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Environmental externalities and regulation constrained cost productivity growth in the US electric utility industry

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Abstract

This paper examines whether having to comply with Phase 1 of Title IV of the 1990 Clean Air Act, and rate of return regulation, each impacted the rate of total factor productivity (TFP) growth when accounting for the production of good and bad outputs. Phase 1, effective from 1995 to 1999, requires electric utilities to reduce their emissions of sulfur dioxide and nitrogen oxide (bad outputs). Actions undertaken to reduce the emissions (using less sulfur content coal, installing equipment), may have led to higher production costs, and impacted the rate of TFP growth. Rate regulation may impact how the firm produces its selected output level, which could lead to higher cost over time, and biased estimates of TFP growth. Following the work of Ball et al. (Struct Change Econ Dyn 16(3): 374–394, 2005), who developed the standard Malmquist cost productivity (MCP) index, we develop a MCP index for a rate regulated firm (RMCP index) then use the standard and regulated indices to determine whether having to comply with Phase 1 impacted TFP growth. Empirical results indicate that (i) the RMCP index underestimated the rate at which TFP growth occurred, (ii) Phase 1 utilities on average experienced positive TFP growth from 1996 to 2000 (Phase 1 firms experienced higher TFP growth rates than the rates experienced by firms not subject to Phase 1), and operated more allocatively inefficient in complying with the Phase 1 restrictions. Complying with Phase 1 did not affect the rate at which technical change occurred or the rates of change in scale efficiency.

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Notes

  1. Title IV, enacted in two phases, called for a 2 million ton reduction in nitrogen oxide (NOx) emissions by 2000, and a 10 million ton reduction in sulfur dioxide (SO2) emissions below the SO2 emissions levels in the 1980s. Phase 2, which started in 2000, requires approximately 2000 power plants to reduce their sulfur dioxide emissions. The goal of Title IV is to reduce annual sulfur dioxide emissions from approximately 19 million tons to a cap of 9.85 million tons (cap goes into effect in 2010).

  2. From 1990 to 2000, electric utilities (i) installed 39 additional scrubbers to reduce SO2 emissions, and (ii) used lower sulfur coal (lower average sulfur percent (weight) of coal) to produce electric power. Statistics on the numbers of additional scrubbers installed comes from the 2003 Edition of the Electric Power Annual, which is published by the Energy Information Administration.

  3. We are unaware of the development of a rate of return regulated Malmquist Cost or Luenberguer Productivity index.

  4. Inputs that could be used to produce the good output may have to be used to reduce or eliminate production of the bad output, in order to comply with environmental regulations.

  5. In the empirical application we exclude the output price as a variable in the linear programming problem because the regulated firm only observes a point on the demand curve for the good output (output price set by the regulator), not the entire demand curve.

  6. In setting the allowed rate of return, the regulators (FERC in this case) essentially determine the firm’s allowed user cost of capital. The variable θ can be thought of as the difference between the firm’s allowed and user cost of capital.

  7. A value of REGCH < 1 implies that cost inefficiency caused by the regulatory constraint is larger in period t + 1 than in period t. A value of AECH < 1 suggests that the firm is less allocatively efficient in period t + 1 than in period t. A value of SCH < 1 implies that the size of the firm under evaluation is farther away from its optimal size in period t + 1 than in period t. A value of EFFCH < 1 suggests that the firm is less technically efficient in period t + 1 than in period t.

  8. The use of constant returns to scale technology to compute some components of RMCPt,t+1 (regulation change, allocative efficiency, technical change), variable returns to scale technology to compute other components (efficiency change), and scale change based on both technologies, as benchmarks, is a similar approach used by Färe et al. (1994a, b) in decomposing the Malmquist index into technical change and efficiency change components. Ray and Desli (1997), in commenting on the use of different technologies to compute the Malmquist index, proposed a decomposition using variable returns to scale technology as the benchmark (the scale efficiency change component in Ray and Desli’s (1997) decomposition differs from the scale efficiency change component in Färe et al. (1994a, b). Färe et al. (1997) offered a reply to Ray and Desli (1997).

