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International comparison of total-factor energy productivity growth: A parametric Malmquist index approach

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  • Du, Kerui
  • Lin, Boqiang

Abstract

This paper constructs a Malmquist energy productivity index based on the Shephard energy distance function to measure total-factor energy productivity change. In order to account for individual heterogeneities as well as statistical noises, we use a newly developed fixed-effects SFA model for estimation. Then it is applied to compare energy productivity growth across the world's 123 economies. The main findings are as follows. First, on average the world witnessed a 34.6% growth of energy productivity between 1990 and 2010 which was mainly driven by technological progress. Second, the developed countries achieved higher growth in energy productivity than the developing countries. Third, the developed countries took lead in technological progress while the developing countries performed better in efficiency improvement. Fourth, there are no evidences supporting σ-convergence among countries' energy productivity growth.

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  • Du, Kerui & Lin, Boqiang, 2017. "International comparison of total-factor energy productivity growth: A parametric Malmquist index approach," Energy, Elsevier, vol. 118(C), pages 481-488.
  • Handle: RePEc:eee:energy:v:118:y:2017:i:c:p:481-488
    DOI: 10.1016/j.energy.2016.10.052
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    as
    1. Chang, Tzu-Pu & Hu, Jin-Li, 2010. "Total-factor energy productivity growth, technical progress, and efficiency change: An empirical study of China," Applied Energy, Elsevier, vol. 87(10), pages 3262-3270, October.
    2. Stern, David I., 2012. "Modeling international trends in energy efficiency," Energy Economics, Elsevier, vol. 34(6), pages 2200-2208.
    3. Woo, Chungwon & Chung, Yanghon & Chun, Dongphil & Seo, Hangyeol & Hong, Sungjun, 2015. "The static and dynamic environmental efficiency of renewable energy: A Malmquist index analysis of OECD countries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 47(C), pages 367-376.
    4. William Greene, 2004. "Distinguishing between heterogeneity and inefficiency: stochastic frontier analysis of the World Health Organization's panel data on national health care systems," Health Economics, John Wiley & Sons, Ltd., vol. 13(10), pages 959-980.
    5. 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.
    6. 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.
    7. 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.
    8. Wang, Qunwei & Su, Bin & Sun, Jiasen & Zhou, Peng & Zhou, Dequn, 2015. "Measurement and decomposition of energy-saving and emissions reduction performance in Chinese cities," Applied Energy, Elsevier, vol. 151(C), pages 85-92.
    9. Filippini, Massimo & Hunt, Lester C., 2015. "Measurement of energy efficiency based on economic foundations," Energy Economics, Elsevier, vol. 52(S1), pages 5-16.
    10. 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.
    11. Choi, Yongrok & Zhang, Ning & Zhou, P., 2012. "Efficiency and abatement costs of energy-related CO2 emissions in China: A slacks-based efficiency measure," Applied Energy, Elsevier, vol. 98(C), pages 198-208.
    12. 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.
    13. Ang, BW, 1994. "Decomposition of industrial energy consumption : The energy intensity approach," Energy Economics, Elsevier, vol. 16(3), pages 163-174, July.
    14. 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.
    15. Ang, B.W. & Mu, A.R. & Zhou, P., 2010. "Accounting frameworks for tracking energy efficiency trends," Energy Economics, Elsevier, vol. 32(5), pages 1209-1219, September.
    16. Po-Chi Chen & Ming-Miin Yu, 2014. "Total factor productivity growth and directions of technical change bias: evidence from 99 OECD and non-OECD countries," Annals of Operations Research, Springer, vol. 214(1), pages 143-165, March.
    17. 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.
    18. Wang, Ke & Wei, Yi-Ming, 2014. "China’s regional industrial energy efficiency and carbon emissions abatement costs," Applied Energy, Elsevier, vol. 130(C), pages 617-631.
    19. Ang, B.W. & Liu, F.L., 2001. "A new energy decomposition method: perfect in decomposition and consistent in aggregation," Energy, Elsevier, vol. 26(6), pages 537-548.
    20. X. Q. Liu & B. W. Ang & H.L. Ong, 1992. "The Application of the Divisia Index to the Decomposition of Changes in Industrial Energy Consumption," The Energy Journal, International Association for Energy Economics, vol. 0(Number 4), pages 161-178.
    21. Honma, Satoshi & Hu, Jin-Li, 2014. "A panel data parametric frontier technique for measuring total-factor energy efficiency: An application to Japanese regions," Energy, Elsevier, vol. 78(C), pages 732-739.
    22. Lin, Boqiang & Du, Kerui, 2013. "Technology gap and China's regional energy efficiency: A parametric metafrontier approach," Energy Economics, Elsevier, vol. 40(C), pages 529-536.
    23. B. W. Ang & Ki-Hong Choi, 1997. "Decomposition of Aggregate Energy and Gas Emission Intensities for Industry: A Refined Divisia Index Method," The Energy Journal, International Association for Energy Economics, vol. 0(Number 3), pages 59-73.
    24. Chen, Yi-Yi & Schmidt, Peter & Wang, Hung-Jen, 2014. "Consistent estimation of the fixed effects stochastic frontier model," Journal of Econometrics, Elsevier, vol. 181(2), pages 65-76.
    25. Ma, Chunbo & Stern, David I., 2008. "China's changing energy intensity trend: A decomposition analysis," Energy Economics, Elsevier, vol. 30(3), pages 1037-1053, May.
    26. Wang, Qunwei & Zhao, Zengyao & Zhou, Peng & Zhou, Dequn, 2013. "Energy efficiency and production technology heterogeneity in China: A meta-frontier DEA approach," Economic Modelling, Elsevier, vol. 35(C), pages 283-289.
    27. Wang, Chunhua, 2011. "Sources of energy productivity growth and its distribution dynamics in China," Resource and Energy Economics, Elsevier, vol. 33(1), pages 279-292, January.
    28. Wu, F. & Fan, L.W. & Zhou, P. & Zhou, D.Q., 2012. "Industrial energy efficiency with CO2 emissions in China: A nonparametric analysis," Energy Policy, Elsevier, vol. 49(C), pages 164-172.
    29. Lin, Boqiang & Du, Kerui, 2014. "Decomposing energy intensity change: A combination of index decomposition analysis and production-theoretical decomposition analysis," Applied Energy, Elsevier, vol. 129(C), pages 158-165.
    30. 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.
    31. Cui, Qiang & Kuang, Hai-bo & Wu, Chun-you & Li, Ye, 2014. "The changing trend and influencing factors of energy efficiency: The case of nine countries," Energy, Elsevier, vol. 64(C), pages 1026-1034.
    32. Menegaki, Angeliki N., 2013. "Growth and renewable energy in Europe: Benchmarking with data envelopment analysis," Renewable Energy, Elsevier, vol. 60(C), pages 363-369.
    33. Ang, B. W. & Lee, S. Y., 1994. "Decomposition of industrial energy consumption : Some methodological and application issues," Energy Economics, Elsevier, vol. 16(2), pages 83-92, April.
    34. Boyd, Gale A. & Hanson, Donald A. & Sterner, Thomas, 1988. "Decomposition of changes in energy intensity : A comparison of the Divisia index and other methods," Energy Economics, Elsevier, vol. 10(4), pages 309-312, October.
    35. 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.
    36. Ang, B.W. & Liu, F.L. & Chung, Hyun-Sik, 2004. "A generalized Fisher index approach to energy decomposition analysis," Energy Economics, Elsevier, vol. 26(5), pages 757-763, September.
    37. Lin, Boqiang & Du, Kerui, 2014. "Measuring energy efficiency under heterogeneous technologies using a latent class stochastic frontier approach: An application to Chinese energy economy," Energy, Elsevier, vol. 76(C), pages 884-890.
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    Cited by:

