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

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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.

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Notes

  1. This approach has become popular in research that integrate the materials balance condition in microeconomic production analysis; see e.g. Coelli et al. (2007) and Lauwers (2009).

  2. End-of-pipe abatement involves production processes that transform uncontrolled byproducts into different byproducts that are of less threat to the environment. One of the issues with end-of-pipe abatement is that it causes additional byproducts, since the abatement process itself is also subordinate to the laws of physics. This issue will not be treated further in the current article; see Pethig (2006) for a discussion.

  3. Recently, some authors have applied Network DEA (Färe and Grosskopf, 2000) to model pollution control activities explicitly (Färe et al. 2013; Hampf, 2013). These studies determine which inputs are allocatable to the production of desirable outputs and pollution control.

  4. The weak disposability model does however not rule out regions of the outputs sets where undesirable outputs can be reduced while simultaneously increasing desirable outputs.

  5. One referee pointed out that the issue of negative shadow prices can also be resolved by assuming that pollutants are freely disposable inputs (or costly disposable outputs). This approach to modeling undesirable outputs is explored by Hailu and Veeman (2001), but has been criticized by Färe and Grosskopf (2003) for being inconsistent with physical laws.

  6. The power plants face regulation on both nitrogen and sulfur emissions. The regulations on sulfur emissions involve emissions trading, while the regulations on nitrogen to a larger extent are based on emission standards. In Rødseth (2011), emissions trading and pollution control are viewed as substitutes that may both be applied to increase the production of desirable outputs without violating legal emission constraints. Since the current paper is concerned with resources allocated to pollution control I choose to focus on nitrogen control, to avoid considerations between pollution control and emissions trading.

  7. The original dataset is collected for the period 2002–2009 and has been updated for 2002–2005. It uses a modified selection criterion, namely that the plants must satisfy Welch and Barnum’s criterion in 2002 and have nonzero consumption of coal and gas in the following years. Consequentially, some of the plants in the sample may violate Welch and Barnum’s criterion in 2005. Historical information does, however, show that the plants in the sample have gas capacities that allow them to satisfy the selection criterion.

  8. The efficiency scores must be multiplied with 100 in order to show percentage change.

  9. Although they are not discussed in this section, the estimated efficiency scores for plants with missing labor data are printed in italics in Table 2.

  10. For comparison, I also execute the tests for all 54 power plants for the model specifications with labor. In this case, no test rejects the null hypotheses for the pollution control model, but the KSM, WILC and MED tests reject the null hypotheses at the 10 percent level for the weak disposability model.

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Acknowledgments

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

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  1. Institute of Transport Economics, Gaustadalléen 21, 0349, Oslo, Norway

    Kenneth Løvold Rødseth

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Rødseth, K.L. Efficiency measurement when producers control pollutants: a non-parametric approach. J Prod Anal 42, 211–223 (2014). https://doi.org/10.1007/s11123-014-0382-2

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