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.

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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.
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  • Banker, R. D., & Morey, R. C. (1986b). The use of categorical variables in data envelopment analysis. Management Science, 32(12), 1613-1627.
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  • 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.
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  • 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.
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  • 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.
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  • 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.
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  • 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.
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  • 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.
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  • 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.
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  • 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.
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