Eco-efficiency considering NetZero and Data Envelopment Analysis: A critical literature review

Emrouznejad, A., M. Marra, G. L. Yang, M. Michali (2023) Eco-efficiency considering NetZero and Data Envelopment Analysis: A critical literature review, IMA Journal of Management Mathematics, https://doi.org/10.1093/imaman/dpad002.

 

List of papers on Data Envelopment Analysis for CO2 reduction (NetZero)

  • Ali, A., & Seiford, L.M. (1990). Translation invariance in data envelopment analysis. Operations Research Letter 10, 403-405.
  • Alizadeh, R., Behiragh, R., Soltanisehat, L., Soltanzadeh, E., Lund, P., (2020). Performance evaluation of complex electricity generation systems: A dynamic network-based data envelopment analysis approach. Energy Economics, 91.
  • Allen, R., Athanassopoulos, A., Dyson, R. G., & Thanassoulis, E. (1997). Weights restrictions and value judgements in Data Envelopment Analysis: Evolution, development and future directions. Annals of Operations Research 73(1), 13-34.
  • An, Q., Wu, Q., Li, J., Xiong, B., Chen, X., (2019) Environmental efficiency evaluation for Xiangjiang River basin cities based on an improved SBM model and Global Malmquist index. Energy Economics, 81, 95-103.
  • Aparicio, J., Pastor, J.T., & Zofio, J. L. (2013). On the inconsistency of the Malmquist–Luenberger index. European Journal of Operational Research 229(3), 738-742.
  • 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.
  • Arabi, B., Munisamy, S., Emrouznejad, A., & Shadman, F. (2014). Power industry restructuring and eco-efficiency changes: A new slacks-based model in Malmquist-Luenberger Index measurement. Energy Policy, 68, 132-145.
  • Azadi, M., R, Kazemi Matin, A. Emrouznejad, and W. Ho (2022) Evaluating Sustainably Resilient Supply Chains: A Stochastic Double Frontier Analytic Model Considering NetZero. Annals of Operations Research, https://doi.org/10.1007/s10479-022-04813-1.
  • Bampatsou, C., Halkos, G., & Beneki, C. (2021). Energy and material flow management to improve EU productivity. Economic Analysis and Policy, 70, 83-93.
  • Banker, R. D. (1984). Estimating the Most Productive Scale Size using Data Envelopment Analysis. European Journal of Operational Research, 17(1), 35-44.
  • 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.
  • Banker, R. D., & Maindiratta, A. (1986). Piecewise Loglinear Estimation of Efficient Production Surfaces. Management Science, 32(1), 126-135.
  • Banker, R. D., & Morey, R. C. (1986a). Efficiency analysis for exogenously fixed inputs and outputs. Operations Research, 34(4), 513-521.
  • Banker, R. D., & Morey, R. C. (1986b). The use of categorical variables in data envelopment analysis. Management Science, 32(12), 1613-1627.
  • Bolin, B., Döös, B.R., Jäger, J., & Warrick, R.A. (editors) (1986). The greenhouse effect, climatic change, and ecosystems. SCOPE 29. Chichester: John Wiley.
  • Boyd, G. A, Tolley, G., & Pang, J. (2002). Plant level productivity, efficiency, and environmental performance of the container glass industry. Environmental Resource Economics, 23(1), 29–43.
  • Boussemart, J.P, Leleu, H., Shen, Z., (2017) Worldwide carbon shadow prices during 1990–2011. Energy Policy, 288-296.
  • Calero-Medina, C., & Noyons, E.C.M. (2008). Combining mapping and citation network analysis for a better understanding of the scientific development: The case of the absorptive capacity field. Journal of Informetrics, 2(4), 272–279.
  • Caves, D. W., Christensen L. R. & Diewert, W. E. (1982). The Economic Theory of Index Numbers and the Measurement of Input, Output, and Productivity. Econometrica, 50 (6), 1393–1414.
  • Chambers, R., Chung, Y., & Färe, R. (1996). Benefit and distance functions. Journal of Economic Theory 70, 407–419.
  • Chang, T. P., & Hu J. L. (2010). Total-factor energy productivity growth, technical progress, and efficiency change: An empirical study of China. Applied Energy, 87(10), 3262-3270.
  • Charnes, A., Cooper, W. W., & Rhodes, E. L. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2(6), 429-444.
  • Charnes, A., Cooper, W. W., Wei, Q. L., & Huang, Z. M. (1989). Cone ratio Data Envelopment Analysis and multi-objective programming. International Journal of Systems Science, 20(7), 1099-1118.
