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Measuring environmental efficiency of industry: a case study of thermal power generation in India

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Abstract

Technical and environmental efficiency of some coal-fired thermal power plants in India is estimated using a methodology that accounts for firm’s efforts to increase the production of good output and reduce pollution with the given resources and technology. The methodology used is directional output distance function. Estimates of firm-specific shadow prices of pollutants (bad outputs), and elasticity of substitution between good and bad outputs are also obtained. The technical and environmental inefficiency of a representative firm is estimated as 0.06 implying that the thermal power generating industry in Andhra Pradesh state of India could increase production of electricity by 6/ while decreasing generation of pollution by 6%. This result shows that there are incentives or win–win opportunities for the firms to voluntarily comply with the environmental regulation. It is found that there is a significant variation in marginal cost of pollution abatement or shadow prices of bad outputs across the firms and an increasing marginal cost of pollution abatement with respect to pollution reduction by the firms. This result calls for the use of economic instruments like pollution taxes instead of command and control regulation used currently in India to reduce air pollution.

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Notes

  1. Null-jointness implies that a firm cannot produce good output in the absence of bad outputs, i.e., \({{\rm if}(y,b)\in P(x)}\) and b = 0 then y = 0.

  2. For the properties of directional output distance function; see Färe et al. (2005).

  3. See Färe et al. (2005).

  4. (i)\({D_b (x,y,b;g)\geq 0;\hbox{(ii)} D_y (x,y,b;g)\leq 0;\hbox{(iii)}D_{yy} (x,y,b;g)\leq 0; \hbox{and} \hbox{(iv)} D_{by} (x,y,b;g)\leq 0}\) .

  5. The LP estimating procedure is adopted in Färe et al. (2001).

  6. However, the stochastic methods have their own disadvantages such as distributional assumptions for the inefficiency and error terms, and the problem of imposing non-linear monotonicity constraints in the estimation process.

  7. For an application of COLS to the Shephard output distance function, see Lovell et al. (1994) and to the directional output distance function, see Färe et al. (2005)

  8. LIMDEP 8.0 version is used in the estimation of directional output distance function.

  9. www.apgenco.com

  10. For the observations that violate the monotonicity conditions, the estimates of directional output distance function are scaling some (those that violate monotonicity) of the observed values of (y,b) back to the frontier along the negatively sloped portion of output set.

  11. The averaging approach is adopted by Coelli and Parelman (1999) in measuring the relative performance of European Railways, by Drake and Simper (2003) in measuring the efficiency of the English and Welsh police force, and by Kumar and Gupta (2004) in measuring the resource use efficiency of US electricity generating plants. Here it should be noted that the averaging is done for the different estimation methods such as parametric linear programming, data evelopment analysis and stochastic estimation. This is the first study which is using the averaging approach for different models using a single estimation technique.

  12. We presented the Morishima elasticity estimates for Model 1 only because the monotonicity conditions are satisfied by most of the observations in this model, but in the other two models the monotonicity conditions with respect to SO2 and NO x are not satisfied by the majority of the observations.

  13. The health effects of exposure of people to a given pollutant say SPM, SO2 or NO x could be different from those of other pollutants. For example, very high concentrations of SPM in the atmosphere could cause serious respiratory problems while those of NO x and SO2 could result in changes in pulmonary functions. There are now number of studies looking at the health impacts of air pollution (Alberini et al. 1997; Hammit and Zhou 2006; Dziegielewska and Mendelsohn 2005; Hubell 2006). Also there are some studies in India estimating the benefits of air pollution abatement (Kumar and Rao 2001; Murty et al. 2003; Gupta 2006).

  14. Mehta et al. (1995), Murty et al. (1999), Pandey (1998), and Misra (1999), Murty and Kumar (2002, 2004).

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Acknowledgements

This paper forms part of research done for the project on ‘Natural Resource Accounting’ at the Institute of Economic Growth, Delhi funded by the Central Statistical Organization, Government of India. We are grateful to an anonymous referee and the editors of this journal for very useful comments on an earlier draft of this paper. We are also benefited from the comments of participants in the faculty seminar of Institute of Economic Growth, Delhi and a presentation at the Third World Congress of Environmental and Resource Economics, Kyoto, Japan. We express thanks to all of them.

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Authors and Affiliations

  1. Institute of Economic Growth, Delhi University Enclave, Delhi, 110007, India

    M. N. Murty & Kishore K. Dhavala

  2. Department of Policy Studies, TERI University, New Delhi, 110003, India

    Surender Kumar

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  1. M. N. Murty
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Correspondence to M. N. Murty.

Appendix

Appendix

Table 9 Estimates of technical efficiency, shadow prices and Morishima elasticity
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Table 10 Estimates of the Morishima elasticity of substitution
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Murty, M.N., Kumar, S. & Dhavala, K. . Measuring environmental efficiency of industry: a case study of thermal power generation in India. Environ Resource Econ 38, 31–50 (2007). https://doi.org/10.1007/s10640-006-9055-6

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