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Undesirable factors in stochastic DEA cross-efficiency evaluation: An application to thermal power plant energy efficiency

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  • Khodadadipour, M.
  • Hadi-Vencheh, A.
  • Behzadi, M.H.
  • Rostamy-malkhalifeh, M.

Abstract

In this study using an input-oriented data envelopment analysis (DEA) model with undesirable outputs a new stochastic model called Expected Ranking Criterion is proposed. The proposed model employs statistical techniques to evaluate the efficiency of decision making units (DMUs) with stochastic data. Based on the proposed model, a stochastic DEA (SDEA) cross-efficiency model is suggested for ranking and discrimination of DMUs. Then, given the non-uniqueness of resulting optimal solution, a stochastic model is introduced for rating priorities by which cross-efficiency evaluation is performed using aggressive approach. Finally, the proposed models are implemented for evaluating 32 thermal power plants. The results show the applicability of the proposed models.

Suggested Citation

  • Khodadadipour, M. & Hadi-Vencheh, A. & Behzadi, M.H. & Rostamy-malkhalifeh, M., 2021. "Undesirable factors in stochastic DEA cross-efficiency evaluation: An application to thermal power plant energy efficiency," Economic Analysis and Policy, Elsevier, vol. 69(C), pages 613-628.
    • Handle: RePEc:eee:ecanpo:v:69:y:2021:i:c:p:613-628
    DOI: 10.1016/j.eap.2021.01.013
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    as
    1. Léopold Simar & Valentin Zelenyuk, 2011. "Stochastic FDH/DEA estimators for frontier analysis," Journal of Productivity Analysis, Springer, vol. 36(1), pages 1-20, August.
    2. Yang, Feng & Ang, Sheng & Xia, Qiong & Yang, Chenchen, 2012. "Ranking DMUs by using interval DEA cross efficiency matrix with acceptability analysis," European Journal of Operational Research, Elsevier, vol. 223(2), pages 483-488.
    3. Per Andersen & Niels Christian Petersen, 1993. "A Procedure for Ranking Efficient Units in Data Envelopment Analysis," Management Science, INFORMS, vol. 39(10), pages 1261-1264, October.
    4. Jin, Jingliang & Zhou, Dequn & Zhou, Peng, 2014. "Measuring environmental performance with stochastic environmental DEA: The case of APEC economies," Economic Modelling, Elsevier, vol. 38(C), pages 80-86.
    5. Sueyoshi, Toshiyuki & Goto, Mika, 2010. "Should the US clean air act include CO2 emission control?: Examination by data envelopment analysis," Energy Policy, Elsevier, vol. 38(10), pages 5902-5911, October.
    6. William Cooper & Zhimin Huang & Vedran Lelas & Susan Li & Ole Olesen, 1998. "Chance Constrained Programming Formulations for Stochastic Characterizations of Efficiency and Dominance in DEA," Journal of Productivity Analysis, Springer, vol. 9(1), pages 53-79, January.
    7. Merja Halme & Tarja Joro & Pekka Korhonen & Seppo Salo & Jyrki Wallenius, 1999. "A Value Efficiency Approach to Incorporating Preference Information in Data Envelopment Analysis," Management Science, INFORMS, vol. 45(1), pages 103-115, January.
    8. Sueyoshi, Toshiyuki, 1999. "DEA non-parametric ranking test and index measurement: slack-adjusted DEA and an application to Japanese agriculture cooperatives," Omega, Elsevier, vol. 27(3), pages 315-326, June.
    9. Wanke, Peter & Barros, C.P. & Figueiredo, Otávio, 2016. "Efficiency and productive slacks in urban transportation modes: A two-stage SDEA-Beta Regression approach," Utilities Policy, Elsevier, vol. 41(C), pages 31-39.
    10. A Hadi-Vencheh & A Esmaeilzadeh, 2013. "A new super-efficiency model in the presence of negative data," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 64(3), pages 396-401, March.
    11. Jradi, Samah & Ruggiero, John, 2019. "Stochastic data envelopment analysis: A quantile regression approach to estimate the production frontier," European Journal of Operational Research, Elsevier, vol. 278(2), pages 385-393.
    12. Léopold Simar & Ingrid Keilegom & Valentin Zelenyuk, 2017. "Nonparametric least squares methods for stochastic frontier models," Journal of Productivity Analysis, Springer, vol. 47(3), pages 189-204, June.
    13. Thanassoulis, E. & Dyson, R. G., 1992. "Estimating preferred target input-output levels using data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 56(1), pages 80-97, January.
