Advertisement

Annals of Operations Research

, Volume 274, Issue 1–2, pp 471–499 | Cite as

Integrated data envelopment analysis and cooperative game for evaluating energy efficiency of transportation sector: a case of Iran

  • Hashem OmraniEmail author
  • Khatereh Shafaat
  • Arash Alizadeh
Original Research

Abstract

Transportation sector with the consumption of 25% of energy play a major role in Iranian economy. This sector produces 27% of total undesirable greenhouse gases in Iran which has directly harmful effects on the environment. Hence, performance assessment of energy efficiency of transportation sector is one of the most important issues for policy makers. In this paper, energy efficiency of transportation sector of 20 provinces in Iran is evaluated based on data envelopment analysis (DEA)—cooperative game approach. First, selected inputs and outputs are categorized into energy and non-energy inputs and desirable and undesirable outputs. Then, classical DEA model is applied to evaluate and rank the provinces. Since, the classical DEA model can’t distinguish between efficient provinces, so, this paper ranks the provinces based on combination of cross-efficiency DEA and cooperative game approaches. In the cooperative game theory, each province is considered as a player and the suitable characteristic function is defined for players. Finally, by calculating the Shapley value for each player, the final ranks of transportation sectors in provinces are concluded. The results indicate that some smaller provinces have better energy efficiency in transportation sector in comparison with big provinces.

Keywords

DEA Cross-efficiency DEA Cooperative game Transportation sector Energy efficiency 

Notes

Acknowledgement

The authors are grateful for the valuable comments and suggestion from the respected reviewers. Their valuable comments and suggestions have enhanced the strength and significance of the paper.

