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A parametric method to estimate environmental energy efficiency with non-radial adjustment: an application to China

Author

Listed:
  • Hongzhou Li

    (Dongbei University of Finance and Economics)

  • Andrea Appolloni

    (University of Rome Tor Vergata
    Cranfield University
    National Research Council (CNR))

  • Yijie Dou

    (Dongbei University of Finance and Economics)

  • Vincenzo Basile

    (Federico II University of Naples)

  • Maria Kopsakangas-Savolainen

    (University of Oulu Business School)

Abstract

To estimate the performance of China in terms of energy use efficiency during the first two decades of the twenty-first century while also taking into consideration pollutant emission, this study uses a panel data set covering 30 provincial administrative regions in mainland China for the period 2000–2016. To overcome problems with the DEA-based method, this study proposes an SFA-based model that can estimate environmental energy efficiency while maintaining the regularity constraints imposed on undesirable output, by using Bayesian technique. Our empirical results show that the average value of environmental energy efficiency during the whole sample period changed from 0.7858 in 2000 to 0.7726 in 2016, with an average value of 0.7812 over the whole period. This result is in sharp contrast with findings based on the often-used GDP/energy and GDP/undesirable output indexes, both of which show an improving trend over same sample period. This study suggests that more sophisticated indexes should be used to evaluate meaningful energy efficiency and environmental protection-related performance.

