Interstate Comparison of Environmental Performance using Stochastic Frontier Analysis: The United States Case Study
Environmental performance of the U.S. States is investigated for the period of 1990 – 2007 using Stochastic Frontier Analysis (SFA). The SFA accounts for both efficiency measure and stochastic noise affecting a frontier. The frontier is formed using indicators of GDP, energy consumption, population, and CO2 emissions. For comparability, all indicators are expressed as ratios to total. Statistical information of the Energy Information Agency of the United States is used. Obtained results reveal the bell - shaped dynamics of environmental efficiency scores. The average efficiency scores rise from 97.6% in 1990 to 99.6% in 1999, and then fall to 98.4% in 2007. The main factor is insufficient decrease in the rate of growth of CO2 emissions with regards to the growth of GDP, population and energy consumption. Data for 2008 following the research period allow for an assumption that the environmental performance of the U.S. States has improved in the last years.
[1] Emissions of Greenhouse Gases in the United States 2008. Energy
Information Administration. http://www.eia.doe.gov, 2009.
[2] Sheremet, A. D. Kompleksnyi ekonomicheskii analiz deiatelnosti
predpriiatiia : (voprosy metodologii). Moskva : Ekonomika, 1974. (In
Russian).
[3] K. Yamaji, R. Matsuhashi, Y. Nagata, Y. Kaya, An Integrated System
for CO2/Energy/GNP Analysis: Case Studies on Economic Measures
for CO2 Reduction in Japan. Workshop on CO2 Reduction and
Removal: Measures for the Next Century, March 19, 1991,
International Institute for Applied Systems Analysis, Laxenburg,
Austria.
[4] M. Raupach, G. Marland, P. Ciais, C. Le Quere, J. Canadell, G.
Klepper, C. Field, Global and regional drivers of accelerating CO2
emissions. Proceedings of the National Academy of Sciences of the
United States of America, vol. 104, no. 24, pp. 10288 - 10293. June 12,
2007, www.pnas.org_cgi_doi_10.1073_pnas.0700609104.
[5] Aigner D, Lovell C, Schmidt P. Formulation and estimation of
stochastic frontier production function models. J Econometrics
1977;6:21-37.
[6] Meeusen W, van Den Broeck J. Efficiency estimation from Cobb-
Douglas production functions with composed error. International
Economic Review 1977;18:435-444.
[7] Materov I. On full identification of the stochastic production frontier
model. Ekonomika i matematicheskie metody 1981;17:784-788 (in
Russian).
[8] Jondrow J, Materov I, Lovell C, Schmidt P. On the estimation of
technical inefficiency in the stochastic frontier production model. J
Econometrics 1982;19:233-238.
[9] Kumbhakar S, Lovell C. Stochastic frontier analysis. Cambridge, UK:
Cambridge University Press, 2003.
[10] Pawitan Y. In all likelihood: Statistical modelling and inference using
likelihood.1st ed. USA: Oxford University Press, 2001.
[11] Cooper W, Seiford L, Zhu J. Handbook on data envelopment analysis
(International Series in Operations Research & Management Science).
New York: Springer, 2004.
[12] Battese G, Coelli T. A model for technical efficiency effects in a
stochastic frontier production function for panel data. Empirical
Economics 1995;20:325-332.
[13] Sena V. Stochastic frontier estimation: A review of the software
options. J Applied Econometrics 1999;14:579-586.
[14] Coelli T. A guide to FRONTIER Version 4.1: A computer program for
stochastic frontier production and cost function estimation. Centre for
Efficiency and Productivity Analysis (CEPA) Working Papers 1996; 7.
Department of Econometrics, University of New England, Australia,
http://www.une.edu.au/econometrics/cepa.htm.
[15] Greene W. LIMDEP (Version 7): User's manual and reference guide.
Econometric Software: New York, 1995.
[16] Cuesta R, Lovell C, Zofío J. Environmental efficiency measurement
with translog distance functions: A parametric approach. Ecological
Economics 2009;68:2232-2242.
[17] Hattori T. Relative performance of U.S. and Japanese electricity
distribution: An application of stochastic frontier analysis. J
Productivity Analysis 2002;18:269-284.
[18] Reinhard S, Lovell C, Thijssen G. Environmental efficiency with
multiple environmentally detrimental variables; estimated with SFA
and DEA. European J Operational Research, 2000;121:287-303.
[19] Avkiran N, Rowlands T. How to better identify the true managerial
performance: State of the art using DEA. Omega 2008;36:317-324.
[20] Fried H, Lovell C, Schmidt S, Yaisawarng S. Accounting for
environmental effects and statistical noise in data envelopment
analysis. J Prod Anal 2002;17(1-2):157-174.
[1] Emissions of Greenhouse Gases in the United States 2008. Energy
Information Administration. http://www.eia.doe.gov, 2009.