  • Charnes, A., Cooper, W. W., Wei, Q. L., & Huang, Z. M. (1990). Fundamental theorems of non-dominated solutions associated with cones in normed linear spaces. Journal of Mathematical Analysis and Applications, 150(1), 54-78.
  • Chen, Y., Liang, L., & Zhu, J. (2009). Equivalence in two-stage DEA approaches. European Journal of Operational Research, 193(2), 600-604.
  • Chen, P. C., Yu, M. M., Chang, C. C., Hsu, S. H., & Managi, S. (2015). The enhanced Russell-based directional distance measure with undesirable outputs: Numerical example considering CO2 emissions. Omega, 53(1), 30-40.
  • Chiu, C. R., Liou, J. L., Wu, P. I., Fang, C. L., (2012). Decomposition of the environmental inefficiency of the meta-frontier with undesirable output. Energy Economics, 34(5), 1392–1399.
  • 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.
  • Cook, W. D., Kress, M., & Seiford, L. M. (1993). On the use of ordinal data in data envelopment analysis. Journal of the Operational Research Society, 44(2), 133-140.
  • Cook, W. D., Kress, M., & Seiford, L. M. (1996). Data Envelopment Analysis in the presence of both quantitative and qualitative factors. Journal of the Operational Research Society, 47(7), 945-953.
  • Cook, W. D., & Seiford, L. M. (2009). Data envelopment analysis (DEA)-Thirty years on. European Journal of Operational Research, 192(1), 1-17.
  • Cook, W. D., & Zhu, J. (2006). Rank order data in DEA: A general framework. European Journal of Operational Research, 174(2), 1021-1038.
  • Cook, W. D., & Zhu, J. (2007). Classifying inputs and outputs in data envelopment analysis. European Journal of Operational Research, 180(2), 692-699.
  • Cook, W. D., & Zhu, J. (2008). CAR-DEA: Context dependent assurance regions in DEA. Operations Research, 56(1), 69-78.
  • Cook, W. D., Zhu, J., Bi, G. B., & Yang F. (2010). Network DEA: Additive efficiency decomposition. European Journal of Operational Research, 207(2), 1122-1129.
  • Cooper, W. W., Huang, Z., Li, S., & Olesen, O. B. (1998). Chance constrained programming formulations for stochastic characterizations of efficiency and dominance in DEA. Journal of Productivity Analysis, 9(1), 53-79.
  • Cooper, W. W., Huang, Z., & Li, S. (1996). Satisficing DEA models under chance constraints. Annals of Operations Research, 66(4), 279-295.
  • Cooper, W. W., Huang, Z., & Li, S. (2004). Chance constraint DEA. In: Cooper, W.W., Seiford, L.M., Zhu, J. (Eds.), Handbook on Data Envelopment Analysis. Norwell, MA: Kluwer Academic Publishers.
  • Cooper, W. W., Park, K. S., & Pastor, J. T. (1999). RAM: A Range Adjusted Measure of Inefficiency for Use with Additive Models, and Relations to Other Models and Measures in DEA. Journal of Productivity Analysis, 11(1), 5–42.
  • Cooper, W. W., Pastor, J. T., Borras, F., Aparicio, J., & Pastor, D. (2011). BAM: a bounded adjusted measure of efficiency for use with bounded additive models. Journal of Productivity Analysis, 35(2), 85-94.
  • Cooper, W. W., Seiford, L. M., & Tone, K. (2000). Data Envelopment Analysis: A Comprehensive Text with Models, Applications, References and DEA-Solver Software. Boston: Kluwer Acaemic.
  • Cooper, W. W., Seiford, L. M., & Tone, K. (2006). Introduction to Data Envelopment Analysis and its Uses. NewYork: Springer Science and Business Media.
  • Cooper, W.W., Seiford, L.M., &Tone, K. (2007). Data Envelopment Analysis: A Comprehensive Text with Models, Applications, References and DEA-Solver Software (Second Edition). New York: Springer.
  • Cooper, W., Seiford, L. & Zhu, J. (2011). Data envelopment analysis: History, models, and interpretations, in W.W. Cooper, L. Seiford & J. Zhu, eds, ‘Handbook on Data Envelopment Analysis. International Series in Operations Research & Management Science’, Vol. 164, Springer, Boston, MA.
  • Deprins, D., Simar, L., & Tulkens, H. (1984). Measuring Labor Efficiency in Post Offices. In M. Marchand, P. Pestieau and H. Tulkens (eds.) The Performance of Public Enterprises: Concepts and Measurement. Amsterdam: North Holland, 243-267.
