DOI: 10.1002/mde.4016
    as

    Download full text from publisher

    File URL: https://doi.org/10.1002/mde.4016
    Download Restriction: no
    File URL: https://libkey.io/10.1002/mde.4016?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
    ---><---

    References listed on IDEAS

    as
    1. Cradden, L. & Kalogeri, C. & Barrios, I. Martinez & Galanis, G. & Ingram, D. & Kallos, G., 2016. "Multi-criteria site selection for offshore renewable energy platforms," Renewable Energy, Elsevier, vol. 87(P1), pages 791-806.
    2. Li, Jiangxia & Pan, Shunqi & Chen, Yongping & Yao, Yu & Xu, Conghao, 2022. "Assessment of combined wind and wave energy in the tropical cyclone affected region:An application in China seas," Energy, Elsevier, vol. 260(C).
    3. Pourali, Mahmoud & Kavianpour, Mohamad Reza & Kamranzad, Bahareh & Alizadeh, Mohamad Javad, 2023. "Future variability of wave energy in the Gulf of Oman using a high resolution CMIP6 climate model," Energy, Elsevier, vol. 262(PB).
    4. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
    5. Edwards, Kimberley D., 1996. "Prospect theory: A literature review," International Review of Financial Analysis, Elsevier, vol. 5(1), pages 19-38.
    6. Kiunke, Theresa & Gemignani, Natalia & Malheiro, Pedro & Brudermann, Thomas, 2022. "Key factors influencing onshore wind energy development: A case study from the German North Sea region," Energy Policy, Elsevier, vol. 165(C).
    7. Azadeh, A. & Ghaderi, S.F. & Nasrollahi, M.R., 2011. "Location optimization of wind plants in Iran by an integrated hierarchical Data Envelopment Analysis," Renewable Energy, Elsevier, vol. 36(5), pages 1621-1631.
    8. Esteban, M. Dolores & Diez, J. Javier & López, Jose S. & Negro, Vicente, 2011. "Why offshore wind energy?," Renewable Energy, Elsevier, vol. 36(2), pages 444-450.
    9. Doljak, Dejan & Stanojević, Gorica, 2017. "Evaluation of natural conditions for site selection of ground-mounted photovoltaic power plants in Serbia," Energy, Elsevier, vol. 127(C), pages 291-300.
    10. R. D. Banker & A. Charnes & W. W. Cooper, 1984. "Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis," Management Science, INFORMS, vol. 30(9), pages 1078-1092, September.
    11. Nicholas C. Barberis, 2013. "Thirty Years of Prospect Theory in Economics: A Review and Assessment," Journal of Economic Perspectives, American Economic Association, vol. 27(1), pages 173-196, Winter.
    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. Shao, Meng & Han, Zhixin & Sun, Jinwei & Xiao, Chengsi & Zhang, Shulei & Zhao, Yuanxu, 2020. "A review of multi-criteria decision making applications for renewable energy site selection," Renewable Energy, Elsevier, vol. 157(C), pages 377-403.
    2. Khanjarpanah, Hossein & Jabbarzadeh, Armin, 2019. "Sustainable wind plant location optimization using fuzzy cross-efficiency data envelopment analysis," Energy, Elsevier, vol. 170(C), pages 1004-1018.
    3. A. Azadeh & M. S. Naghavi lhoseiny & V. Salehi, 2018. "Optimum alternatives of tandem G/G/K queues with disaster customers and retrial phenomenon: interactive voice response systems," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 68(3), pages 535-562, July.
    4. Franz R. Hahn, 2007. "Determinants of Bank Efficiency in Europe. Assessing Bank Performance Across Markets," WIFO Studies, WIFO, number 31499, March.
    5. Alperovych, Yan & Hübner, Georges & Lobet, Fabrice, 2015. "How does governmental versus private venture capital backing affect a firm's efficiency? Evidence from Belgium," Journal of Business Venturing, Elsevier, vol. 30(4), pages 508-525.
    6. Wang, Zhao-Hua & Zeng, Hua-Lin & Wei, Yi-Ming & Zhang, Yi-Xiang, 2012. "Regional total factor energy efficiency: An empirical analysis of industrial sector in China," Applied Energy, Elsevier, vol. 97(C), pages 115-123.
