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Units invariant DEA when weight restrictions are present: ecological performance of US electricity industry

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  • Wade D. Cook

    (York University)

  • Juan Du

    (Tongji University)

  • Joe Zhu

    (Nanjing Audit University
    Worcester Polytechnic Institute)

Abstract

Electricity generation currently is the main industrial source of air emissions in the United States. Both researchers and practitioners are interested in conducting studies to evaluate the ecological performance of this industry, in order to propose solutions to curb emissions of air pollutants and to improve the efficiency of converting fossil resources into electric energy. In this paper, data envelopment analysis (DEA) is used to assess ecological efficiency where air emissions are used as undesirable outputs. Although conventional DEA does not require a priori information on the input and output weights, weight restrictions can be incorporated to reflect a user’s preference over the performance metrics, or to refine the DEA results. Adding weight restrictions voids the fact that DEA scores are independent of the units of measurement. To incorporate weight constraints in ecological efficiency assessment, this paper develops a DEA model that is units-invariant when weight restrictions are imposed. Moreover, the proposed model is equivalent to the standard units-invariant DEA model when weight restrictions are not present.

Suggested Citation

  • Wade D. Cook & Juan Du & Joe Zhu, 2017. "Units invariant DEA when weight restrictions are present: ecological performance of US electricity industry," Annals of Operations Research, Springer, vol. 255(1), pages 323-346, August.
    • Handle: RePEc:spr:annopr:v:255:y:2017:i:1:d:10.1007_s10479-015-1881-x
    DOI: 10.1007/s10479-015-1881-x
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    References listed on IDEAS

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    Cited by:

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    2. Chuanming Yang & Jie Shen & Zhonghua Jiang & Junyu Chen & Yi Xie, 2024. "Evaluation of Atmospheric Environmental Efficiency and Spatiotemporal Differences in the Yangtze River Delta Region of China," Sustainability, MDPI, vol. 16(6), pages 1-16, March.
    3. Xiang Ji & Jie Wu & Qingyuan Zhu & Jiasen Sun, 2019. "Using a hybrid heterogeneous DEA method to benchmark China’s sustainable urbanization: an empirical study," Annals of Operations Research, Springer, vol. 278(1), pages 281-335, July.
    4. Kaffash, Sepideh & Azizi, Roza & Huang, Ying & Zhu, Joe, 2020. "A survey of data envelopment analysis applications in the insurance industry 1993–2018," European Journal of Operational Research, Elsevier, vol. 284(3), pages 801-813.
    5. Barnabé Walheer, 2020. "Output, input, and undesirable output interconnections in data envelopment analysis: convexity and returns-to-scale," Annals of Operations Research, Springer, vol. 284(1), pages 447-467, January.

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