  • Demiral, E. E., & Saglam, U. (2021). Eco-efficiency and Eco-productivity assessments of the states in the United States: A two-stage Non-parametric analysis. Applied Energy, 303, 117649.
  • Despotis, D. K., & Smirlis, Y. G. (2002). Data envelopment analysis with imprecise data. European Journal of Operational Research, 140(1), 24-36.
  • Despotis, D. K. (2005a). Measuring human development via data envelopment analysis: the case of Asia and the Pacific. Omega, 33(5), 385-390.
  • Despotis, D. K. (2005b). A reassessment of the human development index via data envelopment analysis. Journal of the Operational Research Society 56(8), 969-980.
  • Ding, Y., Liu, X., Guo, C., & Cronin, B. (2013). The distribution of references across texts: Some implications for citation analysis. Journal of Informetrics, 7(3), 583–592.
  • Doyle, J., & Green, R. (1994). Efficiency and cross-efficiency in DEA: Derivations, meanings and uses. Journal of the Operations Research Society, 45(5), 567-578.
  • Dyckhoff, H., & Allen, K. (2001). Measuring ecological efficiency with data envelopment analysis (DEA). European Journal of Operational Research, 132(2), 312–325.
  • Dyson, R. G., & Thanassoulis, E. (1998). Reducing weight flexibility in Data Envelopment Analysis. Journal of the Operational Research Society, 39(6), 563-576.
  • Du, M., Wang, B., & Wu, Y.R. (2014). Sources of China’s Economic Growth: An Empirical Analysis Based on the BML Index with Green Growth Accounting. Sustainability, 6(9): 5983-6004.
  • Emrouznejad, A., & Marra, M. (2014). Ordered weighted averaging operators 1988-2014: A citation-based literature survey. International Journal of Intelligent Systems, 29(11), 994–1014.
  • Emrouznejad, A., & Tavana, M. (2014). Performance Measurement with Fuzzy Data Envelopment Analysis. In the series of “Studies in Fuzziness and Soft Computing”, Springer-Verlag, ISBN 978-3-642-41371-1.
  • Emrouznejad, A., Yang, G., & Amin, G. (2019). A novel inverse DEA model with application to allocate the CO2 emissions quota to different regions in Chinese manufacturing industries. Journal of Operational Research Society, 70(7), 1079-1090.
  • Fan, M., Shao, S., & Yang, L.L. (2015). Combining global Malmquist-Luenberger index and generalized method of moments to investigate industrial total factor CO2 emission performance: A case of Shanghai (China). Energy Policy, 79: 189-201.
  • Färe, R., Grosskopf, S., Lindgren, B., & Roos, P. (1992). Productivity Changes in Swedish Pharmacies 1980–1989: A Nonparametric Malmquist Approach. Journal of Productivity Analysis, 3 (1/2), 85– 101.
  • Färe, R., Grosskopf, S., Lovell, C. A. K., & Pasurka, C. (1989). Multilateral productivity comparisons when some outputs are undesirable: a nonparametric approach. Review of Economic and Statistics, 71(1), 90–98.
  • Färe, R., Grosskopf, S., & Lovell, C. A. K. (1985). The Measurement of Efficiency of Production. Boston: Kluwer Nijhoff. Publishing Co.
  • Färe, R., Grosskopf, S., Lovell, C. A. K., & Yaisawarng, S. (1993). Derivation of Shadow Prices for Undesirable Outputs: A Distance Function Approach. Review of Economic and Statistics, 75(2), 374–380.
  • Färe, R., Grosskopf, S., & Lovell, C. A. K. (1994). Production Frontiers. Cambridge: Cambridge University Press.
  • Färe, R., & Grosskopf, S. (2000). Network DEA. Socio-Economic Planning Sciences, 34(1), 35-49.
  • Färe, R., & Grosskopf, S. (2004). Modeling undesirable factors in efficiency evaluation: Comment. European Journal of Operational Research, 157(1), 242-245.
  • Färe, R., & Grosskopf, S. (2010). Directional distance functions and slacks-based measures of efficiency: Some clarifications. European Journal of Operational Research, 206(3), 702-722.
  • Färe, R., Grosskopf, S., & Pasurka, Jr., C. A. (2001). Accounting for air pollution emissions in measures of state manufacturing productivity growth. Journal of Regional Science, 41(3), 381–409.
