IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i18p4902-d415822.html
   My bibliography  Save this article

Performance Assessment Based on the Relative Efficiency of Indian Opencast Coal Mines Using Data Envelopment Analysis and Malmquist Productivity Index

Author

Listed:
  • Biswaranjita Mahapatra

    (Department of Management Studies, Indian Institute of Technology (Indian School of Mines), Dhanbad 826004, India)

  • Chandan Bhar

    (Department of Management Studies, Indian Institute of Technology (Indian School of Mines), Dhanbad 826004, India)

  • Sandeep Mondal

    (Department of Management Studies, Indian Institute of Technology (Indian School of Mines), Dhanbad 826004, India)

Abstract

Coal is the primary source of energy in India. Despite being the second-largest coal-producing country, there exists a significant difference in demand and production in India. In this study, the relative efficiency of twenty-eight selected opencast mines from a large public sector undertaking coal company in India for 2018–2019 was assessed and ranked by using data envelopment analysis (DEA). This study used input-oriented DEA with efficiency decomposition to pure technical efficiency, technical efficiency, and scale efficiency. The result showed that 25% and 36% of mines were efficient in technical efficiency and pure technical efficiency, respectively, whereas the eight mines scale efficiency was inefficient with a decreasing return to scale. Further, in this study, theMalmquist Productivity Index (MPI)was employed to measure the efficiency of the selected mines for three consecutive years (2016–2017 to 2018–2019). The result shows that in only three mines the efficiency is continuously improving from 2016–2017 to 2018–2019, whereas in more than 20% of mines the efficiency score is decreasing. Comparing theMPI efficiency and productivity assessment throughout the years, changes in innovation and technology are increasing from 2017–2018 to 2018–2019. Finally, the study concluded with a comprehensive evaluation of each variable with mines performance. The author formulated the strategies, which in turn help coal professionals to improve the efficiency of the mine.

