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Performance assessment of mining operations using nonparametric production analysis: A bootstrapping approach in DEA

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  • Tsolas, Ioannis E.

Abstract

This paper presents a Data Envelopment Analysis (DEA) model combined with bootstrapping to assess performance in mining operations. Since DEA-type indicators based on nonparametric production analysis are simply point estimates without any standard error, we provide a methodology to assess the performance of strip mining operations by means of a DEA bootstrapping approach. This methodology is applied to a sample of fifteen Illinois strip coal mines using publicly available data (Thompson et al., 1995). The applied approach uses a mixed mine environmental performance indicator (MMEPI) that is derived by means of a VRS DEA environmental technology treating overburden as an undesirable output under the weak disposability assumption, and we compare this measure with a traditional output-oriented mine performance indicator (MPI) omitting overburden. Although omitting undesirable output results in biased performance estimates, these findings are based on sample specific results and indicate this bias is not statistically significant. The confidence intervals derived by the bootstrapping of the proposed MMEPI point estimates indicate that significant inefficiency has taken place in the analyzed sample of Illinois strip mines.

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  • Tsolas, Ioannis E., 2011. "Performance assessment of mining operations using nonparametric production analysis: A bootstrapping approach in DEA," Resources Policy, Elsevier, vol. 36(2), pages 159-167, June.
  • Handle: RePEc:eee:jrpoli:v:36:y:2011:i:2:p:159-167
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    Cited by:

    1. Hosseinzadeh, Ahmad & Smyth, Russell & Valadkhani, Abbas & Le, Viet, 2016. "Analyzing the efficiency performance of major Australian mining companies using bootstrap data envelopment analysis," Economic Modelling, Elsevier, vol. 57(C), pages 26-35.
    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. Halkos, George & Tzeremes, Nickolaos, 2011. "A conditional full frontier modelling for analyzing environmental efficiency and economic growth," MPRA Paper 32839, University Library of Munich, Germany.
    4. Honma, Satoshi & Hu, Jin-Li, 2014. "Industry-level total-factor energy efficiency in developed countries: A Japan-centered analysis," Applied Energy, Elsevier, vol. 119(C), pages 67-78.
    5. Pedro Herrera & Oscar Millones, 2011. "¿Cuál Es El Costo De La Contaminación Ambiental Minera Sobre Los Recursos Hídricos En El Perú?," Documentos de Trabajo / Working Papers 2011-321, Departamento de Economía - Pontificia Universidad Católica del Perú.
    6. Kang, Chao-Chung & Wu, Chin-Chia, 2013. "Statistical precision of productivity change: A bootstrap application to Taiwan’s telecommunications industry," Telecommunications Policy, Elsevier, vol. 37(11), pages 1015-1032.
    7. George Halkos & Nickolaos Tzeremes, 2014. "Measuring the effect of Kyoto protocol agreement on countries’ environmental efficiency in CO 2 emissions: an application of conditional full frontiers," Journal of Productivity Analysis, Springer, vol. 41(3), pages 367-382, June.
    8. Alfredo Dammert & Arturo Vásquez & Raúl García & Victor Zurita & Humberto Ortiz & Erix Ruiz, 2011. "¿Cuál Es El Costo De La Contaminación Ambiental Minerasobre Los Recursos Hídricos En El Perú?: Comentarios," Documentos de Trabajo / Working Papers 2011-326, Departamento de Economía - Pontificia Universidad Católica del Perú.
    9. Song, Ma-Lin & Zhang, Lin-Ling & Liu, Wei & Fisher, Ron, 2013. "Bootstrap-DEA analysis of BRICS’ energy efficiency based on small sample data," Applied Energy, Elsevier, vol. 112(C), pages 1049-1055.
    10. Halkos, George E. & Tzeremes, Nickolaos G., 2012. "Analyzing the Greek renewable energy sector: A Data Envelopment Analysis approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(5), pages 2884-2893.

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