  • Färe, R., Grosskopf, S., Noh, D. W., & Weber, W. (2005). Characteristics of a polluting technology: Theory and practice. Journal of Econometrics, 126(2), 469–492.
  • Färe, R., Grosskopf, S., & Pasurka, C. A. (2007). Environmental production functions and environmental directional distance functions. Energy, 32(7), 1055-1066.
  • Farrell, M. J. (1957). The Measurement of Productive Efficiency. Journal of the Royal Statistical Society, Series A (General), 120(3), 253-290.
  • Feng, C., Chu, F., Ding, J., Bi, G., Liang, L. (2015). Carbon emission abatement (CEA) allocation and compensation schemes based on DEA. Omega, 53, 78-89.
  • Fukuyama, H., & Weber, W. L. (2009). A directional slacks-based measure of technical inefficiency. Socioeconomic Planning Science, 43(4), 274–287.
  • Gai, Y. X., Qiao, Y. B., Deng, H. J., & Wang, Y. T. (2022). Investigating the eco-efficiency of China’s textile industry based on a firm-level analysis. Science of The Total Environment, 833, 155075.
  • Garfield, E., & Sher, I. H. (1963). New factors in the evaluation of scientific literature through citation indexing. American Documentation, 14, 195-201.
  • Golany, G., & Roll, M. (1989). An application procedure for DEA. Omega, 17, 237-250.
  • Goto, M., Otsuka, A., & Sueyoshi, T. (2014). DEA (Data Envelopment Analysis) assessment of operational and environmental efficiencies on Japanese regional industries. Energy, 66, 535-549.
  • Halkos, G. E., & Tzeremes, N. G. (2013). A conditional directional distance function approach for measuring regional environmental efficiency: Evidence from UK regions. European Journal of Operational Research, 227(1), 182–189.
  • He, F., Zhang, Q., Lei, J., Fu, W.H., & Xu, X.N. (2013). Energy efficiency and productivity change of China’s iron and steel industry: Accounting for undesirable outputs. Energy Policy, 54, 204-213.
  • Henriques, C. O., Gouveia, C. M., Tenente, M., & da Silva, P. P. (2022). Employing Value-Based DEA in the eco-efficiency assessment of the electricity sector. Economic Analysis and Policy, 73, 826-844.
  • Hsieh, L. F., & Lin, L. H. (2010). A performance evaluation model for international tourist hotels in Taiwan-An application of the relational network DEA. International Journal of Hospitality Management, 29(1), 14-24.
  • Hua, Z., & Bin, Y. (2007). DEA with undesirable factors. In: Zhu, J., Cook, W.D.(Eds.). Modeling Data Irregularities and Structural Complexities in Data Envelopment Analysis. Springer Science Series (Chapter 6).
  • Huang, J. P., Poh, K. L., & Ang, B. W. (1995). Decision analysis in energy and environmental modeling. Energy, 20(9), 843–855.
  • Jabbour, C. J. C. (2013). Environmental training in organisations: From a literature review to a framework for future research. Resources Conservation and Recycling, 74(1), 144–155.
  • Jebaraj, S., & Iniyan, S. (2006). A review of energy models. Renew. Sustain. Energy Rev., 10, 281–311.
  • Kaneko, S., & Fujii, H. (2010). Financial allocation strategy for the regional pollution abatement cost of reducing sulfur dioxide emissions in the thermal power sector in China. Energy Policy, 38(5): 2131-2141.
  • Kao, C., & Huang, S. N. (2008). Efficiency decomposition in two-stage data envelopment analysis: An application to non-life insurance companies in Taiwan. European Journal of Operational Research, 185(1), 418-429.
  • Kazemi, A., D. U. A Galagedera (2023) , An inverse DEA model for intermediate and output target setting in serially linked general two-stage processes, IMA Journal of Management Mathematics, , https://doi.org/10.1093/imaman/dpab041.
  • Konar, S., & Cohen, M. (1997). Information as Regulation: The effect of Community Right to Know Laws on Toxic Emissions, Journal of Environmental Economics and Management, 32, 109-124.
  • Koopmans, T. C. (1951). Analysis of production as an efficient combination of activities. In: Koopmans, T.C. (Ed.), Activity Analysis of Production and Allocation. New York: Wiley, 33-97.
  • Krautzberger, L., & Wetzel, H. (2012). Transport and CO2: Productivity Growth and Carbon Dioxide Emissions in the European Commercial Transport Industry. Environmental Resource Economics, 53(3), 435–454.