Suggested Citation

  • Hongzhou Li & Andrea Appolloni & Yijie Dou & Vincenzo Basile & Maria Kopsakangas-Savolainen, 2024. "A parametric method to estimate environmental energy efficiency with non-radial adjustment: an application to China," Annals of Operations Research, Springer, vol. 342(3), pages 1379-1405, November.
  • Handle: RePEc:spr:annopr:v:342:y:2024:i:3:d:10.1007_s10479-022-05053-z
    DOI: 10.1007/s10479-022-05053-z
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    1. Filippini, Massimo & Hunt, Lester C., 2012. "US residential energy demand and energy efficiency: A stochastic demand frontier approach," Energy Economics, Elsevier, vol. 34(5), pages 1484-1491.
    2. Filippini, Massimo & Hunt, Lester C., 2015. "Measurement of energy efficiency based on economic foundations," Energy Economics, Elsevier, vol. 52(S1), pages 5-16.
    3. Massimo Filippini & Lester C. Hunt, 2011. "Energy Demand and Energy Efficiency in the OECD Countries: A Stochastic Demand Frontier Approach," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2), pages 59-80.
    4. Gale A. Boyd, 2008. "Estimating Plant Level Energy Efficiency with a Stochastic Frontier," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2), pages 23-44.
    5. Zhou, P. & Ang, B.W., 2008. "Linear programming models for measuring economy-wide energy efficiency performance," Energy Policy, Elsevier, vol. 36(8), pages 2901-2906, August.
    6. George Assaf, A. & Matousek, Roman & Tsionas, Efthymios G., 2013. "Turkish bank efficiency: Bayesian estimation with undesirable outputs," Journal of Banking & Finance, Elsevier, vol. 37(2), pages 506-517.
    7. Cliff Huang & Tai-Hsin Huang & Nan-Hung Liu, 2014. "A new approach to estimating the metafrontier production function based on a stochastic frontier framework," Journal of Productivity Analysis, Springer, vol. 42(3), pages 241-254, December.
    8. Henry Tulkens & Philippe Eeckaut, 2006. "Nonparametric Efficiency, Progress and Regress Measures For Panel Data: Methodological Aspects," Springer Books, in: Parkash Chander & Jacques Drèze & C. Knox Lovell & Jack Mintz (ed.), Public goods, environmental externalities and fiscal competition, chapter 0, pages 395-429, Springer.
    9. P. Zhou & F. Wu & D. Q. Zhou, 2017. "Total-factor energy efficiency with congestion," Annals of Operations Research, Springer, vol. 255(1), pages 241-256, August.
    10. Tim Coelli & Sergio Perelman & Elliot Romano, 1999. "Accounting for Environmental Influences in Stochastic Frontier Models: With Application to International Airlines," Journal of Productivity Analysis, Springer, vol. 11(3), pages 251-273, June.
    11. Sueyoshi, Toshiyuki & Yuan, Yan & Goto, Mika, 2017. "A literature study for DEA applied to energy and environment," Energy Economics, Elsevier, vol. 62(C), pages 104-124.
    12. Zhou, P. & Ang, B.W. & Zhou, D.Q., 2012. "Measuring economy-wide energy efficiency performance: A parametric frontier approach," Applied Energy, Elsevier, vol. 90(1), pages 196-200.
    13. Kuosmanen, Timo, 2012. "Stochastic semi-nonparametric frontier estimation of electricity distribution networks: Application of the StoNED method in the Finnish regulatory model," Energy Economics, Elsevier, vol. 34(6), pages 2189-2199.
    14. Mukherjee, Kankana, 2008. "Energy use efficiency in U.S. manufacturing: A nonparametric analysis," Energy Economics, Elsevier, vol. 30(1), pages 76-96, January.
    15. Christopher O’Donnell & D. Rao & George Battese, 2008. "Metafrontier frameworks for the study of firm-level efficiencies and technology ratios," Empirical Economics, Springer, vol. 34(2), pages 231-255, March.
    16. Meeusen, Wim & van den Broeck, Julien, 1977. "Efficiency Estimation from Cobb-Douglas Production Functions with Composed Error," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 18(2), pages 435-444, June.
    17. Yang, Hongliang & Pollitt, Michael, 2010. "The necessity of distinguishing weak and strong disposability among undesirable outputs in DEA: Environmental performance of Chinese coal-fired power plants," Energy Policy, Elsevier, vol. 38(8), pages 4440-4444, August.
    18. Ang, B.W., 2006. "Monitoring changes in economy-wide energy efficiency: From energy-GDP ratio to composite efficiency index," Energy Policy, Elsevier, vol. 34(5), pages 574-582, March.
