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On selecting directions for directional distance functions in a non-parametric framework: A review

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  • Ke Wang
  • Yujiao Xian
  • Chia-Yen Lee
  • Yi-Ming Wei

    () (Center for Energy and Environmental Policy Research (CEEP), Beijing Institute of Technology)

  • Zhimin Huang

Abstract

Directional distance function (DDF) has been a commonly used technique for estimating efficiency and productivity over the past two decades, and the directional vector is usually predetermined in the applications of DDF. The most critical issue of using DDF remains that how to appropriately project the inefficient decision-making unit (DMU) onto the production frontier along with a justified direction. This paper provides a comprehensive literature review on the techniques for selecting directional vector of the directional distance function. It begins with a brief introduction of the existing methods around the inclusion of the exogenous direction techniques and the endogenous direction techniques. The former commonly includes arbitrary direction and conditional direction techniques, while the latter involves the techniques for seeking theoretically optimized directions (i.e., direction towards the closest benchmark or indicating the largest efficiency improvement potential) and market-oriented directions (i.e., directions towards cost minimization, profit maximization, or marginal profit maximization benchmarks). The main advantages and disadvantages of these techniques are summarized, and the limitations inherent in the exogenous direction-selecting techniques are discussed. It also analytically argues the mechanism of each endogenous direction technique. The literature review is end up with a numerical example of efficiency estimation for power plants, in which most of the reviewed directions for DDF are demonstrated and their evaluation performance are compared.

Suggested Citation

  • Ke Wang & Yujiao Xian & Chia-Yen Lee & Yi-Ming Wei & Zhimin Huang, 2017. "On selecting directions for directional distance functions in a non-parametric framework: A review," CEEP-BIT Working Papers 99, Center for Energy and Environmental Policy Research (CEEP), Beijing Institute of Technology.
  • Handle: RePEc:biw:wpaper:99
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    File URL: http://ceep.bit.edu.cn/docs/2018-10/20181012074849820360.pdf
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    Cited by:

    1. repec:eee:streco:v:47:y:2018:i:c:p:180-193 is not listed on IDEAS
    2. Yujiao Xian & Ke Wang & Xunpeng Shi & Chi Zhang & Yi-Ming Wei & Zhimin Huang, 2018. "Carbon emissions intensity reduction target for China¡¯s power industry: An efficiency and productivity perspective," CEEP-BIT Working Papers 117, Center for Energy and Environmental Policy Research (CEEP), Beijing Institute of Technology.
    3. repec:spr:annopr:v:264:y:2018:i:1:d:10.1007_s10479-017-2719-5 is not listed on IDEAS
    4. repec:kap:jproda:v:50:y:2018:i:1:d:10.1007_s11123-018-0534-x is not listed on IDEAS

    More about this item

    Keywords

    Data Envelopment Analysis (DEA); Least distance; Endogenous mechanism; Cost efficiency; Profit efficiency; Marginal profit maximization;

    JEL classification:

    • Q54 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Climate; Natural Disasters and their Management; Global Warming
    • Q40 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - General

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