Advertisement

Production and safety efficiency evaluation in Chinese coal mines: accident deaths as undesirable output

  • Malin Song
  • Jianlin WangEmail author
  • Jiajia Zhao
  • Tomas Baležentis
  • Zhiyang Shen
S.I.: RealCaseOR

Abstract

Coal mining is one of the highest-risk industries in China. Accident deaths in coal mines attract intense concern every year. This is the first attempt to measure production efficiency of coal mines with consideration of accident deaths. A combined directional distance function and slacks-based model is proposed to assess production and safety efficiency across 18 coal-mining provinces in China. Results showed that the average total factor humanitarian-production efficiency is poor, with nearly half of production potential unexploited. Safety efficiency is also low, and half of the deaths would be avoided if all coal enterprises operated at fully efficient levels. The directional contribution analysis pointed out that southern provinces should pay more attention to accident deaths than northern ones, while the importance of reducing accident death in efficiency promotion declined for nearly all provinces, which creates a tradeoff between safety and efficiency for enterprises and regulators. The results of this study showed that the safety situation of coal mines is not as optimistic as the official data suggest. Effective prevention mechanisms are urgently needed to prevent disastrous accidents in coal mines in China.

Keywords

Total factor humanitarian-production efficiency Total factor safety efficiency Directional distance function Slacks-based measure Accident deaths Coal mining 

Notes

Acknowledgements

We would like to show our appreciation for the support of the Humanities and Social Science Research of the Ministry of Education Youth Project of China (No. 16YJCZH155), the Program for New Century Excellent Talents in University (No. NCET-12-0595), National Natural Science Foundation of China (No. 71171001), Key Foundation of Natural Science for Colleges and Universities in Anhui, China (No. KJ2011A001), Soft Science Foundation of Anhui, China (No. 12020503063), and Key Foundation of National Research in Statistics of China (No. 2011LZ023), Humanities and Social Science Research Project of Education Department in Liaoning, China (No. LN2017QN001).

