Open Access
RAIRO-Oper. Res.
Volume 56, Number 4, July-August 2022
Page(s) 2159 - 2179
Published online 20 July 2022
  • A.P. Afanasiev, V.E. Krivonozhko, A.V. Lychev and O.V. Sukhoroslov, Multidimensional frontier visualization based on optimization methods using parallel computations. J. Global Optim. 76 (2020) 563–574. [CrossRef] [MathSciNet] [Google Scholar]
  • R. Banker, Stochastic data envelopment analysis. Working Paper. Carnegie Mellon University (1988). [Google Scholar]
  • R.D. Banker, Maximum likelihood, consistency and data envelopment analysis: a statistical foundation. Manage. Sci. 39 (1993) 1265–1273. [CrossRef] [Google Scholar]
  • R.D. Banker, A. Charnes and W.W. Cooper, Some models for estimating technical and scale inefficiencies in data envelopment analysis. Manage. Sci. 30 (1984) 1078–1092. [Google Scholar]
  • R.D. Banker, S.M. Datar and C.F. Kemerer, A model to evaluate variables impacting the productivity of software maintenance projects. Manage. Sci. 37 (1991) 1–18. [CrossRef] [Google Scholar]
  • R. Banker, K. Kotarac and L. Neralić, Sensitivity and stability in stochastic data envelopment analysis. J. Oper. Res. Soc. 66 (2015) 134–147. [CrossRef] [Google Scholar]
  • A. Charnes and W.W. Cooper, The Non-Archimedean CCR Ratio for Efficiency Analysis: A Rejoinder to Boyd and Färe. Texas Univ at Austin Center for Cybernetic Studies (1984). [Google Scholar]
  • A. Charnes, W.W. Cooper and E. Rhodes, Measuring the efficiency of decision making units. Eur. J. Oper. Res. 2 (1978) 429–444. [Google Scholar]
  • A. Chames, W. Cooper and E. Rhodes, Short communication: measuring the efficiency of decision making units. Eur. J. Oper. Res. 3 (1979) 339. [CrossRef] [Google Scholar]
  • S. Daneshvar, G. Izbirak and A. Javadi, Sensitivity analysis on modified variable returns to scale model in Data Envelopment Analysis using facet analysis. Comput. Ind. Eng. 76 (2014) 32–39. [CrossRef] [Google Scholar]
  • M. Davtalab-Olyaie, I. Roshdi, V. Partovi Nia and M. Asgharian, On characterizing full dimensional weak facets in DEA with variable returns to scale technology. Optimization 64 (2015) 2455–2476. [CrossRef] [MathSciNet] [Google Scholar]
  • EPI, Environmental Performance Index (EPI) (2021, 16th May). [Google Scholar]
  • M.K. Epstein and J.C. Henderson, Data envelopment analysis for managerial control and diagnosis. Decis. Sci. 20 (1989) 90–119. [CrossRef] [Google Scholar]
  • L.M. Fonseca, J.P. Domingues and A.M. Dima, Mapping the sustainable development goals relationships. Sustainability 12 (2020) 3359. [CrossRef] [Google Scholar]
  • Z. Huang and S.X. Li, Dominance stochastic models in data envelopment analysis. Eur. J. Oper. Res. 95 (1996) 390–403. [CrossRef] [Google Scholar]
  • M.D. Ibrahim and A.A. Alola, Integrated analysis of energy-economic development-environmental sustainability nexus: case study of MENA countries. Sci. Total Environ. 737 (2020) 139768. [CrossRef] [Google Scholar]
  • M. Ibrahim, S. Daneshvar, H. Güden and B. Vizvari, Target setting in data envelopment analysis: efficiency improvement models with predefined inputs/outputs. OPSEARCH 57 (2020) 1319–1336. [CrossRef] [MathSciNet] [Google Scholar]
  • S. Jradi and J. Ruggiero, Stochastic data envelopment analysis: a quantile regression approach to estimate the production frontier. Eur. J. Oper. Res. 278 (2019) 385–393. [CrossRef] [Google Scholar]