References

  1. Andersen, P., & Petersen, N. C. (1993). A procedure for ranking efficient units in data envelopment analysis. Management Science, 39(10), 1261–1264.CrossRefGoogle Scholar
  2. Avkiran, N. K. (2011). Association of DEA super-efficiency estimates with financial ratios: Investigating the case for Chinese banks. Omega., 39(3), 323–334.CrossRefGoogle Scholar
  3. Banker, R. D., Charnes, A., & Cooper, W. W. (1984). Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management Science, 30(9), 1078–1092.CrossRefGoogle Scholar
  4. Barros, C. P., Wanke, P., Dumbo, S., & Manso, J. P. (2017). Efficiency in angolan hydro-electric power station: A two-stage virtual frontier dynamic DEA and simplex regression approach. Renewable and Sustainable Energy Reviews, 78, 588–596.CrossRefGoogle Scholar
  5. Bi, G., Wang, P., Yang, F., & Liang, L. (2014). Energy and environmental efficiency of China’s transportation sector: A multidirectional analysis approach. Mathematical Problems in Engineering2014.  https://doi.org/10.1155/2014/539596.
  6. Bian, Y. W., & Xu, H. (2013). DEA ranking method based upon virtual envelopment frontier and TOPSIS. Systems of Engineering, Theory Practice, 33(2), 482–488.Google Scholar
  7. Chang, Y. T., Zhang, N., Danao, D., & Zhang, N. (2013). Environmental efficiency analysis of transportation system in China: A non-radial DEA approach. Energy Policy, 58, 277–283.CrossRefGoogle Scholar
  8. Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2(6), 429–444.CrossRefGoogle Scholar
  9. Chu, J. F., Wu, J., & Song, M. L. (2016). An SBM-DEA model with parallel computing design for environmental efficiency evaluation in the big data context: A transportation system application. Annals of Operations Research, 1–20.  https://doi.org/10.1007/s10479-016-2264-7.
  10. Cui, Q., & Li, Y. (2014). The evaluation of transportation energy efficiency: An application of three-stage virtual frontier DEA. Transportation Research Part D: Transport and Environment, 29, 1–11.CrossRefGoogle Scholar
  11. Cui, Q., & Li, Y. (2015). An empirical study on the influencing factors of transportation carbon efficiency: Evidences from fifteen countries. Applied Energy, 141, 209–217.CrossRefGoogle Scholar
  12. Cui, Q., Li, Y., Yu, C. L., & Wei, Y. M. (2016). Evaluating energy efficiency for airlines: An application of virtual frontier dynamic slacks based measure. Energy, 113, 1231–1240.CrossRefGoogle Scholar
  13. Dotoli, M., Epicoco, N., Falagario, M., & Sciancalepore, F. (2015). A cross-efficiency fuzzy data envelopment analysis technique for performance evaluation of decision making units under uncertainty. Computers and Industrial Engineering, 79, 103–114.CrossRefGoogle Scholar
  14. Hatami-Marbini, A., Agrell, P. J., Tavana, M., & Khoshnevis, P. (2017). A flexible cross-efficiency fuzzy data envelopment analysis model for sustainable sourcing. Journal of Cleaner Production, 142, 2761–2779.CrossRefGoogle Scholar
  15. Jaber, J. O., Al-Ghandoor, A., & Sawalha, S. A. (2008). Energy analysis and exergy utilization in the transportation sector of Jordan. Energy Policy, 36(8), 2995–3000.CrossRefGoogle Scholar
  16. Lam, K. F. (2010). In the determination of weight sets to compute cross-efficiency ratios in DEA. Journal of the Operational Research Society, 61(1), 134–143.CrossRefGoogle Scholar
  17. Li, Y., Wang, Y. Z., & Cui, Q. (2015). Evaluating airline efficiency: An application of virtual frontier network SBM. Transportation Research Part E: Logistics and Transportation Review, 81, 1–17.CrossRefGoogle Scholar
  18. Li, Y., Wang, Y. Z., & Cui, Q. (2016a). Energy efficiency measures for airlines: An application of virtual frontier dynamic range adjusted measure. Journal of Renewable and Sustainable Energy, 8(1), 015901.CrossRefGoogle Scholar
  19. Li, Y., Xie, J., Wang, M., & Liang, L. (2016b). Super efficiency evaluation using a common platform on a cooperative game. European Journal of Operational Research, 255(3), 884–892.CrossRefGoogle Scholar
  20. Li, Y., Yang, F., Liang, L., & Hua, Z. (2009). Allocating the fixed cost as a complement of other cost inputs: A DEA approach. European Journal of Operational Research, 197(1), 389–401.CrossRefGoogle Scholar
  21. Li, T., Yang, W., Zhang, H., & Cao, X. (2016c). Evaluating the impact of transport investment on the efficiency of regional integrated transport systems in China. Transport Policy, 45, 66–76.CrossRefGoogle Scholar
  22. Liang, L., Wu, J., Cook, W. D., & Zhu, J. (2008). Alternative secondary goals in DEA cross-efficiency evaluation. International Journal of Production Economics, 113(2), 1025–1030.CrossRefGoogle Scholar
  23. Lim, S., Oh, K. W., & Zhu, J. (2014). Use of DEA cross-efficiency evaluation in portfolio selection: An application to Korean stock market. European Journal of Operational Research, 236(1), 361–368.CrossRefGoogle Scholar
  24. Lipscy, P. Y., & Schipper, L. (2013). Energy efficiency in the Japanese transport sector. Energy Policy, 56, 248–258.CrossRefGoogle Scholar
  25. Liu, X., Chu, J., Yin, P., & Sun, J. (2017a). DEA cross-efficiency evaluation considering undesirable output and ranking priority: A case study of eco-efficiency analysis of coal-fired power plants. Journal of Cleaner Production, 142, 877–885.CrossRefGoogle Scholar
  26. Liu, W. B., Meng, W., Li, X. X., & Zhang, D. Q. (2010). DEA models with undesirable inputs and outputs. Annals of Operations Research, 173(1), 177–194.CrossRefGoogle Scholar
  27. Liu, H., Zhang, Y., Zhu, Q., & Chu, J. (2017b). Environmental efficiency of land transportation in China: A parallel slack-based measure for regional and temporal analysis. Journal of Cleaner Production, 142, 867–876.CrossRefGoogle Scholar
  28. Llorca, M., & Jamasb, T. (2017). Energy efficiency and rebound effect in European road freight transport. Transportation Research Part A: Policy and Practice, 101, 98–110.Google Scholar