    19. Buck, J. & Young, D., 2007. "The potential for energy efficiency gains in the Canadian commercial building sector: A stochastic frontier study," Energy, Elsevier, vol. 32(9), pages 1769-1780.
    20. Jim Griffin & Mark Steel, 2007. "Bayesian stochastic frontier analysis using WinBUGS," Journal of Productivity Analysis, Springer, vol. 27(3), pages 163-176, June.
    21. Subal Kumbhakar & Gudbrand Lien & J. Hardaker, 2014. "Technical efficiency in competing panel data models: a study of Norwegian grain farming," Journal of Productivity Analysis, Springer, vol. 41(2), pages 321-337, April.
    22. Schmidt, Peter & Sickles, Robin C, 1984. "Production Frontiers and Panel Data," Journal of Business & Economic Statistics, American Statistical Association, vol. 2(4), pages 367-374, October.
    23. van den Broeck, Julien & Koop, Gary & Osiewalski, Jacek & Steel, Mark F. J., 1994. "Stochastic frontier models : A Bayesian perspective," Journal of Econometrics, Elsevier, vol. 61(2), pages 273-303, April.
    24. Astrid Cullmann, 2012. "Benchmarking and firm heterogeneity: a latent class analysis for German electricity distribution companies," Empirical Economics, Springer, vol. 42(1), pages 147-169, February.
    25. Kuosmanen, Timo, 2006. "Stochastic Nonparametric Envelopment of Data: Combining Virtues of SFA and DEA in a Unified Framework," Discussion Papers 11864, MTT Agrifood Research Finland.
    26. Hu, Jin-Li & Wang, Shih-Chuan, 2006. "Total-factor energy efficiency of regions in China," Energy Policy, Elsevier, vol. 34(17), pages 3206-3217, November.
    27. Gale A. Boyd, 2008. "Estimating Plant Level Energy Efficiency with a Stochastic Frontier," The Energy Journal, , vol. 29(2), pages 23-44, April.
    28. Mandal, Sabuj Kumar, 2010. "Do undesirable output and environmental regulation matter in energy efficiency analysis? Evidence from Indian Cement Industry," Energy Policy, Elsevier, vol. 38(10), pages 6076-6083, October.
    29. Fare, Rolf, 1989. "Multilateral Productivity Comparisons When Some Outputs Are Undesirable: A Nonparametric Approach," The Review of Economics and Statistics, MIT Press, vol. 71(1), pages 90-98, February.
    30. Agrell, Per J. & Bogetoft, Peter, 2017. "Regulatory Benchmarking: Models, Analyses and Applications," Data Envelopment Analysis Journal, now publishers, vol. 3(1-2), pages 49-91, November.
    31. Battese, G E & Coelli, T J, 1995. "A Model for Technical Inefficiency Effects in a Stochastic Frontier Production Function for Panel Data," Empirical Economics, Springer, vol. 20(2), pages 325-332.
    32. Eskelinen, Juha & Kuosmanen, Timo, 2013. "Intertemporal efficiency analysis of sales teams of a bank: Stochastic semi-nonparametric approach," Journal of Banking & Finance, Elsevier, vol. 37(12), pages 5163-5175.
    33. Greene, William, 2005. "Reconsidering heterogeneity in panel data estimators of the stochastic frontier model," Journal of Econometrics, Elsevier, vol. 126(2), pages 269-303, June.
    34. Jie Wu & Panpan Xia & Qingyuan Zhu & Junfei Chu, 2019. "Measuring environmental efficiency of thermoelectric power plants: a common equilibrium efficient frontier DEA approach with fixed-sum undesirable output," Annals of Operations Research, Springer, vol. 275(2), pages 731-749, April.
    35. Atkinson, Scott E. & Dorfman, Jeffrey H., 2005. "Bayesian measurement of productivity and efficiency in the presence of undesirable outputs: crediting electric utilities for reducing air pollution," Journal of Econometrics, Elsevier, vol. 126(2), pages 445-468, June.
    36. Timo Kuosmanen & Andrew Johnson & Antti Saastamoinen, 2015. "Stochastic Nonparametric Approach to Efficiency Analysis: A Unified Framework," International Series in Operations Research & Management Science, in: Joe Zhu (ed.), Data Envelopment Analysis, edition 127, chapter 7, pages 191-244, Springer.
    37. Willam Greene, 2005. "Fixed and Random Effects in Stochastic Frontier Models," Journal of Productivity Analysis, Springer, vol. 23(1), pages 7-32, January.
    38. H. Fried & C. Lovell & S. Schmidt & S. Yaisawarng, 2002. "Accounting for Environmental Effects and Statistical Noise in Data Envelopment Analysis," Journal of Productivity Analysis, Springer, vol. 17(1), pages 157-174, January.
    39. Kumbhakar, Subal C., 1990. "Production frontiers, panel data, and time-varying technical inefficiency," Journal of Econometrics, Elsevier, vol. 46(1-2), pages 201-211.
