Journal of Productivity Analysis

, Volume 42, Issue 2, pp 211–223 | Cite as

Efficiency measurement when producers control pollutants: a non-parametric approach

Article
First Online:

Abstract

This paper treats efficiency measurement when some outputs are undesirable and producers control pollutants by end-of-pipe or change-in-process abatement. A data envelopment analysis framework that compares producers with similar pollution control efforts is proposed. First, my approach avoids arbitrary disposability assumptions for undesirable outputs. Second, the model is used to evaluate the interplay between pollution control activities and technical efficiency. I compare my approach to the traditional neo-classical production model that does not incorporate undesirable outputs among outputs, and to Färe et al.’s (Rev Econ Stat 71:90–98, 1989, J Econom 126:469–492, 2005) well-known model that incorporates bads. I evaluate the common assumption in the literature on polluting technologies, that inputs are allocatable to pollution control, and apply U.S. electricity data to illustrate my main point: Although my empirical model specifications are in line with the literature on polluting technologies, they rely on inputs that play an insignificant role in controlling nitrogen oxides (NOx) emissions. Consequentially, there are no reasons to expect the efficiency scores of the traditional model to differ from the efficiency scores of the other two models that account for resources employed to pollution control. Statistical tests show that my model, which explicitly takes pollution control efforts into account, produces efficiency scores that are not statistically different from the traditional model’s scores for all model specifications, while Färe et al.’s model produces significantly different results for some model specifications. I conclude that the popular production models that incorporate undesirable outputs may not be applicable to all cases involving polluting production and that more emphasis on appropriate empirical specifications is needed.

Keywords

Data envelopment analysis Directional output distance function Pollution control Allocatable inputs Weak disposability axiom 

JEL Classification

D24 Q52 C61 
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Notes

Acknowledgments

The author thanks two anonymous referees for their helpful comments. The usual disclaimer applies.

