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Measuring environmental performance with stochastic environmental DEA: The case of APEC economies

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  • Jin, Jingliang
  • Zhou, Dequn
  • Zhou, Peng

Abstract

Data envelopment analysis (DEA) has been widely used for environmental performance measurement at different levels. Most of environmental DEA models take the deterministic form without considering random factors. This paper presents a stochastic environmental DEA model that can measure environmental performance under random conditions. The proposed model has been applied to evaluate the environmental performance of Asia-Pacific Economic Cooperation (APEC) economies in 2010. The results indicate that the stochastic pure environmental performance of APEC economies is indeed affected by random factors. Especially, the fluctuation of Republic of Korea's stochastic pure environmental performance is most obvious among all the APEC economies.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:ecmode:v:38:y:2014:i:c:p:80-86 DOI: 10.1016/j.econmod.2013.12.017
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    Cited by:

    1. 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.
    2. Arjomandi, Amir & Seufert, Juergen Heinz, 2014. "An evaluation of the world's major airlines' technical and environmental performance," Economic Modelling, Elsevier, vol. 41(C), pages 133-144.
    3. repec:eee:enepol:v:110:y:2017:i:c:p:79-89 is not listed on IDEAS
    4. Abdullah Korkut Üstün, 2016. "Evaluating İstanbul’s disaster resilience capacity by data envelopment analysis," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 80(3), pages 1603-1623, February.
    5. Yin, Pengzhen & Sun, Jiasen & Chu, Junfei & Liang, Liang, 2016. "Evaluating the environmental efficiency of a two-stage system with undesired outputs by a DEA approach: An interest preference perspectiveAuthor-Name: Wu, Jie," European Journal of Operational Research, Elsevier, vol. 254(3), pages 1047-1062.
    6. Stergios Athanassoglou, 2016. "Revisiting Worst-Case DEA for Composite Indicators," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 128(3), pages 1259-1272, September.
    7. Li, Tianxiang & Baležentis, Tomas & Makutėnienė, Daiva & Streimikiene, Dalia & Kriščiukaitienė, Irena, 2016. "Energy-related CO2 emission in European Union agriculture: Driving forces and possibilities for reduction," Applied Energy, Elsevier, pages 682-694.
    8. repec:eee:energy:v:138:y:2017:i:c:p:552-562 is not listed on IDEAS
    9. Abdullah Üstün & Gülay Barbarosoğlu, 2015. "Performance evaluation of Turkish disaster relief management system in 1999 earthquakes using data envelopment analysis," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 75(2), pages 1977-1996, January.
    10. Jie Wu & Jun-Fei Chu & Liang Liang, 2016. "Target setting and allocation of carbon emissions abatement based on DEA and closest target: an application to 20 APEC economies," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 84(1), pages 279-296, November.

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