Suggested Citation

  • Biswaranjita Mahapatra & Chandan Bhar & Sandeep Mondal, 2020. "Performance Assessment Based on the Relative Efficiency of Indian Opencast Coal Mines Using Data Envelopment Analysis and Malmquist Productivity Index," Energies, MDPI, vol. 13(18), pages 1-21, September.
  • Handle: RePEc:gam:jeners:v:13:y:2020:i:18:p:4902-:d:415822
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/18/4902/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/13/18/4902/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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. Jamasb, Tooraj & Pollitt, Michael & Triebs, Thomas, 2008. "Productivity and efficiency of US gas transmission companies: A European regulatory perspective," Energy Policy, Elsevier, vol. 36(9), pages 3398-3412, September.
    3. Barros, Carlos Pestana, 2008. "Efficiency analysis of hydroelectric generating plants: A case study for Portugal," Energy Economics, Elsevier, vol. 30(1), pages 59-75, January.
    4. Yang, Hongliang & Pollitt, Michael, 2009. "Incorporating both undesirable outputs and uncontrollable variables into DEA: The performance of Chinese coal-fired power plants," European Journal of Operational Research, Elsevier, vol. 197(3), pages 1095-1105, September.
    5. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
    6. Chiang Kao, 2017. "Network Data Envelopment Analysis," International Series in Operations Research and Management Science, Springer, number 978-3-319-31718-2, September.
    7. Caves, Douglas W & Christensen, Laurits R & Diewert, W Erwin, 1982. "The Economic Theory of Index Numbers and the Measurement of Input, Output, and Productivity," Econometrica, Econometric Society, vol. 50(6), pages 1393-1414, November.
    8. Kulshreshtha, Mudit & Parikh, Jyoti K., 2002. "Study of efficiency and productivity growth in opencast and underground coal mining in India: a DEA analysis," Energy Economics, Elsevier, vol. 24(5), pages 439-453, September.
    9. Fang, Hong & Wu, Junjie & Zeng, Catherine, 2009. "Comparative study on efficiency performance of listed coal mining companies in China and the US," Energy Policy, Elsevier, vol. 37(12), pages 5140-5148, December.
    10. Vaninsky, Alexander, 2006. "Efficiency of electric power generation in the United States: Analysis and forecast based on data envelopment analysis," Energy Economics, Elsevier, vol. 28(3), pages 326-338, May.
    11. Thompson, Russell G. & Dharmapala, P. S. & Thrall, Robert M., 1995. "Linked-cone DEA profit ratios and technical efficiency with application to Illinois coal mines," International Journal of Production Economics, Elsevier, vol. 39(1-2), pages 99-115, April.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Jarosław Kaczmarek, 2022. "The Balance of Outlays and Effects of Restructuring Hard Coal Mining Companies in Terms of Energy Policy of Poland PEP 2040," Energies, MDPI, vol. 15(5), pages 1-30, March.
    2. Qing Yang & Jinbo Qiao & Shaohui Zou & Delu Wang & Jiayi Hao, 2023. "Towards Sustainable Development: Investigating the Heterogeneity and Driving Factors of Green Total Factor Productivity in Coal Enterprises," Sustainability, MDPI, vol. 15(19), pages 1-18, October.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    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. Fang, Hong & Wu, Junjie & Zeng, Catherine, 2009. "Comparative study on efficiency performance of listed coal mining companies in China and the US," Energy Policy, Elsevier, vol. 37(12), pages 5140-5148, December.
    3. Ahmad Hosseinzadeh & Russell Smyth & Abbas Valadkhani & Amir Moradi, 2018. "What determines the efficiency of Australian mining companies?," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 62(1), pages 121-138, January.
    4. Yan He & Yung-ho Chiu & Bin Zhang, 2020. "Prevaluating Technical Efficiency Gains From Potential Mergers and Acquisitions in China’s Coal Industry," SAGE Open, , vol. 10(3), pages 21582440209, July.
    5. Wu, Peng & Wang, Yiqing & Chiu, Yung-ho & Li, Ying & Lin, Tai-Yu, 2019. "Production efficiency and geographical location of Chinese coal enterprises - undesirable EBM DEA," Resources Policy, Elsevier, vol. 64(C).
    6. Fallahi, Alireza & Ebrahimi, Reza & Ghaderi, S.F., 2011. "Measuring efficiency and productivity change in power electric generation management companies by using data envelopment analysis: A case study," Energy, Elsevier, vol. 36(11), pages 6398-6405.
    7. Ke Wang & Chia-Yen Lee & Jieming Zhang & Yi-Ming Wei, 2018. "Operational performance management of the power industry: a distinguishing analysis between effectiveness and efficiency," Annals of Operations Research, Springer, vol. 268(1), pages 513-537, September.
    8. Liu, C.H. & Lin, Sue J. & Lewis, Charles, 2010. "Evaluation of thermal power plant operational performance in Taiwan by data envelopment analysis," Energy Policy, Elsevier, vol. 38(2), pages 1049-1058, February.
    9. Mahlberg, Bernhard & Luptacik, Mikulas & Sahoo, Biresh K., 2011. "Examining the drivers of total factor productivity change with an illustrative example of 14 EU countries," Ecological Economics, Elsevier, vol. 72(C), pages 60-69.
    10. Barnabé Walheer, 2019. "Dynamic directional nonparametric profit efficiency analysis for a single decision-making unit: an aggregation approach," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 41(4), pages 1123-1149, December.
    11. Khoshroo, Alireza & Izadikhah, Mohammad & Emrouznejad, Ali, 2022. "Total factor energy productivity considering undesirable pollutant outputs: A new double frontier based malmquist productivity index," Energy, Elsevier, vol. 258(C).
    12. Li, Feng & Zhang, Danlu & Zhang, Jinyu & Kou, Gang, 2022. "Measuring the energy production and utilization efficiency of Chinese thermal power industry with the fixed-sum carbon emission constraint," International Journal of Production Economics, Elsevier, vol. 252(C).
    13. Malin Song & Jianlin Wang & Jiajia Zhao & Tomas Baležentis & Zhiyang Shen, 2020. "Production and safety efficiency evaluation in Chinese coal mines: accident deaths as undesirable output," Annals of Operations Research, Springer, vol. 291(1), pages 827-845, August.
    14. Seifert, Stefan & Cullmann, Astrid & von Hirschhausen, Christian, 2016. "Technical efficiency and CO2 reduction potentials — An analysis of the German electricity and heat generating sector," Energy Economics, Elsevier, vol. 56(C), pages 9-19.
    15. Chen, Zhongfei & Barros, Carlos Pestana & Borges, Maria Rosa, 2015. "A Bayesian stochastic frontier analysis of Chinese fossil-fuel electricity generation companies," Energy Economics, Elsevier, vol. 48(C), pages 136-144.
    16. Barros, Carlos Pestana & Peypoch, Nicolas, 2008. "Technical efficiency of thermoelectric power plants," Energy Economics, Elsevier, vol. 30(6), pages 3118-3127, November.
    17. Thompson, Russell G. & Brinkmann, Emile J. & Dharmapala, P. S. & Gonzalez-Lima, M. D. & Thrall, Robert M., 1997. "DEA/AR profit ratios and sensitivity of 100 large U.S. banks," European Journal of Operational Research, Elsevier, vol. 98(2), pages 213-229, April.
    18. Zhou, P. & Ang, B.W. & Poh, K.L., 2008. "A survey of data envelopment analysis in energy and environmental studies," European Journal of Operational Research, Elsevier, vol. 189(1), pages 1-18, August.
    19. Holvad, Torben, 2020. "Efficiency analyses for the railway sector: An overview of key issues," Research in Transportation Economics, Elsevier, vol. 82(C).
    20. Wang, Ke & Wei, Yi-Ming & Huang, Zhimin, 2018. "Environmental efficiency and abatement efficiency measurements of China's thermal power industry: A data envelopment analysis based materials balance approach," European Journal of Operational Research, Elsevier, vol. 269(1), pages 35-50.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:13:y:2020:i:18:p:4902-:d:415822. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.