Measuring Energy Efficiency: An Application of Stochastic Frontier Production Function Analysis to Power Sector in Kerala

Pillai N., Vijayamohanan (2019): Measuring Energy Efficiency: An Application of Stochastic Frontier Production Function Analysis to Power Sector in Kerala.

Abstract

Traditionally, there are two basically reciprocal energy efficiency Indicators: one, in terms of energy intensity, that is, energy use per unit of activity output, and the other, in terms of energy productivity, that is, activity output per unit of energy use. The enquiry that has proceeded from the problems associated with this method of a single energy input factor in terms of productivity has led to multi-factor productivity analysis. We have here two approaches: parametric and non-parametric. Parametric approach famously includes two methods: the erstwhile popular total factor energy productivity analysis and the currently fanciful stochastic frontier production function analysis; The non-parametric approach is popularly represented by data envelopment analysis. The present paper is an attempt to measure efficiency in electrical energy consumption in Kerala, India. We apply the parametric method of stochastic frontier production function analysis on a panel data of the Kerala power sector with three sectors (Primary, Secondary and Tertiary) for the period from 1970-71 to 2016-17. For a comparative purpose, we also have a regression with a pooled data stochastic frontier. The results indicate that the sector-wise technical efficiency estimates of the Kerala power sector are independent of time, which can significantly refer to a technically stagnant situation in energy efficiency. The implication of the time-varying decay model, even though statistically insignificant, of a falling trend in the technical efficiency of all the three sectors also is a hot matter of serious concerns.

• Item Type: MPRA Paper
• Language: English
• Keywords: Energy efficiency, Kerala, Power sector, Stochastic frontier, Technical efficiency.
• Subjects: C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C13 - Estimation: General; C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C51 - Model Construction and Estimation; Q - Agricultural and Natural Resource Economics ; Environmental and Ecological Economics > Q4 - Energy

References

Afriat, S.N. (1972), "Efficiency Estimation of Production Functions", International Economic Review, 13, 568-598.

Aigner, D., and S. Chu, 1968, “On Estimating the Industry Production Function,” American Economic Review, 58, pp. 826–839.

Aigner, D., K. Lovell, and P. Schmidt, 1977, “Formulation and Estimation of Stochastic Frontier Production Function Models,” Journal of Econometrics, 6, pp. 21–37.

Ali, A. I. and L. M. Seiford (1993), ‘The Mathematical Programming Approach to Efficiency Analysis’, in Fried, H.O., C.A.K. Lovell and S.S. Schmidt (eds), The Measurement of Productive Efficiency, Oxford University Press, New York, 120-159.

Ang, B.W., (2004). ‘Decomposition analysis for policymaking in energy: which is the preferred method?’, Energy Policy, 32, pp. 1131–1139.

Ang B.W. and K.H. Choi (1997). “Decomposition of aggregate energy and gas emission intensities for industry: a refined Divisia index method”. The Energy Journal. 18(3): 59-73.

Ang B.W., F.Q. Zhang and K.H. Choi (1998).”Factorizing changes in energy and environmental studies through decomposition”. Energy 23(6): 489-495.

Ang B.W. and F.Q. Zhang (2000). “A survey of index decomposition analysis in energy and environmental studies”. Energy 25(12):1149-1176.

Bauer, P., 1990, “A Survey of Recent Econometric Developments in Frontier Estimation,” Journal of Econometrics, 46, pp. 21–39.

Banker, R.D., A. Chames and W.W. Cooper (1984), "Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis", Management Science, 30, 1078-1092.

Boles, J.N. (1966), "Efficiency Squared - Efficient Computation of Efficiency Proceedings of the 39th Annual Meeting of the Western Farm Economic Association, pp 137-142.

Bossanyi, E. (1979). UK primary energy consumption and the changing structure of final demand. Energy Policy 7(6): 489-495.

Boyd, Gale; McDonald, John; Ross, M. and Hansont, D. A. (1987) “Separating the Changing Composition of U.S. Manufacturing Production from Energy Efficiency Improvements: A Divisia Index Approach”. The Energy Journal, Volume 8, issue Number 2, 77-96.

Charnes, A., W.W. Cooper and E. Rhodes (1978), "Measuring the Efficiency of Decision Making Units", European Journal of Operational Research, 2, 429-444.

Coelli, T.J., Rao D.S.P., O’Donnell C.J. and Battese G.E. (2005). An introduction to efficiency and productivity analysis. Springer.

Farrell, M.J. (1957), "The Measurement of Productive Efficiency", Journal of the Royal Statistical Society, Series A, CXX, Part 3, 253-290.

Førsund,F.,K. Lovell,and P. Schmidt, 1980,“ASurvey of Frontier Production Functions and of Their Relationship to Efficiency Measurement,” Journal of Econometrics, 13, pp. 5–25.

Greene, W., 1993, “The Econometric Approach to Efficiency Analysis,” in The Measurement of Productive Efficiency, H. Fried, K. Lovell, and S. Schmidt, eds., Oxford University Press, Oxford.

Henderson, James M. and Quandt, Richard E. (1971) Microeconomic Theory: A Mathematical Approach. 2nd edition. McGraw-Hill Kogakusha, London.

Kumbhakar, S., and K. Lovell, 2000, Stochastic Frontier Analysis, Cambridge University Press, Cambridge, UK.

Lovell, K., 1993,“Production Frontiers and Productive Efficiency,” in The Measurement of Productive Efficiency,H. Fried,K. Lovell, and S. Schmidt, eds., Oxford University Press, Oxford, UK.

Meeusen,W., and J. van den Broeck, 1977, “Efficiency Estimation from Cobb-Douglas Production Functions with Composed Error,” International Economic Review, 18, pp. 435–444.

Myers, J. and Nakamura L. (1978). Saving energy in manufacturing. Cambridge, MA: Ballinger, 1978.

Patterson, Murray G (1996) “What is energy efficiency? Concepts, indicators and methodological issues”. Energy Policy, vol. 24, issue 5, 377-390.

Richmond, J., 1974, “Estimating the Efficiency of Production,” International Economic Review, 15, pp. 515–521.

Schmidt, P., 1976, “On the Statistical Estimation of Parametric Frontier Production Functions,” Review of Economics and Statistics, 58, pp. 238–239.

Schmidt, P., 1985, “Frontier Production Functions,” Econometric Reviews, 4, pp. 289–328.

Schmidt, P., and R. Sickles, 1984, “Production Frontiers and Panel Data,” Journal of Business and Economic Statistics, 2, pp. 367–374.

Seiford, L. M. and Thrall, R. M. (1990), ‘Recent Developments in DEA: The Mathematical Approach to Frontier Analysis’, Journal of Econometrics 46, 7-38.