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

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  • 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
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    References listed on IDEAS

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    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. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. Sarıca, Kemal & Or, Ilhan, 2007. "Efficiency assessment of Turkish power plants using data envelopment analysis," Energy, Elsevier, vol. 32(8), pages 1484-1499.
    11. Ramanathan, R, 2001. "Comparative Risk Assessment of energy supply technologies: a Data Envelopment Analysis approach," Energy, Elsevier, vol. 26(2), pages 197-203.
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    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

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