Quarterly Publication

Document Type : Original Article

Authors

1 Department of Mathematics, Jolfa International Branch, Islamic Azad University, Jolfa, Iran.

2 Department of Mathematics, Sowmesara Branch, Islamic Azad University, Sowmesara, Iran.

Abstract

The traditional Data Envelopment Analysis (DEA) model on network-structured performance analysis normally considers desirable intermediate measures. In many real cases, the intermediate measures consist of both desirable and undesirable factors. The motivation of this paper is employing “Natural and managerial disposability” in two-stage network structures with undesirable intermediate measure. The non-cooperative game theory is proposed to study the two-stage structure. A real case of 34 OECD countries in 2012 has been illustrated to shed a light on applicability of the proposed methodology.

Keywords

  1. Avilés-Sacoto, E. C., Avilés-Sacoto, S. V., Güemes-Castorena, D., & Cook, W. D. (2021). Environmental performance evaluation: a state-level DEA analysis. Socio-economic planning sciences, 78, 101082. https://doi.org/10.1016/j.seps.2021.101082
  2. Banker, R. D., Charnes, A., & Cooper, W. W. (1984). Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management science30(9), 1078-1092. https://doi.org/10.1287/mnsc.30.9.1078
  3. Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European journal of operational research2(6), 429-444. https://doi.org/10.1016/0377-2217(78)90138-8
  4. Mavi, R. K., Saen, R. F., & Goh, M. (2019). Joint analysis of eco-efficiency and eco-innovation with common weights in two-stage network DEA: a big data approach. Technological forecasting and social change144, 553-562. https://doi.org/10.1016/j.techfore.2018.01.035
  5. Omrani, H., Alizadeh, A., Emrouznejad, A., & Teplova, T. (2022). A robust credibility DEA model with fuzzy perturbation degree: An application to hospitals performance. Expert Systems with Applications189, 116021.
  6. Porter, M. E., & Van der Linde, C. (1995). Toward a new conception of the environment-competitiveness relationship. Journal of economic perspectives9(4), 97-118. DOI: 1257/jep.9.4.97
  7. Shao, L., Yu, X., & Feng, C. (2019). Evaluating the eco-efficiency of China's industrial sectors: a two-stage network data envelopment analysis. Journal of environmental management247, 551-560. https://doi.org/10.1016/j.jenvman.2019.06.099
  8. Sueyoshi, T., & Goto, M. (2010). Measurement of a linkage among environmental, operational, and financial performance in Japanese manufacturing firms: a use of Data Envelopment Analysis with strong complementary slackness condition. European journal of operational research207(3), 1742-1753. https://doi.org/10.1016/j.ejor.2010.07.024
  9. Sueyoshi, T., & Goto, M. (2010). Should the US clean air act include CO2 emission control?: examination by data envelopment analysis. Energy policy38(10), 5902-5911. https://doi.org/10.1016/j.enpol.2010.05.044
  10. Sueyoshi, T., & Goto, M. (2011). DEA approach for unified efficiency measurement: assessment of Japanese fossil fuel power generation. Energy economics33(2), 292-303. https://doi.org/10.1016/j.eneco.2010.07.008
  11. Sueyoshi, T., & Goto, M. (2011). Methodological comparison between two unified (operational and environmental) efficiency measurements for environmental assessment. European journal of operational research210(3), 684-693. https://doi.org/10.1016/j.ejor.2010.10.030
  12. Sueyoshi, T., & Goto, M. (2011). Measurement of returns to scale and damages to scale for DEA-based operational and environmental assessment: how to manage desirable (good) and undesirable (bad) outputs?. European journal of operational research211(1), 76-89. https://doi.org/10.1016/j.ejor.2010.11.013
  13. Sueyoshi, T., & Goto, M. (2012). Data envelopment analysis for environmental assessment: comparison between public and private ownership in petroleum industry. European journal of operational research216(3), 668-678. https://doi.org/10.1016/j.ejor.2011.07.046
  14. Sueyoshi, T., & Goto, M. (2013). Returns to scale vs. damages to scale in data envelopment analysis: an impact of US clean air act on coal-fired power plants. Omega41(2), 164-175. https://doi.org/10.1016/j.omega.2010.04.005
  15. Sueyoshi, T., & Goto, M. (2012). Returns to scale and damages to scale under natural and managerial disposability: strategy, efficiency and competitiveness of petroleum firms. Energy economics34(3), 645-662. https://doi.org/10.1016/j.eneco.2011.07.003
  16. Sueyoshi, T., & Goto, M. (2012). Environmental assessment by DEA radial measurement: US coal-fired power plants in ISO (Independent System Operator) and RTO (Regional Transmission Organization). Energy economics34(3), 663-676. https://doi.org/10.1016/j.eneco.2011.08.016
  17. Sueyoshi, T., & Goto, M. (2012). Weak and strong disposability vs. natural and managerial disposability in DEA environmental assessment: comparison between Japanese electric power industry and manufacturing industries. Energy economics34(3), 686-699. https://doi.org/10.1016/j.eneco.2011.10.018
  18. Sueyoshi, T., & Goto, M. (2012). DEA radial measurement for environmental assessment and planning: desirable procedures to evaluate fossil fuel power plants. Energy policy41, 422-432. https://doi.org/10.1016/j.enpol.2011.11.003
  19. Sueyoshi, T., Goto, M., & Ueno, T. (2010). Performance analysis of US coal-fired power plants by measuring three DEA efficiencies. Energy policy38(4), 1675-1688. https://doi.org/10.1016/j.enpol.2009.11.017
  20. Sueyoshi, T., Yuan, Y., & Goto, M. (2017). A literature study for DEA applied to energy and environment. Energy economics62, 104-124. https://doi.org/10.1016/j.eneco.2016.11.006
  21. Tachega, M. A., Yao, X., Liu, Y., Ahmed, D., Li, H., & Mintah, C. (2021). Energy efficiency evaluation of oil producing economies in Africa: DEA, malmquist and multiple regression approaches. Cleaner environmental systems2, 100025. https://doi.org/10.1016/j.cesys.2021.100025
  22. Wang, D., Du, Z., & Wu, H. (2020). Ranking global cities based on economic performance and climate change mitigation. Sustainable cities and society62, 102395. https://doi.org/10.1016/j.scs.2020.102395
  23. Wang, R., Wang, Q., & Yao, S. (2021). Evaluation and difference analysis of regional energy efficiency in China under the carbon neutrality targets: insights from DEA and Theil models. Journal of environmental management293, 112958. https://doi.org/10.1016/j.jenvman.2021.112958
  24. Zhao, P., Zeng, L., Li, P., Lu, H., Hu, H., Li, C., ... & Qi, Y. (2022). China's transportation sector carbon dioxide emissions efficiency and its influencing factors based on the EBM DEA model with undesirable outputs and spatial Durbin model. Energy238, 121934. https://doi.org/10.1016/j.energy.2021.121934