  29. Mashayekhi, Z., & Omrani, H. (2016). An integrated multi-objective Markowitz DEA cross-efficiency model with fuzzy returns for portfolio selection problem. Applied Soft Computing, 38, 1–9.CrossRefGoogle Scholar
  30. Meng, F., Liu, G., Yang, Z., Casazza, M., Cui, S., & Ulgiati, S. (2017). Energy efficiency of urban transportation system in Xiamen China. An integrated approach. Applied Energy, 186, 234–248.CrossRefGoogle Scholar
  31. Nakabayashi, K., & Tone, K. (2006). Egoist’s dilemma: a DEA game. Omega., 34(2), 135–148.CrossRefGoogle Scholar
  32. Omrani, H., Beiragh, R. G., & Kaleibari, S. S. (2015). Performance assessment of Iranian electricity distribution companies by an integrated cooperative game data envelopment analysis principal component analysis approach. International Journal of Electrical Power & Energy Systems, 64, 617–625.CrossRefGoogle Scholar
  33. Oral, M., Amin, G. R., & Oukil, A. (2015). Cross-efficiency in DEA: A maximum resonated appreciative model. Measurement, 63, 159–167.CrossRefGoogle Scholar
  34. Oukil, A., & Amin, G. R. (2015). Maximum appreciative cross-efficiency in DEA: A new ranking method. Computers & Industrial Engineering, 81, 14–21.CrossRefGoogle Scholar
  35. Ramanathan, R. (2000). A holistic approach to compare energy efficiencies of different transport modes. Energy Policy, 28(11), 743–747.CrossRefGoogle Scholar
  36. Ramón, N., Ruiz, J. L., & Sirvent, I. (2010). On the choice of weights profiles in cross-efficiency evaluations. European Journal of Operational Research, 207(3), 1564–1572.CrossRefGoogle Scholar
  37. Rezaee, M. J., Izadbakhsh, H., & Yousefi, S. (2016). An improvement approach based on DEA-game theory for comparison of operational and spatial efficiencies in urban transportation systems. KSCE Journal of Civil Engineering, 20(4), 1526–1531.CrossRefGoogle Scholar
  38. Rezaee, M. J., Moini, A., & Asgari, F. H. A. (2012). Unified performance evaluation of health centers with integrated model of data envelopment analysis and bargaining game. Journal of Medical Systems, 36(6), 3805–3815.CrossRefGoogle Scholar
  39. Roboredo, M. C., Aizemberg, L., & Meza, L. A. (2015). The DEA game cross-efficiency model applied to the Brazilian football championship. Procedia Computer Science, 55, 758–763.CrossRefGoogle Scholar
  40. Ruiz, J. L. (2013). Cross-efficiency evaluation with directional distance functions. European Journal of Operational Research, 228(1), 181–189.CrossRefGoogle Scholar
  41. Sadjadi, S. J., Omrani, H., Abdollahzadeh, S., Alinaghian, M., & Mohammadi, H. (2011). A robust super-efficiency data envelopment analysis model for ranking of provincial gas companies in Iran. Expert Systems with Applications, 38(9), 10875–10881.CrossRefGoogle Scholar
  42. Saidur, R., Sattar, M. A., Masjuki, H. H., Ahmed, S., & Hashim, U. (2007). An estimation of the energy and exergy efficiencies for the energy resources consumption in the transportation sector in Malaysia. Energy Policy, 35(8), 4018–4026.CrossRefGoogle Scholar
  43. Seiford, L. M., & Zhu, J. (2002). Modeling undesirable factors in efficiency evaluation. European Journal of Operational Research, 142(1), 16–20.CrossRefGoogle Scholar
  44. Sexton, T. R., Silkman, R. H., & Hogan, A. J. (1986). Data envelopment analysis: Critique and extensions. New Directions for Program Evaluation., 1986(32), 73–105.CrossRefGoogle Scholar
  45. Song, M., Zheng, W., & Wang, Z. (2016). Environmental efficiency and energy consumption of highway transportation systems in China. International Journal of Production Economics, 181, 441–449.CrossRefGoogle Scholar
  46. Wanke, P., & Barros, C. P. (2016). Efficiency in Latin American airlines: a two-stage approach combining Virtual Frontier Dynamic DEA and Simplex Regression. Journal of Air Transport Management, 54, 93–103.CrossRefGoogle Scholar
  47. Wu, J., Chu, J., Sun, J., & Zhu, Q. (2016a). DEA cross-efficiency evaluation based on Pareto improvement. European Journal of Operational Research, 248(2), 571–579.CrossRefGoogle Scholar
  48. Wu, J., & Liang, L. (2012). A multiple criteria ranking method based on game cross-evaluation approach. Annals of Operations Research, 197(1), 191–200.CrossRefGoogle Scholar
  49. Wu, J., Liang, L., & Yang, F. (2009). Determination of the weights for the ultimate cross-efficiency using Shapley value in cooperative game. Expert Systems with Applications, 36(1), 872–876.CrossRefGoogle Scholar
  50. Wu, J., Sun, J., Liang, L., & Zha, Y. (2011). Determination of weights for ultimate cross-efficiency using Shannon entropy. Expert Systems with Applications, 38(5), 5162–5165.CrossRefGoogle Scholar
  51. Wu, J., Zhu, Q., Chu, J., Liu, H., & Liang, L. (2016b). Measuring energy and environmental efficiency of transportation systems in China based on a parallel DEA approach. Transportation Research Part D: Transport and Environment, 48, 460–472.CrossRefGoogle Scholar
  52. Yu, M. M., Ting, S. C., & Chen, M. C. (2010). Evaluating the cross-efficiency of information sharing in supply chains. Expert Systems with Applications, 37(4), 2891–2897.CrossRefGoogle Scholar
  53. Zhang, M., Li, G., Mu, H. L., & Ning, Y. D. (2011). Energy and exergy efficiencies in the Chinese transportation sector, 1980–2009. Energy, 36(2), 770–776.CrossRefGoogle Scholar
  54. Zhou, G., Chung, W., & Zhang, Y. (2014). Measuring energy efficiency performance of China’s transport sector: A data envelopment analysis approach. Expert Systems with Applications, 41(2), 709–722.CrossRefGoogle Scholar

Copyright information

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

Authors and Affiliations

  • Hashem Omrani
    • 1
    Email author
  • Khatereh Shafaat
    • 1
  • Arash Alizadeh
    • 1
  1. 1.Faculty of Industrial EngineeringUrmia University of TechnologyUrmiaIran

Personalised recommendations