References

  1. Arabi, B., Munisamy, S., & Emrouznejad, A. (2015). A new slacks-based measure of Malmquist–Luenberger index in the presence of undesirable outputs. Omega, 51, 29–37.CrossRefGoogle Scholar
  2. Asmild, M., Kronborg, D., & Matthews, K. (2016). Introducing and modeling inefficiency contributions. European Journal of Operational Research, 248(2), 725–730.CrossRefGoogle Scholar
  3. Baek, C., & Lee, J.-D. (2009). The relevance of DEA benchmarking information and the least-distance measure. Mathematical and Computer Modelling, 49(1), 265–275.CrossRefGoogle Scholar
  4. Bogetoft, P., & Otto, L. (2011). Additional topics in DEA. In P. Bogetoft & L. Otto (Eds.), Benchmarking with DEA, SFA, and R (pp. 115–153). New York: Springer.CrossRefGoogle Scholar
  5. Byrnes, P., Fare, R., & Grosskopf, S. (1984). Measuring productive efficiency: An application to Illinois strip mines. Management Science, 30(6), 671–681.CrossRefGoogle Scholar
  6. Byrnes, P., Fare, R., Grosskopf, S., & Lovell, C. A. K. (1988). The effect of unions on productivity: U.S. surface mining of coal. Management Science, 34(9), 1037–1053.CrossRefGoogle Scholar
  7. Chambers, R. G., Chung, Y., & Färe, R. (1996). Benefit and distance functions. Journal of Economic Theory, 70(2), 407–419.CrossRefGoogle Scholar
  8. Charnes, A., & Cooper, W. W. (1962). Programming with linear fractional functionals. Naval Research Logistics Quarterly, 9(1), 181–186.CrossRefGoogle Scholar
  9. Charnes, A., Cooper, W. W., Golany, B., Seiford, L., & Stutz, J. (1985). Foundations of data envelopment analysis for Pareto–Koopmans efficient empirical production functions. Journal of Econometrics, 30(1), 91–107.CrossRefGoogle Scholar
  10. Charnes, A., Cooper, W. W., & Rhodes, E. J. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2(6), 429–444.CrossRefGoogle Scholar
  11. Choi, T.-M. (2015). Sustainable management of mining operations with accidents: A mean–variance optimization model. Resources Policy, 46, 116–122.CrossRefGoogle Scholar
  12. Choi, T.-M. (2016). Multi-period risk minimization purchasing models for fashion products with interest rate, budget, and profit target considerations. Annals of Operations Research, 237(1), 77–98.CrossRefGoogle Scholar
  13. Choi, T.-M., Cheng, T. C. E., & Zhao, X. (2016). Multi-methodological research in operations management. Production and Operations Management, 25(3), 379–389.CrossRefGoogle Scholar
  14. Chung, Y. H., Färe, R., & Grosskopf, S. (1997). Productivity and undesirable outputs: A directional distance function approach. Journal of Environmental Management, 51(3), 229–240.CrossRefGoogle Scholar
  15. Daraio, C., & Simar, L. (2016). Efficiency and benchmarking with directional distances: A data-driven approach. Journal of the Operational Research Society, 67(7), 928–944.CrossRefGoogle Scholar
  16. Fang, H., Wu, J., & Zeng, C. (2009). Comparative study on efficiency performance of listed coal mining companies in China and the US. Energy Policy, 37(12), 5140–5148.CrossRefGoogle Scholar
  17. Färe, R., & Grosskopf, S. (2006). New directions: Efficiency and Productivity. New York: Springer.Google Scholar
  18. Färe, R., Grosskopf, S., & Margaritis, D. (2015). Distance functions in primal and dual spaces. In J. Zhu (Ed.), Data envelopment analysis: A handbook of models and methods (pp. 1–21). New York: Springer.CrossRefGoogle Scholar
  19. Färe, R., Grosskopf, S., & Pasurka, C. A. (2007). Environmental production functions and environmental directional distance functions. Energy, 32(7), 1055–1066.CrossRefGoogle Scholar
  20. Färe, R., Grosskopf, S., & Whittaker, G. (2013). Directional output distance functions: Endogenous directions based on exogenous normalization constraints. Journal of Productivity Analysis, 40(3), 267–269.CrossRefGoogle Scholar
  21. Farsi, M., Filippini, M., & Greene, W. (2005). Efficiency measurement in network industries: Application to the Swiss railway companies. Journal of Regulatory Economics, 28(1), 69–90.CrossRefGoogle Scholar
  22. Farsi, M., Filippini, M., & Greene, W. (2006). Application of panel data models in benchmarking analysis of the electricity distribution sector. Annals of Public and Cooperative Economics, 77(3), 271–290.CrossRefGoogle Scholar
  23. Fukuyama, H., & Weber, W. L. (2009). A directional slacks-based measure of technical inefficiency. Socio-Economic Planning Sciences, 43(4), 274–287.CrossRefGoogle Scholar
  24. Geissler, B., Mew, M. C., Weber, O., & Steiner, G. (2015). Efficiency performance of the world’s leading corporations in phosphate rock mining. Resources, Conservation and Recycling, 105(Part B), 246–258.CrossRefGoogle Scholar
  25. Homer, A. W. (2009). Coal mine safety regulation in China and the USA. Journal of Contemporary Asia, 39(3), 424–439.CrossRefGoogle Scholar
  26. Kulshreshtha, M., & Parikh, J. K. (2002). Study of efficiency and productivity growth in opencast and underground coal mining in India: A DEA analysis. Energy Economics, 24(5), 439–453.CrossRefGoogle Scholar
