Abbott, M. (2006). The productivity and efficiency of the Australian electricity supply industry. Energy Economics, 28, 444–454.
Afriat, S. (1972). Efficiency estimation of production functions. International Economic Review, 13, 568–598.
Afsharian, M., Ahn, H., & Alirezaee, M. (2015). Developing selective proportionality on the FDH models: New insight on the proportionality axiom. International Journal of Information and Decision Sciences, 7, 99–114.
Agrell, P. J., Bogetoft, P., Brock, M., & Tind, J. (2005). Efficiency evaluation with convex pairs. Advanced Modeling and Optimization, 7, 211–237.
Alirezaee, M., Hajinezhad, E., & Paradi, J. C. (2018). Objective identification of technological returns to scale for data envelopment analysis models. European Journal of Operational Research, 266, 678–688.
Banker, R. D., Charnes, A., & Cooper, W. W. (1984). Some models for estimating technical and scale efficiencies in data envelopment analysis. Management Science, 30, 1078–1092.
Banker, R. D., Cooper, W. W., Seiford, L. M., Thrall, R. M., & Zhu, J. (2004). Returns to scale in different DEA models. European Journal of Operational Research, 154, 345–362.
Banker, R. D., Cooper, W. W., Seiford, L. M., & Zhu, J. (2011). Returns to Scale in DEA. In W. Cooper, L. Seiford, & J. Zhu (Eds.), Handbook on data envelopment analysis. International series in operations research & management science (Vol. 164). Boston, MA: Springer.
Bi, G., Luo, Y., Ding, J., & Liang, L. (2015). Environmental performance analysis of Chinese industry from a slacks-based perspective. Annals Of Operations Research, 228, 65–80.
Bogetoft, P. (1996). DEA on relaxed convexity assumptions. Management Science, 42, 457–465.
Bogetoft, P., Tama, J. M., & Tind, J. (2000). Convex input and output projections of nonconvex production possibility sets. Management Science, 46, 858–869.
Briec, W., Kerstens, K., & Vanden, Eeckaut P. (2004). Non-convex technologies and cost functions: Definitions, duality and nonparametric tests of convexity. Journal of Economics, 81, 155–192.
Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2, 429–444.
Chen, C.-M. (2014). Evaluating eco-efficiency with data envelopment analysis: An analytical reexamination. Annals of Operations Research, 214, 49–71.
Cherchye, L., De Rock, B., Dierynck, B., Roodhooft, F., & Sabbe, J. (2013). Opening the black box of efficiency measurement: Input allocation in multi-output settings. Operations Research, 61, 1148–1165.
Cherchye, L., De Rock, B., & Walheer, B. (2015). Multi-output efficiency with good and bad outputs. European Journal of Operational Research, 240, 872–881.
Cherchye, L., De Rock, B., & Walheer, B. (2016). Multi-output profit efficiency and directional distance function. OMEGA, 61, 100–109.
Chung, Y., Färe, R., & Grosskopf, S. (1997). Productivity and undesirable outputs: A directional distance function approach. Journal of Environmental Management, 51, 229–240.
Cook, W. D., Du, J., & Zhu, J. (2017). Units invariant DEA when weight restrictions are present: Ecological performance of US electricity industry. Annals of Operations Research, 255, 323–346.
Cook, W. D., & Seiford, L. M. (2009). Data envelopment analysis (DEA)-thirty years on. European Journal of Operational Research, 192, 1–17.
Cooper, W. W., Seiford, L. M., & Tone, K. (2007). Data envelopment analysis: A comprehensive text with models, applications, references and DEA-solver software (2nd ed.). New York: Springer.
Cooper, W. W., Seiford, L. M., & Zhu, J. (2004). Handbook on data envelopment analysis (Vol. 2). New York: Springer.
Dakpo, K. H., Jeanneaux, P., & Latruffe, L. (2016). Modelling pollution-generating technologies in performance benchmarking: Recent developments, limits and future prospects in the nonparametric framework. European Journal of Operational Research, 250, 347–359.
Debreu, G. (1951). The coefficient of resource utilization. Econometrica, 19(3), 273–292.
Dekker, D., & Post, T. (2001). A quasi-concave DEA model with an application for branch performance evaluation. European Journal of Operational Research, 132, 296–311.