  • Koopmans, T.C. (1951). Analysis of production as an efficient combination of activities. In T.C. Koopmans (Ed.), Activity analysis of production and allocation, Cowles Commission (pp.33-97). New York: Wiley.
  • Kumar S. (2006). Environmentally sensitive productivity growth: A global analysis using Malmquist-Luenberger index. Ecological Economics, 56(2), 280–293.
  • 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), 1580-92.
  • Kumar, A., Mangla Kumar S. & Kumar, P. (2022) An integrated literature review on sustainable food supply chains: Exploring research themes and future directions. Science of the Total Environment, 821.
  • Lage Junior, M., & Godinho Filho, M. (2010). Variations of the kanban system: Literature review and classification. International Journal of Production Economics, 125(1), 13–21.
  • Lall, P., Featherstone, A.M., & Norman, D.W. (2002). Productivity growth in the Western Hemisphere (1978-1994): the Caribbean in perspective. Journal of Productivity Analysis, 17, 213-231.
  • Lampe, H. W., & Hilgers, D. (2014). Trajectories of efficiency measurement: A bibliometric analysis of DEA and SFA. European Journal Operational Research, 240(1), 1–21.
  • Land, K. C., Lovell, C. A. K., & Thore, S. (1992). Productive efficiency under capitalism and state socialism: The chance constrained programming approach. Public Finance in a World of Transition 47(s): 109-121.
  • Land, K. C., Lovell, C. A. K., & Thore, S. (1994). Production efficiency under capitalism and state socialism: An empirical inquiry using chance-constrained data envelopment analysis. Technological Forecasting and Social Change, 46(2): 139-152.
  • Lee, J. D., Baek, C., Kim, H. S., & Lee, J. S. (2014). Development pattern of the DEA research field: a social network analysis approach. Journal of Productivity Analysis, 41(1), 175–186.
  • Lee, J. D., Park, J. B., & Kim, T. Y. (2002). Estimation of the shadow prices of pollutants with production/environment inefficiency taken into account: a nonparametric directional distance function approach. Journal of Environmental Management, 64, 365–375.
  • Lee, J. D., Baek, C., Kim, H. S., & Lee, J. S. (2014). Development pattern of the DEA research field: a social network analysis approach. Journal of Productivity Analysis, 41(2), 175–186.
  • Liang, L., Wu, J., Cook, W. D., & Zhu, J. (2008). The DEA game cross efficiency model and its Nash equilibrium. Operations Research, 56 (5), 1278-1288.
  • Li, K., & Lin, B. (2015). The improvement gap in energy intensity: Analysis of China’s thirty provincial regions using the improved DEA (data envelopment analysis) model. Energy, 84(1), 589–599.
  • Lin, B., & Du, K. (2015). Energy CO2 emissions performance in China’s regional economies: Do market-oriented reforms matter? Energy Policy, 78(1), 113-124.
  • Liou, J. L., Chiu, C. R., Huang, F. M., & Liu, W. Y. (2015). Analyzing the Relationship between CO2 Emission and Economic Efficiency by a Relaxed Two-Stage DEA Model. Aerosolo and Air Quality Research, 15(2), 694-701.
  • Liu, J. S., Lu, L. Y. Y., & Lu, W. M. (2016). Research fronts in data envelopment analysis. Omega, 58(1), 33–45.
  • Liu, J. S., Lu, L. Y. Y., Lu, W. M., & Lin, B. J. Y. (2013). Data envelopment analysis 1978–2010: A citation-based literature survey. Omega, 41(1), 3–15.
  • Lober, G., & Staat, M. (2010). Integrating categorical variables in Data Envelopment Analysis models: A simple solution technique. European Journal of Operational Research, 202(3), 810-818.
  • Long, X., Zhao, X., & Cheng F. (2015). The comparison analysis of total factor productivity and eco-efficiency in China’s cement manufactures. Energy Policy, 81(1), 61-66.
  • Lovell, C. A. K., Pastor, J. T., & Turner, J. A. (1995). Measuring macroeconomic performance in the OECD: a comparison of European and non-European countries. European Journal of Operational Research, 87, 507–518.
  • Lozano, S., Gutiérrez, E., & Moreno, P. (2013). Network DEA approach to airports performance assessment considering undesirable outputs. Applied Mathematical Modelling, 37(4), 1665-1676.
  • Lozano, S., S. Khezri (2023), A new interval efficiency measure in data envelopment analysis based on efficiency potential, IMA Journal of Management Mathematics, 34 (1): 123–142.