    7. Azarnoosh Kafi & Behrouz Daneshian & Mohsen Rostamy-Malkhalifeh, 2021. "Forecasting the confidence interval of efficiency in fuzzy DEA," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 31(1), pages 41-59.
    8. Ruiqing Yuan & Xiangyang Xu & Yanli Wang & Jiayi Lu & Ying Long, 2024. "Evaluating Carbon-Emission Efficiency in China’s Construction Industry: An SBM-Model Analysis of Interprovincial Building Heating," Sustainability, MDPI, vol. 16(6), pages 1-16, March.
    9. Costa, Marcelo Azevedo & Lopes, Ana Lúcia Miranda & de Pinho Matos, Giordano Bruno Braz, 2015. "Statistical evaluation of Data Envelopment Analysis versus COLS Cobb–Douglas benchmarking models for the 2011 Brazilian tariff revision," Socio-Economic Planning Sciences, Elsevier, vol. 49(C), pages 47-60.
    10. Ahmad, Usman, 2011. "Financial Reforms and Banking Efficiency: Case of Pakistan," MPRA Paper 34220, University Library of Munich, Germany.
    11. Bharti Seth & Punita Saxena & Shalini Arora, 2024. "Operational performance of Indian passenger airlines using hierarchical categorical DEA approach," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 15(4), pages 1415-1423, April.
    12. Ashrafi, Ali & Seow, Hsin-Vonn & Lee, Lai Soon & Lee, Chew Ging, 2013. "The efficiency of the hotel industry in Singapore," Tourism Management, Elsevier, vol. 37(C), pages 31-34.
    13. Büschken, Joachim, 2009. "When does data envelopment analysis outperform a naïve efficiency measurement model?," European Journal of Operational Research, Elsevier, vol. 192(2), pages 647-657, January.
    14. António Afonso & Ana Patricia Montes & José M. Domínguez, 2024. "Measuring Tax Burden Efficiency in OECD Countries: An International Comparison," CESifo Working Paper Series 11333, CESifo.
    15. Andreas Dellnitz & Andreas Kleine & Madjid Tavana, 2024. "An integrated data envelopment analysis and regression tree method for new product price estimation," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 46(4), pages 1189-1211, December.
    16. Jahangoshai Rezaee, Mustafa & Jozmaleki, Mehrdad & Valipour, Mahsa, 2018. "Integrating dynamic fuzzy C-means, data envelopment analysis and artificial neural network to online prediction performance of companies in stock exchange," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 489(C), pages 78-93.
    17. Suhyeon Han & Shinyoung Park & Sejin An & Wonjun Choi & Mina Lee, 2023. "Research on Analyzing the Efficiency of R&D Projects for Climate Change Response Using DEA–Malmquist," Sustainability, MDPI, vol. 15(10), pages 1-23, May.
    18. Chenini Hajer & Jarboui Anis, 2018. "Analysis of the Impact of Governance on Bank Performance: Case of Commercial Tunisian Banks," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 9(3), pages 871-895, September.
    19. Chai, Naijie & Zhou, Wenliang & Hu, Xinlei, 2022. "Safety evaluation of urban rail transit operation considering uncertainty and risk preference: A case study in China," Transport Policy, Elsevier, vol. 125(C), pages 267-288.
    20. Zanella, Andreia & Camanho, Ana S. & Dias, Teresa G., 2015. "Undesirable outputs and weighting schemes in composite indicators based on data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 245(2), pages 517-530.

    More about this item

    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:wly:mgtdec:v:45:y:2024:i:1:p:539-553. 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: Wiley Content Delivery (email available below). General contact details of provider: http://www3.interscience.wiley.com/cgi-bin/jhome/7976 .

    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.