Despic, O., Despic, M., & Paradi, J. (2007). DEA-R: ratio-based comparative efficiency model, its mathematical relation to DEA and its use in applications. Journal of Productivity Analysis, 28, 33–44.
Ding, J., Dong, W., Liang, L., & Zhu, J. (2017). Goal congruence analysis in multi-Division Organizations with shared resources based on data envelopment analysis. European Journal of Operational Research, 263, 961–973.
Du, J., Cook, W. D., Liang, L., & Zhu, J. (2014). Fixed cost and resource allocation based on DEA cross-efficiency. European Journal of Operational Research, 235, 206–214.
Ehrgott, M., & Tind, J. (2009). Column generation with free replicability in DEA. Omega, 37, 943–950.
Färe, R., & Grosskopf, S. (2000). Network DEA. Socio-Economic Planning Sciences, 34, 35–49.
Färe, R., & Grosskopf, S. (2003). Nonparametric productivity analysis with undesirable outputs: Comment. American Journal of Agricultural Economics, 85, 1070–1074.
Färe, R., & Grosskopf, S. (2004). Modeling undesirable factors in efficiency evaluation: Comment. European Journal of Operational Research, 157, 242–245.
Färe, R., & Grosskopf, S. (2009). A comment on weak disposability in nonparametric production analysis. American Journal of Agricultural Economics, 91, 535–538.
Färe, R., Grosskopf, S., & Lovell, C. A. K. (1994). Production frontier. Cambridge: Cambridge University Press.
Färe, R., Grosskopf, S., Lovell, C. A. K., & Pasurka, C. (1989). Multilateral productivity comparisons when some outputs are undesirable: A nonparametric approach. The Review of Economics and Statistics, 71, 90–98.
Färe, R., Grosskopf, S., & Whittaker, G. (2007). Network DEA. In J. Zhu & W. Cook (Eds.), Modeling data irregularities and structural complexities in data envelopment analysis. New York: Springer.
Färe, R., & Primont, D. (1995). Multi-output production and duality: theory and applications. Boston: Kluwer.
Farrell, M. (1957). The measurement of productive efficiency. Journal of the Royal Statistical Society Series 1, General, 120(Part, 3), 253–281.
Forsund, F. R. (2018). Multi-equation modelling of desirable and undesirable outputs satisfying the materials balance. Empirical Economics, 54, 67–69.
Fried, H., Lovell, C. A. K., & Schmidt, S. (2008). The measurement of productive efficiency and productivity change. Oxford: Oxford University Press.
Giannakis, D., Jamasb, T., & Pollitt, M. (2005). Benchmarking and incentive regulation of quality of service: An application to the UK electricity distribution networks. Energy Policy, 33, 2256–2271.
Hailu, A. (2003). Nonparametric productivity analysis with undesirable outputs: Reply. American Journal of Agricultural Economics, 85, 1075–1077.
Hailu, A., & Veeman, T. S. (2001). Non-parametric productivity analysis with undesirable outputs: An application to the Canadian pulp and paper industry. American Journal of Agricultural Economics, 83, 605–616.
Hoff, A. (2007). Second stage DEA: Comparison of approaches for modeling the DEA score. European Journal of Operational Research, 181, 425–435.
Izadikhah, M., & Saen, R. F. (2018, forthcoming). Assessing sustainability of supply chains by chance-constrained two-stage DEA model in the presence of undesirable factors. Computers & Operations Research. https://doi.org/10.1016/j.cor.2017.10.002.
Jamasb, T., & Pollitt, M. (2003). International benchmarking and regulation: An application to European electricity distribution utilities. Energy Policy, 31, 16091622.
Korhonen, P. J., & Syrijanen, M. J. (2003). Evaluation of cost efficiency in finnish electricity distribution. Annals of Operations Research, 121, 105–122.
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, 439–453.
Kuosmanen, T. (2001). DEA with efficiency classification preserving conditional convexity. European Journal of Operational Research, 132, 326–342.
Kuosmanen, T. (2003). Duality theory of non-convex technologies. European Journal of Operational Research, 20, 273–304.
Kuosmanen, T. (2005). Weak disposability in nonparametric production analysis with undesirable outputs. American Journal of Agricultural Economics, 87, 1077–1082.