  • Lu, L. Y. Y., & Liu, J. S. (2013). An innovative approach to identify the knowledge diffusion path: the case of resource-based theory. Scientometrics, 94(1), 225–246.
  • Luenberger, D. G. (1992). Benefit functions and duality. Journal of Mathematical Economics, 21(5): 461-481.
  • Luenberger, D. G. (1995). Microeconomic Theory. Boston: McGraw-Hill.
  • Mahlberg, B., & Sahoo, B. K. (2011). Radial and non-radial decompositions of Luenberger productivity indicator with an illustrative application. International Journal of Production economics, 131(2), 721–726.
  • Mahmoudi, R., A. Emrouznejad, H. Khosroshahi, M. Khashei, and P. Rajabi (2019), Performance evaluation of thermal power plants considering CO2 emission: A multistage PCA, Clustering, Game theory and Data Envelopment Analysis, Journal of Cleaner Production, 223 (20): 641-650.
  • Malmquist, S. (1953). Index numbers and indifference surfaces. Trabajos de Estatistica, 4:209-242.
  • Mandal, S. K., & Madheswaran, S. (2010). Environmental efficiency of the Indian cement industry: An interstate analysis. Energy Policy, 38(2), 1108-1118.
  • Mariano, E. B., Sobreiro, V. A., & Rebelatto, D. A. D. N. (2015). Human development and data envelopment analysis: A structured literature review. Omega, 54(1), 33–49.
  • Monastyrenko, E. (2017) Eco-efficiency outcomes of mergers and acquisitions in the European electricity industry. Energy Policy, 107, 258-277.
  • Moutinho, V., & Madaleno, M. (2021). A two-stage DEA model to evaluate the technical eco-efficiency indicator in the EU countries. International Journal of Environmental Research and Public Health, 18(6), 3038.
  • NRC (2010). Advancing the Science of Climate Change. National Research Council. The National Academies Press, Washington, DC, USA.
  • Oh, D. (2010a). A meta frontier approach for measuring an environmentally sensitive productivity growth index. Energy Economics, 32, 146–157.
  • Oh, D. (2010b). A global Malmquist-Luenberger productivity index. Journal of Productivity Analysis, 34(1), 183–197.
  • Oh, D, Heshmati A. (2010). A sequential Malmquist–Luenberger productivity index: Environmentally sensitive productivity growth considering the progressive nature of technology. Energy Economics, 32(6), 1345–1355.
  • Oukil, A. (2023), Investigating prospective gains from mergers in the agricultural sector through Inverse DEA, IMA Journal of Management Mathematics, https://doi.org/10.1093/imaman/dpac004
  • Olesen, O. B., Petersen N C. (1995). Chance constrained efficiency evaluation. Management science, 41(3), 442-457.
  • Pastor, J. T. (1996). Translation invariance in data envelopment analysis. Annals of Operations Research, 66, 93-102.
  • Pastor, J.T., Lovell, C.A.K. (2005). A global Malmquist productivity index. Economic Letter, 88, 266-271.
  • Pastor, J. T., Ruiz, J. L., Sirvent, I. (1999). An enhanced DEA Russell graph efficiency measure. European Journal of Operational Research, 115(3), 596–607.
  • Ramli, N. A., & Munisamy, S. (2015). Eco-efficiency in greenhouse emissions among manufacturing industries: A range adjusted measure. Economic Modelling, 47(1), 219–227.
  • Ray, S.C., & Desli, E. (1997). Productivity Growth, Technical Progress, and Efficiency Change in Industrialized Countries: Comment. American Economic Association, 87(5), 1033-1039.
  • Riccardi, R., Oggioni, G., & Toninelli, R. (2012). Efficiency analysis of world cement industry in presence of undesirable output: Application of data envelopment analysis and directional distance function. Energy Policy, 44(1), 140–152.
  • Roll, Y., Cook, W. D., & Golany B. (1991). Controlling factor weights in Data Envelopment Analysis. IIE Transactions, 23(1), 2-9.
  • Rousseau, J. J., & Semple, J. H. (1995). Two-person ratio efficiency games. Management Science, 41(3), 435-441.
  • Russell, R. R. (1988). On the Axiomatic Approach to the Measurement of Technical Efficiency. In W. Eichorn (ed.), Measurement in Economics. Heidelberg: Physica-Verlag.
  • Russell, R. R. (1990). Continuity of Measures of Technical Efficiency. Journal of Economic Theory, 51(2), 255–267.
