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Measuring Eco-Inefficiency: A New Frontier Approach

Author

Listed:
  • Chien-Ming Chen

    (Nanyang Business School, Nanyang Technological University, Singapore 639798, Republic of Singapore)

  • Magali A. Delmas

    (Institute of the Environment and Sustainability, and Anderson School of Management, University of California, Los Angeles, Los Angeles, California 90095)

Abstract
Growing social concerns over the environmental externalities associated with business activities are pushing firms to identify activities that create economic value with less environmental impact and to become more eco-efficient. Over the past two decades, researchers have increasingly used frontier efficiency models to evaluate productive efficiency in the presence of undesirable outputs, such as greenhouse gas emissions or toxic emissions. In this paper, we identify critical flaws in existing frontier models and show that these models can identify eco-inefficient firms as eco-efficient. We develop a new eco-inefficiency frontier model that rectifies these problems. Our model calculates an eco-inefficiency score for each firm and improvements in outputs necessary to attain eco-efficiency. We demonstrate through a Monte Carlo experiment that our eco-inefficiency model provides a more reliable measurement of corporate eco-inefficiency than the existing frontier models. We also extend the single-output Cobb-Douglas production function to multiple desirable and undesirable outputs. This extension allows for greater flexibility in the simulation analysis of frontier models.

Suggested Citation

  • Chien-Ming Chen & Magali A. Delmas, 2012. "Measuring Eco-Inefficiency: A New Frontier Approach," Operations Research, INFORMS, vol. 60(5), pages 1064-1079, October.
    • Handle: RePEc:inm:oropre:v:60:y:2012:i:5:p:1064-1079
    DOI: 10.1287/opre.1120.1094
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    References listed on IDEAS