    14. Huang, Zhimin & Li, Susan X., 1996. "Dominance stochastic models in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 95(2), pages 390-403, December.
    15. Kao, Chiang & Liu, Shiang-Tai, 2019. "Stochastic efficiency measures for production units with correlated data," European Journal of Operational Research, Elsevier, vol. 273(1), pages 278-287.
    16. O. Olesen, 2006. "Comparing and Combining Two Approaches for Chance Constrained DEA," Journal of Productivity Analysis, Springer, vol. 26(2), pages 103-119, October.
    17. Yeh, Tsai-lien & Chen, Tser-yieth & Lai, Pei-ying, 2010. "A comparative study of energy utilization efficiency between Taiwan and China," Energy Policy, Elsevier, vol. 38(5), pages 2386-2394, May.
    18. Cooper, William W. & Deng, H. & Huang, Zhimin & Li, Susan X., 2004. "Chance constrained programming approaches to congestion in stochastic data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 155(2), pages 487-501, June.
    19. Soares de Mello, João Carlos C.B. & Angulo Meza, Lidia & da Silveira, Juliana Quintanilha & Gomes, Eliane Gonçalves, 2013. "About negative efficiencies in Cross Evaluation BCC input oriented models," European Journal of Operational Research, Elsevier, vol. 229(3), pages 732-737.
    20. Bruni, M.E. & Conforti, D. & Beraldi, P. & Tundis, E., 2009. "Probabilistically constrained models for efficiency and dominance in DEA," International Journal of Production Economics, Elsevier, vol. 117(1), pages 219-228, January.
    21. A. Charnes & W. W. Cooper, 1959. "Chance-Constrained Programming," Management Science, INFORMS, vol. 6(1), pages 73-79, October.
    22. Wai Peng Wong, 2009. "Performance evaluation of supply chain in stochastic environment: using a simulation based DEA framework," International Journal of Business Performance and Supply Chain Modelling, Inderscience Enterprises Ltd, vol. 1(2/3), pages 203-228.
    23. Shi, Guang-Ming & Bi, Jun & Wang, Jin-Nan, 2010. "Chinese regional industrial energy efficiency evaluation based on a DEA model of fixing non-energy inputs," Energy Policy, Elsevier, vol. 38(10), pages 6172-6179, October.
    24. Jinpei Liu & Mengdi Fang & Feifei Jin & Chengsong Wu & Huayou Chen, 2020. "Multi-Attribute Decision Making Based on Stochastic DEA Cross-Efficiency with Ordinal Variable and Its Application to Evaluation of Banks’ Sustainable Development," Sustainability, MDPI, vol. 12(6), pages 1-15, March.
    25. O. B. Olesen & N. C. Petersen, 1995. "Chance Constrained Efficiency Evaluation," Management Science, INFORMS, vol. 41(3), pages 442-457, March.
    26. Olesen, Ole B. & Petersen, Niels Christian, 2016. "Stochastic Data Envelopment Analysis—A review," European Journal of Operational Research, Elsevier, vol. 251(1), pages 2-21.
    27. Mandal, Sabuj Kumar, 2010. "Do undesirable output and environmental regulation matter in energy efficiency analysis? Evidence from Indian Cement Industry," Energy Policy, Elsevier, vol. 38(10), pages 6076-6083, October.
    28. W W Cooper & H Deng & Z Huang & S X Li, 2002. "Chance constrained programming approaches to technical efficiencies and inefficiencies in stochastic data envelopment analysis," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 53(12), pages 1347-1356, December.
    29. Despotis, Dimitris K. & Smirlis, Yiannis G., 2002. "Data envelopment analysis with imprecise data," European Journal of Operational Research, Elsevier, vol. 140(1), pages 24-36, July.
    30. Seiford, Lawrence M. & Zhu, Joe, 2002. "Modeling undesirable factors in efficiency evaluation," European Journal of Operational Research, Elsevier, vol. 142(1), pages 16-20, October.
    31. Jianwei Ren & Bin Gao & Jiewei Zhang & Chunhua Chen, 2020. "Measuring the Energy and Carbon Emission Efficiency of Regional Transportation Systems in China: Chance-Constrained DEA Models," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-12, April.
    32. Chen, Zhongfei & Wanke, Peter & Antunes, Jorge Junio Moreira & Zhang, Ning, 2017. "Chinese airline efficiency under CO2 emissions and flight delays: A stochastic network DEA model," Energy Economics, Elsevier, vol. 68(C), pages 89-108.
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