IDEAS home Printed from https://ideas.repec.org/a/ebl/ecbull/eb-18-00851.html
   My bibliography  Save this article

The effects of electricity prices on productive efficiency of states' wind power performances in the United States

Author

Listed:
  • Ãœmit SaÄŸlam

    (East Tennessee State University)

Abstract

Wind power is the largest renewable energy source, which produces a negligible amount of greenhouse gas (GHG) emissions, has gained enormous attention in the electricity generation sector over the past decade in the United States. In this study, a Data Envelopment Analysis (DEA) is implemented to quantitatively evaluate the relative efficiencies of the 39 states' wind power production for the electricity generation. Eight output-oriented CCR (Charnes, Cooper, and Rhodes) models are developed with different combinations of pre-determined four input and five output variables to investigate the effect of electricity prices on the productive efficiency and to test the robustness of the DEA models. The DEA results indicate that two-thirds of the states operate wind power efficiently. Although the high retail price of electricity has a significant contribution to the productive efficiency of the six states, it does not affect the relative efficiency scores of the nineteen states. The location and the size of operation are not advantage/disadvantage to operating wind power at the most productive scale.

Suggested Citation

  • Ãœmit SaÄŸlam, 2019. "The effects of electricity prices on productive efficiency of states' wind power performances in the United States," Economics Bulletin, AccessEcon, vol. 39(2), pages 866-875.
    • Handle: RePEc:ebl:ecbull:eb-18-00851
    as