  • Scheel, H. (2001). Undesirable outputs in efficiency evaluation. European Journal of Operational Research, 132, 400-410.
  • Seiford, L. M, & Zhu J. (1999). Profitability and marketability of the top 55 U.S. commercial banks. Management Science, 45(9): 1270–1288
  • Seiford, L. M., Zhu, J. (1998). An acceptance system decision rule with Data Envelopment Analysis. Computers and Operations Research, 25(4), 329-332.
  • Seiford, L. M., Zhu, J. (2002). Modeling undesirable factors in efficiency evaluation. European Journal of Operational Research, 142(1), 16-20.
  • Sexton, T. R., Silkman, R. H., & Hogan, A. J. (1986). Data Envelopment Analysis: Critique and extensions. In Silkman, R.H. (Ed.), Measuring efficiency: An assessment of Data Envelopment Analysis. San Francisco, CA: Jossey-Bass.
  • Shephard, R.W., & Färe, R. (1974). The law of diminishing returns. Zeitschrift für Nationalökonomie, 34, 69–90.
  • Syrjanen, M J. (2004). Non-discretionary and discretionary factors and scale in data envelopment analysis. European Journal of Operational Research, 158(1), 20-33.
  • Song, M., An, Q., Zhang, W., Wang, Z., & Wu, J. (2012). Environmental efficiency evaluation based on data envelopment analysis: A review. Renewable and Sustainable Energy Reviews, 16(7), 4465–4469.
  • Sueyoshi, T., & Yuan, Y. (2016). Marginal rate of transformation and rate of substitution measured by DEA environmental assessment: Comparison among European and North American nations. Energy Economics, 56, 270-287.
  • Sueyoshi, T., & Yuan, Y. (2017). Social sustainability measured by intermediate approach for DEA environmental assessment: Chinese regional planning for economic development and pollution prevention. Energy Economics, 66, 154-166.
  • Sueyoshi, T., & Goto, M., (2015) DEA environmental assessment in time horizon: Radial approach for Malmquist index measurement on petroleum companies. Energy Economics, 15, 329-345.
  • Taleb, M., , R. Khalid, A. Emrouznejad, R. Ramli (2022). Environmental efficiency under weak disposability: an improved super efficiency data envelopment analysis model with application for assessment of port operations considering NetZero. Environment, Development and Sustainability, https://doi.org/10.1007/s10668-022-02320-8.
  • Thanassoulis, E. (2001), Introduction to the Theory and Application of Data Envelopment Analysis: A Foundation Text with Integrated Software, NewYork: Springer.
  • Thanassoulis, E., Boussofiane, A., Dyson, R. G. (1996). A comparison of data envelopment analysis and ratio analysis as tools for performance assessment. Omega, 24(3), 229-244.
  • Thompson, R. G., Jr Singleton, F. D., Thrall, R. M., & Smith, B. A. (1986). Comparative site evaluation for locating a high-energy physics lab in Texas. Interfaces, 16(6), 35-49.
  • Thompson, R. G., Langemeier, L. N., Lee, C. T., Lee, E., & Thrall, R. M. (1990). The role of multiplier bounds in efficiency analysis with application to Kansas farming. Journal of Econometrics, 46(1), 93-108.
  • Thore, S. (1987). Chance-constrained activity analysis. European Journal of Operational Research, 30(3), 267-269.
  • Tohidi, G., Razavyan, S., & Tohidnia, S. (2012). A global cost Malmquist productivity index using data envelopment analysis. Journal of the Operational Research Society, 63, 72–78.
  • Tone, K., & Tsutsui, M. (2009). Network DEA: A Slacks-based Measure Approach. European Journal of Operational Research, 197(1), 243-252.
  • Tone, K. (2001). A slacks-based measure of efficiency in data envelopment analysis. European Journal of Operational Research, 130(3), 498-509.
  • Wang, Y. M., & Chin, K. S. (2010). Some alternative DEA models for two-stage process. Expert Systems with Applications, 37 (12), 8799-8808.
  • Wang, K., & Wei, Y. (2014). China’s regional industrial energy efficiency and carbon emissions abatement costs. Applied Energy, 130, 617-631.
  • Wang, Q. W., Zhou, P., Shen, N., & Wang, S. S. (2013). Measuring carbon dioxide emission performance in Chinese provinces: A parametric approach. Renewable and Sustainable Energy Reviews, 21, 324-330.
  • Watanabe, M., & Tanaka, K. (2007). Efficiency analysis of Chinese industry: A directional distance function approach. Energy Policy, 35(12), 6323-6331.