  9. The data sample ends in 2000 because starting in 2002, electric utilities were no longer required to report the number of electric department employees. Also, there was insufficient data on sulfur dioxide and nitrogen oxide emissions to include 2001 in the data sample. The data sample starts in 1992 because of the difficulty of obtaining firm FERC Form 1 reports before 1992. Further, in the 1990s, there were several electric utilities that (i) divested of their generation equipment, or (ii) merged with other firms. We excluded utilities where (1) we could not obtain sufficient data over the sample period, (2) the firms were involved in mergers, and (3) divested of their generation equipment.

  10. Emissions data from energy consumption for electricity production and useful thermal output at combined-heat-and-power plants shows that for almost every year from 1991 to 2004, the quantity of sulfur dioxide emissions was more than double the quantity of nitrogen oxide emissions. From 1993 through 1995, the quantity of sulfur dioxide emissions was between 1.5 and 2.0 times the quantity of nitrogen oxide emissions (data source: 2002 and 2003 editions of the Electric Power Annual, which is published by the Energy Information Administration).

  11. The class life for electric utility steam production plant, under the Modified Accelerated Cost Recovery System, is 28 years. Using the straight-line depreciation method, the rate of depreciation of the production plant would be approximately 4 % (1/28). Data on the class life of the plant is obtained from the U.S. Master Depreciation Guide (published by Commerce Clearing House 1998).

  12. Using the decimal equivalent, the mean value of the allowed rate of return for the data sample is 0.0979 (9.79 %), with a standard deviation of 0.0121. The minimum and maximum values over the data sample are 0.0659 (minimum value) and 0.1295 (maximum value). The mean value of the allowed rate by year ranges 0.1035–0.0942, where the standard deviation per year is typically around 0.011. The mean value by firm ranges from 0.0737 to 0.1295.

  13. An anonymous referee provided us with the following recommendations in estimating TFP growth for electric utilities that were not subject to the Phase 1 restrictions: (1) compute TFP growth imposing strong disposability of bad outputs, or (2) compute TFP growth without including production of the bad outputs (essentially dropping the restrictions regarding bad outputs. The rank correlations obtained when comparing the estimates of RMCP and MCP when considering bad outputs as weakly disposable (for Phase 1 firms) to their corresponding estimates of RMCP and MCP when considering bad outputs as strongly disposable (for firms not subject to Phase 1) is 0.9776 for RMCP, and 0.9590 for MCP respectively. The rank correlations obtained when comparing the estimates of RMCP and MCP when considering the bad outputs as weakly disposable (for Phase 1 firms) to their corresponding estimates when not considering the bad outputs (for firms not subject to Phase 1) are 0.7364 for RMCP and 0.7701 for MCP. Following Picazo and Prior (2009), in the development of the empirical part we decided not to consider results coming from the assumption of strong disposability of bad outputs. The main reason for this decision is that although there is the production of bad outputs, the lack of environmental rules could drive managers not to be concerned about environmental issues and no effort to reduce pollutants could be deployed. A detailed discussion of this specific situation, and its implications, is presented in Picazo and Prior (2009). Another advantage in favor of not considering bad outputs for firms not subject to Phase 1 is that there were no infeasible solutions when using this specification (there were 9 cases of infeasible solutions when imposing strong disposability of bad outputs).

  14. If restrictions on the bad output are not imposed, when estimating the productivity indices and their components, the larger source of scale inefficiency still comes from inefficient output production in the region of decreasing returns to scale.

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Acknowledgments

We are grateful to Jennifer Shand and Sean Delehunt for their assistance in compiling the data, and to participants at the 10th European Workshop on Efficiency and Productivity Analysis, in Lille (France) for their comments and suggestions. Diego Prior acknowledges the financial support of the Spanish Department of Science and Technology (Plan Nacional de Investigación Científica, Desarrollo e Innovación Tecnológica 2008-2011, Code: ECP2010-18967).

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Correspondence to Gerald Granderson.

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Appendix

See Fig. 1.

Fig. 1
figure 1

Probability distribution functions for RMCP and MCP indices

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Granderson, G., Prior, D. Environmental externalities and regulation constrained cost productivity growth in the US electric utility industry. J Prod Anal 39, 243–257 (2013). https://doi.org/10.1007/s11123-012-0301-3

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