    40. Ayres, Robert U & Kneese, Allen V, 1969. "Production , Consumption, and Externalities," American Economic Review, American Economic Association, vol. 59(3), pages 282-297, June.
    41. Rolf Fare, 1993. "Derivation of Shadow Prices for Undesirable Outputs: A Distance Function Approach," The Review of Economics and Statistics, MIT Press, vol. 75(2), pages 374-380, May.
    42. Li, Hong-Zhou & Kopsakangas-Savolainen, Maria & Xiao, Xing-Zhi & Tian, Zhen-Zhen & Yang, Xiao-Yuan & Wang, Jian-Lin, 2016. "Cost efficiency of electric grid utilities in China: A comparison of estimates from SFA–MLE, SFA–Bayes and StoNED–CNLS," Energy Economics, Elsevier, vol. 55(C), pages 272-283.
    43. Wu, Li-Ming & Chen, Bai-Sheng & Bor, Yun-Chang & Wu, Yin-Chin, 2007. "Structure model of energy efficiency indicators and applications," Energy Policy, Elsevier, vol. 35(7), pages 3768-3777, July.
    44. Battese, George E. & Coelli, Tim J., 1988. "Prediction of firm-level technical efficiencies with a generalized frontier production function and panel data," Journal of Econometrics, Elsevier, vol. 38(3), pages 387-399, July.
    45. Fare, Rolf & Grosskopf, Shawna & Noh, Dong-Woon & Weber, William, 2005. "Characteristics of a polluting technology: theory and practice," Journal of Econometrics, Elsevier, vol. 126(2), pages 469-492, June.
    46. Pitt, Mark M. & Lee, Lung-Fei, 1981. "The measurement and sources of technical inefficiency in the Indonesian weaving industry," Journal of Development Economics, Elsevier, vol. 9(1), pages 43-64, August.
    47. Proskuryakova, L. & Kovalev, A., 2015. "Measuring energy efficiency: Is energy intensity a good evidence base?," Applied Energy, Elsevier, vol. 138(C), pages 450-459.
    48. Filippini, Massimo & Hunt, Lester C. & Zorić, Jelena, 2014. "Impact of energy policy instruments on the estimated level of underlying energy efficiency in the EU residential sector," Energy Policy, Elsevier, vol. 69(C), pages 73-81.
    49. Timothy J. Coelli & D.S. Prasada Rao & Christopher J. O’Donnell & George E. Battese, 2005. "An Introduction to Efficiency and Productivity Analysis," Springer Books, Springer, edition 0, number 978-0-387-25895-9, June.
    50. George E. Battese & D. S. Prasada Rao, 2002. "Technology Gap, Efficiency, and a Stochastic Metafrontier Function," International Journal of Business and Economics, School of Management Development, Feng Chia University, Taichung, Taiwan, vol. 1(2), pages 87-93, August.
    51. Feijoo, Maria L. & Franco, Juan F. & Hernandez, Jose M., 2002. "Global warming and the energy efficiency of Spanish industry," Energy Economics, Elsevier, vol. 24(4), pages 405-423, July.
    52. Cropper, Maureen L & Oates, Wallace E, 1992. "Environmental Economics: A Survey," Journal of Economic Literature, American Economic Association, vol. 30(2), pages 675-740, June.
    53. Pittman, Russell W, 1983. "Multilateral Productivity Comparisons with Undesirable Outputs," Economic Journal, Royal Economic Society, vol. 93(372), pages 883-891, December.
    54. Cornwell, Christopher & Schmidt, Peter & Sickles, Robin C., 1990. "Production frontiers with cross-sectional and time-series variation in efficiency levels," Journal of Econometrics, Elsevier, vol. 46(1-2), pages 185-200.
    55. Aigner, Dennis & Lovell, C. A. Knox & Schmidt, Peter, 1977. "Formulation and estimation of stochastic frontier production function models," Journal of Econometrics, Elsevier, vol. 6(1), pages 21-37, July.
    56. Bokusheva, Raushan & Kumbhakar, Subal C., 2014. "A Distance Function Model with Good and Bad Outputs," 2014 International Congress, August 26-29, 2014, Ljubljana, Slovenia 182765, European Association of Agricultural Economists.
    57. Liao, Hua & Du, Yun-Fei & Huang, Zhimin & Wei, Yi-Ming, 2016. "Measuring energy economic efficiency: A mathematical programming approach," Applied Energy, Elsevier, vol. 179(C), pages 479-487.
    58. Mekaroonreung, Maethee & Johnson, Andrew L., 2012. "Estimating the shadow prices of SO2 and NOx for U.S. coal power plants: A convex nonparametric least squares approach," Energy Economics, Elsevier, vol. 34(3), pages 723-732.
    59. Timo Kuosmanen & Mika Kortelainen, 2012. "Stochastic non-smooth envelopment of data: semi-parametric frontier estimation subject to shape constraints," Journal of Productivity Analysis, Springer, vol. 38(1), pages 11-28, August.
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