  • S. Jradi, C.F. Parmeter and J. Ruggiero, Quantile estimation of the stochastic frontier model. Econ. Lett. 182 (2019) 15–18. [CrossRef] [Google Scholar]
  • S. Kazemi, M. Tavana, M. Toloo and N.A. Zenkevich, A common weights model for investigating efficiency-based leadership in the russian banking industry. RAIRO: Oper. Res. 55 (2021) 213–229. [CrossRef] [EDP Sciences] [MathSciNet] [Google Scholar]
  • E. Kazemzadeh, J.A. Fuinhas, M. Koengkan, F. Osmani and N. Silva, Do energy efficiency and export quality affect the ecological footprint in emerging countries? A two-step approach using the SBM–DEA model and panel quantile regression. Environ. Syst. Decis. (2022) 1–18. DOI: 10.1007/s10669-022-09846-2. [Google Scholar]
  • E. Koçak, H.K. Kınacıand K. Shehzad, Environmental efficiency of disaggregated energy R&D expenditures in OECD: a bootstrap DEA approach. Environ. Sci. Pollut. Res. 28 (2021) 19381–19390. [CrossRef] [PubMed] [Google Scholar]
  • M. Koengkan, J.A. Fuinhas, E. Kazemzadeh, F. Osmani, N.K. Alavijeh, A. Auza and M. Teixeira, Measuring the economic efficiency performance in Latin American and Caribbean countries: an empirical evidence from stochastic production frontier and data envelopment analysis. Int. Econ. 169 (2022) 43–54. [CrossRef] [Google Scholar]
  • V. Moutinho, J.A. Fuinhas, A.C. Marques and R. Santiago, Assessing eco-efficiency through the DEA analysis and decoupling index in the Latin America countries. J. Cleaner Prod. 205 (2018) 512–524. [CrossRef] [Google Scholar]
  • O.B. Olesen and N.C. Petersen, Facet analysis in data envelopment analysis. In: Data Envelopment Analysis, edited by J. Zhu. Springer US (2015). [Google Scholar]
  • O.B. Olesen and, N.C. Petersen, Stochastic data envelopment analysis – a review. Eur. J. Oper. Res. 251 (2016) 2–21. [CrossRef] [Google Scholar]
  • J. Ondrich and J. Ruggiero, Efficiency measurement in the stochastic frontier model. Eur. J. Oper. Res. 129 (2001) 434–442. [CrossRef] [Google Scholar]
  • S. Sarkar, Performance measurement using a novel directional distance function based super efficiency model and neighbourhood theory. RAIRO: Oper. Res. 55 (2021) 3617–3638. [CrossRef] [EDP Sciences] [MathSciNet] [Google Scholar]
  • J.K. Sengupta, Theory of DEA models. In: Dynamics of Data Envelopment Analysis, Springer (1995) 1–37. [Google Scholar]
  • A. Takeda and H. Nishino, On measuring the inefficiency with the inner-product norm in data envelopment analysis. Eur. J. Oper. Res. 133 (2001) 377–393. [CrossRef] [Google Scholar]
  • H. Yang and M. Pollitt, The necessity of distinguishing weak and strong disposability among undesirable outputs in DEA: environmental performance of Chinese coal-fired power plants. Energy Policy 38 (2010) 4440–4444. [CrossRef] [Google Scholar]
  • Z. Zhou, W. Sun, H. Xiao, Q. Jin and W. Liu, Stochastic leader–follower DEA models for two-stage systems. J. Manage. Sci. Eng. 6 (2021) 413–434. [Google Scholar]
  • Q. Zhu, J. Aparicio, F. Li, J. Wu and G. Kou, Determining closest targets on the extended facet production possibility set in data envelopment analysis: modeling and computational aspects. Eur. J. Oper. Res. 296 (2022) 927–939. [CrossRef] [Google Scholar]

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