    1. Zheming Yan & Rui Shi & Zhiming Yang, 2018. "ICT Development and Sustainable Energy Consumption: A Perspective of Energy Productivity," Sustainability, MDPI, Open Access Journal, vol. 10(7), pages 1-15, July.
    2. Tan, Ruipeng & Lin, Boqiang & Liu, Xiying, 2019. "Impacts of eliminating the factor distortions on energy efficiency—A focus on China's secondary industry," Energy, Elsevier, vol. 183(C), pages 693-701.
    3. Du, Kerui & Li, Jianglong, 2019. "Towards a green world: How do green technology innovations affect total-factor carbon productivity," Energy Policy, Elsevier, vol. 131(C), pages 240-250.
    4. Tengfei Huo & Hong Ren & Weiguang Cai & Wei Feng & Miaohan Tang & Nan Zhou, 2018. "The total-factor energy productivity growth of China’s construction industry: evidence from the regional level," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 92(3), pages 1593-1616, July.
    5. Li, Jianglong & Liu, Hongxun & Du, Kerui, 2019. "Does market-oriented reform increase energy rebound effect? Evidence from China's regional development," China Economic Review, Elsevier, vol. 56(C), pages 1-1.
    6. Walheer, Barnabé, 2018. "Labour productivity growth and energy in Europe: A production-frontier approach," Energy, Elsevier, vol. 152(C), pages 129-143.

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