  27. Kumar, S., & Managi, S. (2010). Environment and productivities in developed and developing countries: The case of carbon dioxide and sulfur dioxide. Journal of Environmental Management, 91(7), 1580–1592.CrossRefGoogle Scholar
  28. Lee, C.-Y., & Johnson, A. L. (2015). Measuring efficiency in imperfectly competitive markets: An example of rational inefficiency. Journal of Optimization Theory and Applications, 164(2), 702–722.CrossRefGoogle Scholar
  29. Li, L.-B., & Hu, J.-L. (2012). Ecological total-factor energy efficiency of regions in China. Energy Policy, 46, 216–224.CrossRefGoogle Scholar
  30. Lozano, S., & Gutiérrez, E. (2008). Non-parametric frontier approach to modelling the relationships among population, GDP, energy consumption and CO2 emissions. Ecological Economics, 66(4), 687–699.CrossRefGoogle Scholar
  31. Luenberger, D. G. (1992). Benefit functions and duality. Journal of Mathematical Economics, 21(5), 461–481.CrossRefGoogle Scholar
  32. Pal, D., & Mitra, S. K. (2016). An application of the directional distance function with the number of accidents as an undesirable output to measure the technical efficiency of state road transport in India. Transportation Research Part A: Policy and Practice, 93, 1–12.Google Scholar
  33. Shen, Z., Boussemart, J.-P., & Leleu, H. (2017). Aggregate green productivity growth in OECD’s countries. International Journal of Production Economics, 189, 30–39.CrossRefGoogle Scholar
  34. Sider, H. (1983). Safety and productivity in underground coal mining. The Review of Economics and Statistics, 65(2), 225–233.CrossRefGoogle Scholar
  35. Simar, L., & Vanhems, A. (2012). Probabilistic characterization of directional distances and their robust versions. Journal of Econometrics, 166(2), 342–354.CrossRefGoogle Scholar
  36. Song, M., Fisher, R., Wang, J., & Cui, L. (2016). Environmental performance evaluation with big data: Theories and methods. Annals of Operations Research.  https://doi.org/10.1007/s10479-016-2158-8.CrossRefGoogle Scholar
  37. Song, M., & Wang, J. (2016). Coal price fluctuations in China: Economic effects and policy implications. Journal of Renewable & Sustainable Energy, 8(6), 9011–9014.CrossRefGoogle Scholar
  38. Tone, K. (2001). A slacks-based measure of efficiency in data envelopment analysis. European Journal of Operational Research, 130(3), 498–509.CrossRefGoogle Scholar
  39. Tsolas, I. E. (2011). Performance assessment of mining operations using nonparametric production analysis: A bootstrapping approach in DEA. Resources Policy, 36(2), 159–167.CrossRefGoogle Scholar
  40. Tu, J. (2007). Safety challenges in China’s coal mining industry. China Brief, 7(1), 6–8.Google Scholar
  41. Wang, Q., Su, B., Zhou, P., & Chiu, C.-R. (2016). Measuring total-factor CO2 emission performance and technology gaps using a non-radial directional distance function: A modified approach. Energy Economics, 56, 475–482.CrossRefGoogle Scholar
  42. Wang, L., Tran, T., & Nguyen, N. (2015). An empirical study of hybrid DEA and grey system theory on analyzing performance: A case from Indian mining industry. Journal of Applied Mathematics, 2015, 1–15.Google Scholar
  43. Wang, K., Xian, Y., Lee, C.-Y., Wei, Y.-M., & Huang, Z. (2017). On selecting directions for directional distance functions in a non-parametric framework: A review. Annals of Operations Research.  https://doi.org/10.1007/s10479-017-2423-5.CrossRefGoogle Scholar
  44. Weber, M. M., & Weber, W. L. (2004). Productivity and efficiency in the trucking industry: Accounting for traffic fatalities. International Journal of Physical Distribution & Logistics Management, 34(1), 39–61.CrossRefGoogle Scholar
  45. Wu, J., Xiong, B., An, Q., Sun, J., & Wu, H. (2017). Total-factor energy efficiency evaluation of Chinese industry by using two-stage DEA model with shared inputs. Annals of Operations Research, 255(1), 257–276.CrossRefGoogle Scholar
  46. Yu, M.-M., & Fan, C.-K. (2006). Measuring the cost effectiveness of multimode bus transit in the presence of accident risks. Transportation Planning and Technology, 29(5), 383–407.CrossRefGoogle Scholar
  47. Zhou, Y., Xing, X., Fang, K., Liang, D., & Xu, C. (2013). Environmental efficiency analysis of power industry in China based on an entropy SBM model. Energy Policy, 57, 68–75.CrossRefGoogle Scholar
  48. Zofio, J. L., Pastor, J. T., & Aparicio, J. (2013). The directional profit efficiency measure: On why profit inefficiency is either technical or allocative. Journal of Productivity Analysis, 40(3), 257–266.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Statistics and Applied MathematicsAnhui University of Finance and EconomicsBengbuChina
  2. 2.Center for Industrial and Business OrganizationDongbei University of Finance and EconomicsDalianChina
  3. 3.Research Academy of Economic and Social DevelopmentDongbei University of Finance and EconomicsDalianChina
  4. 4.Lithuanian Institute of Agrarian EconomicsVilniusLithuania
  5. 5.The Export-Import Bank of ChinaBeijingChina

Personalised recommendations