Kuosmanen, T., & Podinovski, V. (2009). Weak disposability in nonparametric production analysis: Reply to Färe and Grosskopf. American Journal of Agricultural Economics, 91, 539–545.
Li, Y. J., Yang, F., Liang, L., & Hua, Z. S. (2009). Allocating the fixed cost as a complement of other cost inputs: A DEA approach. European Journal of Operational Research, 197, 389–401.
Liu, W., Meng, W., Li, X. X., & Zhang, D. Q. (2010). DEA models with undesirable inputs and outputs. Annals of Operations Research, 173, 177–194.
Liu, W., Zhou, Z., Ma, C., Liu, D., & Shen, W. (2015). Two-stage DEA models with undesirable input-intermediate-outputs. Omega, 56, 74–87.
Lozano, S., & Villa, G. (2010). Gradual technical and scale efficiency improvement in DEA. Annals of Operations Research, 173, 123–136.
Maghbouli, M., Amirteimoori, A., & Kordrostami, S. (2014). Two-stage network structures with undesirable outputs: A DEA based approach. Measurement, 48, 109–118.
McDonald, J. (2009). Using least squares and tobit in second stage DEA efficiency analyses. European Journal of Operational Research, 197, 792–798.
Nehring, K., & Puppe, C. (2004). Modelling cost complementarities in terms of joint production. Journal of Economic Theory, 118, 252–264.
Panzar, J. C., & Willig, R. D. (1977). Economies of scale in multi-output production. Quarterly Journal of Economics, 91, 481–493.
Panzar, J. C., & Willig, R. D. (1981). Economies of scope. American Economic Review, 71(2), 268–272.
Perez-Lopez, G., Prior, D., & Zafra-Gomez, J. L. (2018). Temporal scale efficiency in DEA panel data estimations. An application to the solid waste disposal service in Spain. Omega, 76, 18–27.
Petersen, N. C. (1990). Data envelopment analysis on a relaxed set of assumptions. Management Science, 36, 305–314.
Podinovski, V. V. (2004a). Bridging the gap between the constant and variable returns-to-scale models: Selective proportionality in data envelopment analysis. Journal of the Operational Research Society, 55, 265–276.
Podinovski, V. V. (2004b). On the linearization of reference technologies for testing returns to scale in FDH models. European Journal of Operational Research, 152(3), 800–802.
Podinovski, V. V. (2004c). Local and global returns to scale in performance measurement. Journal of Operational Research Society, 55(2), 170–178.
Podinovski, V. V. (2005). Selective convexity in DEA models. European Journal of Operational Research, 161, 552–563.
Podinovski, V. V. (2009). Production technologies based on combined proportionality assumptions. Journal of Productivity Analysis, 32, 21–26.
Podinovski, V. V. (2018). Returns to scale in convex production technologies. European Journal of Operational Research, 258, 970–982.
Podinovski, V. V., & Husain, W. R. W. (2017). The hybrid returns-to-scale model and its extension by production trade-offs: An application to the efficiency assessment of public universities in Malaysia. Annals of Operations Research, 250, 65–84.
Podinovski, V. V., Ismail, I., Bouzdine-Chameeva, T., & Wenjuan Zhang, W. J. (2014). Combining the assumptions of variable and constant returns to scale in the efficiency evaluation of secondary schools. European Journal of Operational Research, 239, 504–513.
Podinovski, V. V., & Kuosmanen, T. (2011). Modelling weak disposability in data envelopment analysis under relaxed convexity assumptions. European Journal of Operational Research, 211, 577–585.
Podinovski, V. V., Olesen, O. B., & Sarrico, S. C. (2018). Nonparametric production technologies with multiple component processes. Operations Research, 66, 282–300.
Pombo, C., & Taborda, R. (2006). Performance and efficiency in Colombias power distribution system: Effects of the 1994 reform. Energy Economics, 28, 339–369.
Reinhard, S., Lovell, C. A. K., & Thijssen, G. J. (2002). Analysis of Environmental Variation. American Journal of Agricultural Economics, 84, 1054–1065.
Sahoo, B., & Tone, K. (2015). Scale elasticity in non-parametric DEA approach. In J. Zhu (Ed.), Data envelopment analysis. International series in operations research & management science (Vol. 221). Boston, MA: Springer.