  • Weber, W. L., & Domazlicky, B. (2001). Productivity growth and pollution in state manufacturing. Review of Economic and Statistics, 83(1), 195–199.
  • Xia, B., Dong, S. C., Li, Z. H., Zhao, M. Y., Sun, D. Q., Zhang, W. B., & Li, Y. (2022). Eco-efficiency and its drivers in tourism sectors with respect to carbon emissions from the supply chain: An Integrated EEIO and DEA Approach. International Journal of Environmental Research and Public Health, 19(11), 6951.
  • Xiao, H. J., Wang, D. P., Qi, Y., Shao, S., Zhou, Y., & Shan, Y. L. (2021). The governance-production nexus of eco-efficiency in Chinese resource-based cities: A two-stage network DEA approach. Energy Economics, 101, 105408.
  • Xu, B., & Lin, B. (2015). Factors affecting CO2 emissions in China’s transport sector: a dynamic nonparametric additive regression model. Journal of Cleaner Production, 101, 311–322.
  • Wang, H., Zhou, P., Bai-Chen, X., Zang, N. (2019). Assessing drivers of CO2 emissions in China’s electricity sector: A meta frontier production-theoretical decomposition analysis. European Journal of Operational Research, 275(3), 1096-1107.
  • Wu, F., Fan, L.W., Zhou, P., & Zhou, D.Q. (2012). Industrial energy efficiency with CO2 emissions in China: a nonparametric analysis. Energy Policy, 49(1) 164-172.
  • Yang, G. L., Shen, W. F., Zhang, D. Q., & Liu, W. B. (2014). Extended Utility and DEA Models without Explicit Input. Journal of the Operational Research Society, 65, 1212–1220.
  • Yörük, B. K., & Zaim, O. (2005). Productivity growth in OECD countries: A comparison with Malmquist indices. Journal of Comparative Economics, 33(2), 401–420.
  • Yuan, P., Cheng, S., Sun, J., & Liang, W. (2013). Measuring the environmental efficiency of the Chinese industrial sector: A directional distance function approach. Mathematical and Computer Modelling, 58(5-6), 936–947.
  • Zhang, C. H., Liu, H. Y., Bressers, H. T. A., & Buchanan, K. S. (2011). Productivity growth and environmental regulations – accounting for undesirable outputs: Analysis of China’s thirty provincial regions using the Malmquist-Luenberger index. Ecological Economics, 70(12), 2369-2379.
  • Zhang, N., & Choi, Y. (2014). A note on the evolution of directional distance function and its development in energy and environmental studies 1997–2013. Renew. Sustain. Energy Rev., 33, 50–59.
  • Zhang, N., & Choi, Y. (2013a.) Total-factor carbon emission performance of fossil fuel power plants in China: A meta frontier non-radial Malmquist index analysis. Energy Economics, 40, 549–559.
  • Zhang, N., & Choi, Y. (2013b). A comparative study of dynamic changes in CO2 emission performance of fossil fuel power plants in China and Korea. Energy Policy 62, 324–332.
  • Zhang, N., Kong, F., & Choi, Y. (2014). Measuring sustainability performance for China: A sequential generalized directional distance function approach. Economic Modelling, 41, 392–397.
  • Zhang, N., Zhou, P., & Choi, Y. (2013). Energy efficiency, CO2 emission performance and technology gaps in fossil fuel electricity generation in Korea: A meta-frontier non-radial directional distance function analysis. Energy Policy, 56, 653–662.
  • Zhang, N., Zhou, P., & Kung, C. C. (2015). Total-factor carbon emission performance of the Chinese transportation industry: A bootstrapped non-radial Malmquist index analysis. Renew. Sustain. Energy Rev., 41, 584–593.
  • Zhou, P., Ang, B. W., Han, J. Y. (2010). Total factor carbon emission performance: A Malmquist index analysis. Energy Economics, 32(1) 194–201.
  • 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), 1–18.
  • Zhou, P., Ang, B. W., & Wang, H. (2012). Energy and CO2 emission performance in electricity generation: A non-radial directional distance function approach. European Journal of Operational Research, 221(3), 625–635.
  • Zhu, C, N. Zhu (2023), Assessing the eco-efficiency of industrial investment in China: a DEA approach, IMA Journal of Management Mathematics, 34 (1): 143–163, https://doi.org/10.1093/imaman/dpab044.
  • Zieschang, K. O. (1984). An Extended Farrell Technical Efficiency Measure. Journal of Economic Theory, 33(2), 387–396.