References

  1. Ball E, Färe R, Grosskopf S, Zaim O (2005) Accounting for externalities in the measurement of productivity growth: the Malmquist cost productivity measure. Struct Change Econ Dynam 16:374–394CrossRefGoogle Scholar
  2. Barbera AJ, McConnell VD (1990) The impact of environmental regulations on industry productivity: direct and indirect effects. J Environ Econ Manag 18:50–65Google Scholar
  3. Baumgärtner S, Arons JS (2003) Necessity and inefficiency in the generation of waste. J Ind Ecol 7:113–123CrossRefGoogle Scholar
  4. Baumgärtner S, Dyckhoff H, Faber M, Proops J, Schiller J (2001) The concept of joint production and ecological economics. Ecol Econ 36:365–372CrossRefGoogle Scholar
  5. Brännlund R, Lundgren T (2009) Environmental policy without costs? A review of the Porter hypothesis. Int Rev Environ Resour Econ 3:75–117Google Scholar
  6. Brännlund R, Färe R, Grosskopf S (1995) Environmental regulation and profitability: an application to Swedish pulp and paper mills. Environ Resour Econ 6:23–36CrossRefGoogle Scholar
  7. Chung YH, Färe R, Grosskopf S (1997) Productivity and undesirable outputs: a directional distance function approach. J Environ Manag 51:229–240CrossRefGoogle Scholar
  8. Coelli T, Lauwers L, Van Huylenbroeck G (2007) Environmental efficiency measurement and the materials balance condition. J Prod Anal 28:3–12CrossRefGoogle Scholar
  9. Färe R, Grosskopf S (2000) Network DEA. Socio Econ Plan Sci 34:35–49CrossRefGoogle Scholar
  10. Färe R, Grosskopf S (2003) Nonparametric productivity analysis with undesirable outputs: comment. Am J Agric Econ 85:1070–1074CrossRefGoogle Scholar
  11. Färe R, Primont D (1995) Multi-output production and duality: theory and applications. Kluwer Academic, BostonCrossRefGoogle Scholar
  12. Färe R, Grosskopf S, Lovell CAK, Pasurka CA (1989) Multilateral productivity comparisons when some outputs are undesirable: a nonparametric approach. Rev Econ Stat 71:90–98CrossRefGoogle Scholar
  13. Färe R, Grosskopf S, Pasurka CA (2001) Accounting for air pollution emissions in measures of state manufacturing productivity growth. J Regional Sci 41:381–409CrossRefGoogle Scholar
  14. Färe R, Grosskopf S, Noh D-W, Weber WL (2005) Characteristics of a polluting technology: theory and practice. J Econom 126:469–492CrossRefGoogle Scholar
  15. Färe R, Grosskopf S, Weber WL (2006) Shadow prices and pollution costs in U.S. agriculture. Ecol Econ 56:89–103CrossRefGoogle Scholar
  16. Färe R, Grosskopf S, Pasurka CA (2007a) Environmental production functions and environmental directional distance functions. Energy 32:1055–1066CrossRefGoogle Scholar
  17. Färe R, Grosskopf S, Pasurka CA (2007b) Pollution abatement activities and traditional productivity. Ecol Econ 62:673–682CrossRefGoogle Scholar
  18. Färe R, Grosskopf S, Pasurka CA (2013) Joint production of good and bad outputs with a network application. In: Shogren JF (ed) Encyclopedia of energy, natural resources and environmental economics. Elsevier, San DiegoGoogle Scholar
  19. Førsund FR (2009) Good modelling of bad outputs: pollution and multiple-output production. Int Rev Environ Resour Econ 3:1–38Google Scholar
  20. Hailu A, Veeman TS (2001) Non-parametric productivity analysis with undesirable outputs: an application to the Canadian pulp and paper industry. Am J Agric Econ 83:605–616CrossRefGoogle Scholar
  21. Hampf B (2013) Separating environmental efficiency into production and abatement efficiency: a nonparametric model with application to US power plants. J Prod Anal. doi: 10.1007/s11123-013-0357-8
  22. Jaffe AB, Peterson SR, Portney PR, Stavins RN (1995) Environmental regulation and the competitiveness of U.S. manufacturing: what does the evidence tell us? J Econ Lit 33:132–163Google Scholar
  23. Kuosmanen T (2005) Weak disposability in nonparametric production analysis with undesirable outputs. Am J Agric Econ 87:1077–1082CrossRefGoogle Scholar
  24. Kuosmanen T (2009) Data envelopment analysis with missing data. J Oper Res Soc 60:1767–1774CrossRefGoogle Scholar
  25. Lauwers L (2009) Justifying the incorporation of the materials balance principle into frontier-based eco-efficiency models. Ecol Econ 68:1605–1614CrossRefGoogle Scholar
  26. Liu Y, Sumaila UR (2010) Estimating pollution abatement costs of salmon aquaculture: a joint production approach. Land Econ 86:569–584Google Scholar
  27. Murty S (2010) Externalities and fundamental nonconvexities: a reconciliation of approaches to general equilibrium externality modeling and implications for decentralization. J Econ Theory 145:331–353CrossRefGoogle Scholar
  28. Murty S, Robert Russell R, Levkoff SB (2012) On modeling pollution-generating technologies. J Environ Econ Manag 64:117–135Google Scholar
  29. O’Donnell C, Rao DSP, Battese G (2008) Metafrontier frameworks for the study of firm-level efficiencies and technology ratios. Empir Econ 34:231–255CrossRefGoogle Scholar
  30. Palmer K, Oates WE, Portney PR (1995) Tightening environmental standards: the benefit-cost or the no-cost paradigm? J Econ Perspect 9:119–132CrossRefGoogle Scholar
  31. Pasurka CA (2006) Decomposing electric power plant emissions within a joint production framework. Energy Econ 28:26–43CrossRefGoogle Scholar
  32. Pethig R (2006) Non-linear production, abatement, pollution and materials balance reconsidered. J Environ Econ Manag 51:185–204Google Scholar
  33. Picazo-Tadeo AJ, Prior D (2009) Environmental externalities and efficiency measurement. J Environ Manag 90:3332–3339CrossRefGoogle Scholar
  34. Porter M, Van Der Linde C (1995) Toward a new conception of the environment–competitiveness relationship. J Econ Perspect 9:97–118CrossRefGoogle Scholar
  35. Ray SC (1988) Data envelopment analysis, nondiscretionary inputs and efficiency: an alternative interpretation. Socio Econ Plan Sci 22:167–176CrossRefGoogle Scholar
  36. Reig-Martínez E, Picazo-Tadeo AJ, Hernández-Sancho F (2001) The calculation of shadow prices for industrial wastes using distance functions: an analysis for Spanish ceramic pavements firms. Int J Prod Econ 69:277–285CrossRefGoogle Scholar
  37. Rødseth KL (2011) Treatment of undesirable outputs in production analysis: desirable modeling strategies and applications. Thesis, Institute for Economics and Resource Management. Norwegian University of Life Sciences, ÅsGoogle Scholar
  38. Rødseth KL (2013) Capturing the least costly way of reducing pollution: a shadow price approach. Ecol Econ 92:16–24CrossRefGoogle Scholar
  39. Ruggiero J (1996) On the measurement of technical efficiency in the public sector. Eur J Oper Res 90:553–565CrossRefGoogle Scholar
  40. Shadbegian RJ, Gray WB (2005) Pollution abatement expenditures and plant-level productivity: a production function approach. Ecol Econ 54:196–208CrossRefGoogle Scholar
  41. Shephard RW (1970) Theory of cost and production functions. Princeton University Press, PrincetonGoogle Scholar
  42. Shephard RW, Färe R (1974) The law of diminishing returns. Z Nationalokonomie 34:69–90CrossRefGoogle Scholar
  43. Simar L, Wilson PW (2007) Estimation and inference in two-stage, semi-parametric models of production processes. J Econom 136:31–64CrossRefGoogle Scholar
  44. Welch E, Barnum D (2009) Joint environmental and cost efficiency analysis of electricity generation. Ecol Econ 68:2336–2343CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.Institute of Transport EconomicsOsloNorway

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