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A new slacks-based measure of Malmquist–Luenberger index in the presence of undesirable outputs

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  • Arabi, Behrouz
  • Munisamy, Susila
  • Emrouznejad, Ali

Abstract

In the majority of production processes, noticeable amounts of bad byproducts or bad outputs are produced. The negative effects of the bad outputs on efficiency cannot be handled by the standard Malmquist index to measure productivity change over time. Toward this end, the Malmquist–Luenberger index (MLI) has been introduced, when undesirable outputs are present. In this paper, we introduce a Data Envelopment Analysis (DEA) model as well as an algorithm, which can successfully eliminate a common infeasibility problem encountered in MLI mixed period problems. This model incorporates the best endogenous direction amongst all other possible directions to increase desirable output and decrease the undesirable outputs at the same time. A simple example used to illustrate the new algorithm and a real application of steam power plants is used to show the applicability of the proposed model.

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  • Arabi, Behrouz & Munisamy, Susila & Emrouznejad, Ali, 2015. "A new slacks-based measure of Malmquist–Luenberger index in the presence of undesirable outputs," Omega, Elsevier, vol. 51(C), pages 29-37.
  • Handle: RePEc:eee:jomega:v:51:y:2015:i:c:p:29-37
    DOI: 10.1016/j.omega.2014.08.006
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    Cited by:

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    2. 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.
    3. Miao, Zhuang & Chen, Xiaodong & Baležentis, Tomas & Sun, Chuanwang, 2019. "Atmospheric environmental productivity across the provinces of China: Joint decomposition of range adjusted measure and Luenberger productivity indicator," Energy Policy, Elsevier, vol. 132(C), pages 665-677.
    4. Song, Malin & Wang, Jianlin, 2018. "Environmental efficiency evaluation of thermal power generation in China based on a slack-based endogenous directional distance function model," Energy, Elsevier, vol. 161(C), pages 325-336.
    5. Kao, Chiang, 2017. "Measurement and decomposition of the Malmquist productivity index for parallel production systems," Omega, Elsevier, vol. 67(C), pages 54-59.
    6. Chen, Weidong & Geng, Wenxin, 2017. "Fossil energy saving and CO2 emissions reduction performance, and dynamic change in performance considering renewable energy input," Energy, Elsevier, vol. 120(C), pages 283-292.
    7. Long, Xingle & Wu, Chao & Zhang, Jijian & Zhang, Jing, 2018. "Environmental efficiency for 192 thermal power plants in the Yangtze River Delta considering heterogeneity: A metafrontier directional slacks-based measure approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 3962-3971.
    8. Hampf, Benjamin, 2018. "Cost and environmental efficiency of U.S. electricity generation: Accounting for heterogeneous inputs and transportation costs," Energy, Elsevier, vol. 163(C), pages 932-941.
    9. Chen, Lei & Wang, Ying-Ming & Lai, Fujun, 2017. "Semi-disposability of undesirable outputs in data envelopment analysis for environmental assessments," European Journal of Operational Research, Elsevier, vol. 260(2), pages 655-664.
    10. Emrouznejad, Ali & Yang, Guo-liang, 2016. "A framework for measuring global Malmquist–Luenberger productivity index with CO2 emissions on Chinese manufacturing industries," Energy, Elsevier, vol. 115(P1), pages 840-856.
    11. Feng, Chenpeng & Chu, Feng & Ding, Jingjing & Bi, Gongbing & Liang, Liang, 2015. "Carbon Emissions Abatement (CEA) allocation and compensation schemes based on DEA," Omega, Elsevier, vol. 53(C), pages 78-89.
    12. Kiani Mavi, Reza & Saen, Reza Farzipoor & Goh, Mark, 2019. "Joint analysis of eco-efficiency and eco-innovation with common weights in two-stage network DEA: A big data approach," Technological Forecasting and Social Change, Elsevier, vol. 144(C), pages 553-562.
    13. Liu, Guangtian & Wang, Bing & Zhang, Ning, 2016. "A coin has two sides: Which one is driving China’s green TFP growth?," Economic Systems, Elsevier, vol. 40(3), pages 481-498.
    14. Arabi, Behrouz & Munisamy, Susila & Emrouznejad, Ali & Toloo, Mehdi & Ghazizadeh, Mohammad Sadegh, 2016. "Eco-efficiency considering the issue of heterogeneity among power plants," Energy, Elsevier, vol. 111(C), pages 722-735.
    15. Emrouznejad, Ali & Yang, Guo-liang, 2016. "CO2 emissions reduction of Chinese light manufacturing industries: A novel RAM-based global Malmquist–Luenberger productivity index," Energy Policy, Elsevier, vol. 96(C), pages 397-410.
    16. Yang, Guo-liang & Fukuyama, Hirofumi & Song, Yao-yao, 2019. "Estimating capacity utilization of Chinese manufacturing industries," Socio-Economic Planning Sciences, Elsevier, vol. 67(C), pages 94-110.
    17. Manello, Alessandro, 2017. "Productivity growth, environmental regulation and win–win opportunities: The case of chemical industry in Italy and Germany," European Journal of Operational Research, Elsevier, vol. 262(2), pages 733-743.
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