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    1. Fernandez C. & Koop G. & Steel M.F.J., 2002. "Multiple-Output Production With Undesirable Outputs: An Application to Nitrogen Surplus in Agriculture," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 432-442, June.
    2. Boaz Golany & Eran Tamir, 1995. "Evaluating Efficiency-Effectiveness-Equality Trade-Offs: A Data Envelopment Analysis Approach," Management Science, INFORMS, vol. 41(7), pages 1172-1184, July.
    3. Adler, Nicole & Yazhemsky, Ekaterina, 2010. "Improving discrimination in data envelopment analysis: PCA-DEA or variable reduction," European Journal of Operational Research, Elsevier, vol. 202(1), pages 273-284, April.
    4. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
    5. Jesús T. Pastor & JosÉ L. Ruiz & Inmaculada Sirvent, 2002. "A Statistical Test for Nested Radial Dea Models," Operations Research, INFORMS, vol. 50(4), pages 728-735, August.
    6. Yun Zhang & Robert Bartels, 1998. "The Effect of Sample Size on the Mean Efficiency in DEA with an Application to Electricity Distribution in Australia, Sweden and New Zealand," Journal of Productivity Analysis, Springer, vol. 9(3), pages 187-204, March.
    7. Léopold Simar & Paul W. Wilson, 1998. "Sensitivity Analysis of Efficiency Scores: How to Bootstrap in Nonparametric Frontier Models," Management Science, INFORMS, vol. 44(1), pages 49-61, January.
    8. William Cooper & Kyung Park & Jesus Pastor, 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, Springer, vol. 11(1), pages 5-42, February.
    9. Thierry Post & Laurens Cherchye & Timo Kuosmanen, 2002. "Nonparametric Efficiency Estimation In Stochastic Environments," Operations Research, INFORMS, vol. 50(4), pages 645-655, August.
    10. Magali A. Delmas & Michael W. Toffel, 2008. "Organizational responses to environmental demands: opening the black box," Strategic Management Journal, Wiley Blackwell, vol. 29(10), pages 1027-1055, October.
    11. O. B. Olesen & N. C. Petersen, 1995. "Chance Constrained Efficiency Evaluation," Management Science, INFORMS, vol. 41(3), pages 442-457, March.
    12. Thierry Post, 2001. "Performance Evaluation in Stochastic Environments Using Mean-Variance Data Envelopment Analysis," Operations Research, INFORMS, vol. 49(2), pages 281-292, April.
    13. Seiford, Lawrence M. & Thrall, Robert M., 1990. "Recent developments in DEA : The mathematical programming approach to frontier analysis," Journal of Econometrics, Elsevier, vol. 46(1-2), pages 7-38.
    14. Andrew King & Michael Lenox, 2002. "Exploring the Locus of Profitable Pollution Reduction," Management Science, INFORMS, vol. 48(2), pages 289-299, February.
    15. Fare, Rolf, et al, 1989. "Multilateral Productivity Comparisons When Some Outputs Are Undesirable: A Nonparametric Approach," The Review of Economics and Statistics, MIT Press, vol. 71(1), pages 90-98, February.
    16. Greene, William, 2005. "Reconsidering heterogeneity in panel data estimators of the stochastic frontier model," Journal of Econometrics, Elsevier, vol. 126(2), pages 269-303, June.
    17. Rajiv D. Banker, 1993. "Maximum Likelihood, Consistency and Data Envelopment Analysis: A Statistical Foundation," Management Science, INFORMS, vol. 39(10), pages 1265-1273, October.
    18. Magali Delmas & Vered Doctori Blass, 2010. "Measuring corporate environmental performance: the trade‐offs of sustainability ratings," Business Strategy and the Environment, Wiley Blackwell, vol. 19(4), pages 245-260, May.
    19. Sahoo, Biresh K. & Luptacik, Mikulas & Mahlberg, Bernhard, 2011. "Alternative measures of environmental technology structure in DEA: An application," European Journal of Operational Research, Elsevier, vol. 215(3), pages 750-762, December.
    20. Atakelty Hailu & Terrence S. Veeman, 2001. "Non-parametric Productivity Analysis with Undesirable Outputs: An Application to the Canadian Pulp and Paper Industry," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 83(3), pages 605-616.
    21. Wade D. Cook & Joe Zhu, 2007. "Data Irregularities And Structural Complexities In Dea," Springer Books, in: Joe Zhu & Wade D. Cook (ed.), Modeling Data Irregularities and Structural Complexities in Data Envelopment Analysis, chapter 0, pages 1-11, Springer.
    22. Gary Koop, 1998. "Carbon dioxide emissions and economic growth: A structural approach," Journal of Applied Statistics, Taylor & Francis Journals, vol. 25(4), pages 489-515.
    23. Gibbins, Jon & Chalmers, Hannah, 2008. "Carbon capture and storage," Energy Policy, Elsevier, vol. 36(12), pages 4317-4322, December.
    24. Banker, Rajiv D. & Gadh, Vandana M. & Gorr, Wilpen L., 1993. "A Monte Carlo comparison of two production frontier estimation methods: Corrected ordinary least squares and data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 67(3), pages 332-343, June.
    25. Magali Delmas & Ivan Montiel, 2009. "Greening the Supply Chain: When Is Customer Pressure Effective?," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 18(1), pages 171-201, March.
    26. Fare, Rolf & Grosskopf, Shawna & Noh, Dong-Woon & Weber, William, 2005. "Characteristics of a polluting technology: theory and practice," Journal of Econometrics, Elsevier, vol. 126(2), pages 469-492, June.
    27. Timo Kuosmanen & Andrew L. Johnson, 2010. "Data Envelopment Analysis as Nonparametric Least-Squares Regression," Operations Research, INFORMS, vol. 58(1), pages 149-160, February.
    28. Rajiv D. Banker & Ram Natarajan, 2008. "Evaluating Contextual Variables Affecting Productivity Using Data Envelopment Analysis," Operations Research, INFORMS, vol. 56(1), pages 48-58, February.
    29. R. D. Banker & A. Charnes & W. W. Cooper, 1984. "Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis," Management Science, INFORMS, vol. 30(9), pages 1078-1092, September.
    30. Indranil Bardhan & William Cooper & Subal Kumbhakar, 1998. "A Simulation Study of Joint Uses of Data Envelopment Analysis and Statistical Regressions for Production Function Estimation and Efficiency Evaluation," Journal of Productivity Analysis, Springer, vol. 9(3), pages 249-278, March.
    31. Seiford, Lawrence M. & Zhu, Joe, 2002. "Modeling undesirable factors in efficiency evaluation," European Journal of Operational Research, Elsevier, vol. 142(1), pages 16-20, October.
    32. Charles J. Corbett & Robert D. Klassen, 2006. "Extending the Horizons: Environmental Excellence as Key to Improving Operations," Manufacturing & Service Operations Management, INFORMS, vol. 8(1), pages 5-22, March.
    33. Tone, Kaoru, 2001. "A slacks-based measure of efficiency in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 130(3), pages 498-509, May.
    34. Zhongsheng Hua & Yiwen Bian, 2007. "DEA with Undesirable Factors," Springer Books, in: Joe Zhu & Wade D. Cook (ed.), Modeling Data Irregularities and Structural Complexities in Data Envelopment Analysis, chapter 0, pages 103-121, Springer.
    35. William W. Cooper & Lawrence M. Seiford & Kaoru Tone, 2007. "Data Envelopment Analysis," Springer Books, Springer, edition 0, number 978-0-387-45283-8, June.
    36. Robert D. Klassen & Curtis P. McLaughlin, 1996. "The Impact of Environmental Management on Firm Performance," Management Science, INFORMS, vol. 42(8), pages 1199-1214, August.
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