[2] Sheremet, A. D. Kompleksnyi ekonomicheskii analiz deiatelnosti
predpriiatiia : (voprosy metodologii). Moskva : Ekonomika, 1974. (In
Russian).
[3] K. Yamaji, R. Matsuhashi, Y. Nagata, Y. Kaya, An Integrated System
for CO2/Energy/GNP Analysis: Case Studies on Economic Measures
for CO2 Reduction in Japan. Workshop on CO2 Reduction and
Removal: Measures for the Next Century, March 19, 1991,
International Institute for Applied Systems Analysis, Laxenburg,
Austria.
[4] M. Raupach, G. Marland, P. Ciais, C. Le Quere, J. Canadell, G.
Klepper, C. Field, Global and regional drivers of accelerating CO2
emissions. Proceedings of the National Academy of Sciences of the
United States of America, vol. 104, no. 24, pp. 10288 - 10293. June 12,
2007, www.pnas.org_cgi_doi_10.1073_pnas.0700609104.
[5] Aigner D, Lovell C, Schmidt P. Formulation and estimation of
stochastic frontier production function models. J Econometrics
1977;6:21-37.
[6] Meeusen W, van Den Broeck J. Efficiency estimation from Cobb-
Douglas production functions with composed error. International
Economic Review 1977;18:435-444.
[7] Materov I. On full identification of the stochastic production frontier
model. Ekonomika i matematicheskie metody 1981;17:784-788 (in
Russian).
[8] Jondrow J, Materov I, Lovell C, Schmidt P. On the estimation of
technical inefficiency in the stochastic frontier production model. J
Econometrics 1982;19:233-238.
[9] Kumbhakar S, Lovell C. Stochastic frontier analysis. Cambridge, UK:
Cambridge University Press, 2003.
[10] Pawitan Y. In all likelihood: Statistical modelling and inference using
likelihood.1st ed. USA: Oxford University Press, 2001.
[11] Cooper W, Seiford L, Zhu J. Handbook on data envelopment analysis
(International Series in Operations Research & Management Science).
New York: Springer, 2004.
[12] Battese G, Coelli T. A model for technical efficiency effects in a
stochastic frontier production function for panel data. Empirical
Economics 1995;20:325-332.
[13] Sena V. Stochastic frontier estimation: A review of the software
options. J Applied Econometrics 1999;14:579-586.
[14] Coelli T. A guide to FRONTIER Version 4.1: A computer program for
stochastic frontier production and cost function estimation. Centre for
Efficiency and Productivity Analysis (CEPA) Working Papers 1996; 7.
Department of Econometrics, University of New England, Australia,
http://www.une.edu.au/econometrics/cepa.htm.
[15] Greene W. LIMDEP (Version 7): User's manual and reference guide.
Econometric Software: New York, 1995.
[16] Cuesta R, Lovell C, Zofío J. Environmental efficiency measurement
with translog distance functions: A parametric approach. Ecological
Economics 2009;68:2232-2242.
[17] Hattori T. Relative performance of U.S. and Japanese electricity
distribution: An application of stochastic frontier analysis. J
Productivity Analysis 2002;18:269-284.
[18] Reinhard S, Lovell C, Thijssen G. Environmental efficiency with
multiple environmentally detrimental variables; estimated with SFA
and DEA. European J Operational Research, 2000;121:287-303.
[19] Avkiran N, Rowlands T. How to better identify the true managerial
performance: State of the art using DEA. Omega 2008;36:317-324.
[20] Fried H, Lovell C, Schmidt S, Yaisawarng S. Accounting for
environmental effects and statistical noise in data envelopment
analysis. J Prod Anal 2002;17(1-2):157-174.
@article{"International Journal of Business, Human and Social Sciences:51280", author = "Alexander Y. Vaninsky", title = "Interstate Comparison of Environmental Performance using Stochastic Frontier Analysis: The United States Case Study", abstract = "Environmental performance of the U.S. States is investigated for the period of 1990 – 2007 using Stochastic Frontier Analysis (SFA). The SFA accounts for both efficiency measure and stochastic noise affecting a frontier. The frontier is formed using indicators of GDP, energy consumption, population, and CO2 emissions. For comparability, all indicators are expressed as ratios to total. Statistical information of the Energy Information Agency of the United States is used. Obtained results reveal the bell - shaped dynamics of environmental efficiency scores. The average efficiency scores rise from 97.6% in 1990 to 99.6% in 1999, and then fall to 98.4% in 2007. The main factor is insufficient decrease in the rate of growth of CO2 emissions with regards to the growth of GDP, population and energy consumption. Data for 2008 following the research period allow for an assumption that the environmental performance of the U.S. States has improved in the last years.
", keywords = "Stochastic frontier analysis, environmental performance, interstate comparisons.", volume = "4", number = "6", pages = "709-5", }