    Download full text from publisher

    File URL: http://www.accessecon.com/Pubs/EB/2019/Volume39/EB-19-V39-I2-P84.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Xiongfeng Pan & Jing Zhang & Changyu Li & Xianyou Pan & Jinbo Song, 2019. "Analysis of China’s regional wind power generation efficiency and its influencing factors," Energy & Environment, , vol. 30(2), pages 254-271, March.
    3. Ederer, Nikolaus, 2015. "Evaluating capital and operating cost efficiency of offshore wind farms: A DEA approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 42(C), pages 1034-1046.
    4. Xin-gang, Zhao & Zhen, Wei, 2019. "The technical efficiency of China's wind power list enterprises: An estimation based on DEA method and micro-data," Renewable Energy, Elsevier, vol. 133(C), pages 470-479.
    5. Iribarren, Diego & Martín-Gamboa, Mario & Dufour, Javier, 2013. "Environmental benchmarking of wind farms according to their operational performance," Energy, Elsevier, vol. 61(C), pages 589-597.
    6. Iglesias, Guillermo & Castellanos, Pablo & Seijas, Amparo, 2010. "Measurement of productive efficiency with frontier methods: A case study for wind farms," Energy Economics, Elsevier, vol. 32(5), pages 1199-1208, September.
    7. Sueyoshi, Toshiyuki & Yuan, Yan & Goto, Mika, 2017. "A literature study for DEA applied to energy and environment," Energy Economics, Elsevier, vol. 62(C), pages 104-124.
    8. Sarıca, Kemal & Or, Ilhan, 2007. "Efficiency assessment of Turkish power plants using data envelopment analysis," Energy, Elsevier, vol. 32(8), pages 1484-1499.
    9. Ramanathan, R, 2001. "Comparative Risk Assessment of energy supply technologies: a Data Envelopment Analysis approach," Energy, Elsevier, vol. 26(2), pages 197-203.
    10. Iribarren, Diego & Vázquez-Rowe, Ian & Rugani, Benedetto & Benetto, Enrico, 2014. "On the feasibility of using emergy analysis as a source of benchmarking criteria through data envelopment analysis: A case study for wind energy," Energy, Elsevier, vol. 67(C), pages 527-537.
    11. Cayir Ervural, Beyzanur & Zaim, Selim & Delen, Dursun, 2018. "A two-stage analytical approach to assess sustainable energy efficiency," Energy, Elsevier, vol. 164(C), pages 822-836.
    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. Sueyoshi, Toshiyuki & Yuan, Yan & Goto, Mika, 2017. "A literature study for DEA applied to energy and environment," Energy Economics, Elsevier, vol. 62(C), pages 104-124.
    2. Ioannis E. Tsolas, 2020. "Benchmarking Wind Farm Projects by Means of Series Two-Stage DEA," Clean Technol., MDPI, vol. 2(3), pages 1-12, September.
    3. Santos, Marllen & González, Mario, 2019. "Factors that influence the performance of wind farms," Renewable Energy, Elsevier, vol. 135(C), pages 643-651.
    4. Ederer, Nikolaus, 2015. "Evaluating capital and operating cost efficiency of offshore wind farms: A DEA approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 42(C), pages 1034-1046.
    5. Ibrahim Yilmaz, 2023. "A Hybrid DEA–Fuzzy COPRAS Approach to the Evaluation of Renewable Energy: A Case of Wind Farms in Turkey," Sustainability, MDPI, vol. 15(14), pages 1-18, July.
    6. Papież, Monika & Śmiech, Sławomir & Frodyma, Katarzyna, 2019. "Factors affecting the efficiency of wind power in the European Union countries," Energy Policy, Elsevier, vol. 132(C), pages 965-977.
    7. Zurano-Cervelló, Patricia & Pozo, Carlos & Mateo-Sanz, Josep María & Jiménez, Laureano & Guillén-Gosálbez, Gonzalo, 2019. "Sustainability efficiency assessment of the electricity mix of the 28 EU member countries combining data envelopment analysis and optimized projections," Energy Policy, Elsevier, vol. 134(C).
    8. Sai, Rockson & Lin, Boqiang, 2022. "Productivity assessment of power generation in Kenya: What are the impacts?," Energy, Elsevier, vol. 254(PA).
    9. Rodríguez, Xosé A. & Regueiro, Rosa M. & Doldán, Xoán R., 2020. "Analysis of productivity in the Spanish wind industry," Renewable and Sustainable Energy Reviews, Elsevier, vol. 118(C).
    10. Mardani, Abbas & Zavadskas, Edmundas Kazimieras & Streimikiene, Dalia & Jusoh, Ahmad & Khoshnoudi, Masoumeh, 2017. "A comprehensive review of data envelopment analysis (DEA) approach in energy efficiency," Renewable and Sustainable Energy Reviews, Elsevier, vol. 70(C), pages 1298-1322.
    11. Hongli Liu & Xiaoyu Yan & Jinhua Cheng & Jun Zhang & Yan Bu, 2021. "Driving Factors for the Spatiotemporal Heterogeneity in Technical Efficiency of China’s New Energy Industry," Energies, MDPI, vol. 14(14), pages 1-21, July.
    12. Xiongfeng Pan & Jing Zhang & Changyu Li & Xianyou Pan & Jinbo Song, 2019. "Analysis of China’s regional wind power generation efficiency and its influencing factors," Energy & Environment, , vol. 30(2), pages 254-271, March.
    13. Li, Jiaxin & Wang, Zihan & Cheng, Xin & Shuai, Jing & Shuai, Chuanmin & Liu, Jing, 2020. "Has solar PV achieved the national poverty alleviation goals? Empirical evidence from the performances of 52 villages in rural China," Energy, Elsevier, vol. 201(C).
    14. Vaninsky, Alexander, 2010. "Prospective national and regional environmental performance: Boundary estimations using a combined data envelopment – stochastic frontier analysis approach," Energy, Elsevier, vol. 35(9), pages 3657-3665.
    15. Ding, Li-Li & Lei, Liang & Zhao, Xin & Calin, Adrian Cantemir, 2020. "Modelling energy and carbon emission performance: A constrained performance index measure," Energy, Elsevier, vol. 197(C).
    16. Li, Feng & Zhang, Danlu & Zhang, Jinyu & Kou, Gang, 2022. "Measuring the energy production and utilization efficiency of Chinese thermal power industry with the fixed-sum carbon emission constraint," International Journal of Production Economics, Elsevier, vol. 252(C).
    17. Wang, Derek D. & Sueyoshi, Toshiyuki, 2017. "Assessment of large commercial rooftop photovoltaic system installations: Evidence from California," Applied Energy, Elsevier, vol. 188(C), pages 45-55.
    18. Pieralli, Simone & Ritter, Matthias & Odening, Martin, 2015. "Efficiency of wind power production and its determinants," Energy, Elsevier, vol. 90(P1), pages 429-438.
    19. Jindal, Abhinav & Nilakantan, Rahul, 2021. "Falling efficiency levels of Indian coal-fired power plants: A slacks-based analysis," Energy Economics, Elsevier, vol. 93(C).
    20. Andreas Eder & Bernhard Mahlberg & Bernhard Stürmer, 2021. "Measuring and explaining productivity growth of renewable energy producers: An empirical study of Austrian biogas plants," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 48(1), pages 37-63, February.

    More about this item

    Keywords

    Data Envelopment Analysis (DEA); Electricity Prices; Multi-Criteria Decision Making; Productive Efficiency; Wind Power.;
    All these keywords.

    JEL classification:

    • C6 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling
    • Q4 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy

    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:ebl:ecbull:eb-18-00851. 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: John P. Conley (email available below). General contact details of provider: .

    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.