Salerian, J., & Chan, C. (2005). Restricting multiple-output multiple-input DEA models by disaggregating the output-input vector. Journal of Productivity Analysis, 24, 5–29.
Sarkis, J., & Cordeiro, J. J. (2012). Ecological modernization in the electrical utility industry: An application of a bads-goods DEA model of ecological and technical efficiency. European Journal of Operational Research, 219, 386–395.
Scheel, H. (2001). Undesirable outputs in efficiency valuations. European Journal of Operational Research, 142, 16–20.
Seiford, L. M., & Zhu, J. (2002). Modeling undesirable factors in efficiency evaluation. European Journal of Operational Research, 132, 400–410.
Shepard, R. W. (1970). Theory of cost and production functions. Princeton: Princeton University Press.
Silva, M. C. A. (2018). Output-specific inputs in DEA: An application to courts of justice in Portugal. Omega, 79, 43–53.
Sueyoshi, T., & Goto, M. (2011). DEA approach for unified efficiency measurement: Assessment of Japanese fossil fuel power generation. Energy Economics, 33, 292–303.
Sueyoshi, T., & Goto, M. (2012). DEA environmental assessment of coal fired power plants: Methodological comparison between radial and non-radial models. Energy Economics, 34, 1854–1863.
Sueyoshi, T., & Goto, M. (2014). Environmental Sustainability development for supply chain management in U.S. petroleum industry by DEA environmental assessment. Energy Economics, 46, 360–374.
Tone, K. (2011). On returns to scale under weight restrictions in data envelopment analysis. Journal of Productivity Analysis, 16, 31–47.
Tone, K., & Sahoo, B. S. (2006). Re-examining scale elasticity in DEA. Annals of Operations Research, 145, 69–87.
Tone, K., & Tsutsui, M. (2007). Decomposition of cost efficiency and its application to Japanese-US electric utility comparisons. Socio-Economic Planning Sciences, 41, 91–106.
Tone, K., & Tsutsui, M. (2009). Network DEA: A slacks-based measure approach. European Journal of Operational Research, 197, 243–252.
Tone, K., & Tsutsui, M. (2011). Applying an efficiency measure of desirable and undesirable outputs in DEA to U.S. electric utilities. Journal of Centrum Cathedra, 4(2), 236–249.
Tulkens, H. (1993). On FDH analysis: Some methodological issues and applications to retail banking, courts, and urban transit. Journal of Productivity Analysis, 4, 183–210.
Walheer, B. (2016a). A multi-sector nonparametric production-frontier analysis of the economic growth and the convergence of the European countries. Pacific Economic Review, 21(4), 498–524.
Walheer, B. (2016b). Growth and convergence of the OECD countries: A multi-sector production-frontier approach. European Journal of Operational Research, 252, 665–675.
Walheer, B. (2018a). Aggregation of metafrontier technology gap ratios: The case of European sectors in 1995–2015. European Journal of Operational Research, 269, 1013–1026.
Walheer, B. (2018b). Cost Malmquist productivity index: An output-specific approach for group comparison. Journal of Productivity Analysis, 49(1), 79–94.
Walheer, B. (2018c). Disaggregation of the Cost Malmquist Productivity Index with joint and output-specific inputs. Omega, 75, 1–12.
Walheer, B. (2018d). Malmquist productivity index for multi-output producers: An application to electricity generation plants. Socio-Economic Planning Sciences. 10.1016/j.seps.2018.02.003.
Walheer, B. (2018e). Scale efficiency for multi-output cost minimizing producers: The case of the US electricity plants. Energy Economics, 70, 26–36.
Walheer, B. (2018f). Scale, congestion, and technical efficiency of European countries: A sector-based nonparametric approach. Empirical Economics. 10.1007/s00181-018-1426-7.
Walheer, B., & Zhang, L.-J. (2018). Profit Luenberger and Malmquist–Luenberger indexes for multi-activity decision making units: The case of the star-rated hotel industry in China. Tourism Management, 69, 1–11.
Yu, M.-M., Chern, C.-C., & Hsiao, B. (2013). Human resource rightsizing using centralized data envelopment analysis: Evidence from Taiwans airports. Omega, 41(1), 119–130.
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, 189, 1–18.
