IDEAS home Printed from https://ideas.repec.org/p/eth/wpswif/13-183.html
   My bibliography  Save this paper

Measurement of the “Underlying energy efficiency” in Chinese provinces

Author

Abstract

China is one of the largest consumers of energy globally. The country also emits some of the highest levels of CO2 globally. In 2009, 18% of the world’s total energy was consumed in China and the growth rate of energy consumption in China is 6.4% per year. In recent years, the Chinese government decided to introduce several energy policy instruments to promote energy efficiency. For instance, reduction targets for the level of energy intensity have been defined for provinces in China. However, energy intensity is not an accurate proxy for energy efficiency because changes in energy intensity are a function of changes in several socioeconomic factors. For this reason, in this paper we present an empirical analysis on the measurement of the persistent and transient “underlying energy efficiency” of Chinese provinces. For this purpose, a log-log aggregate energy demand frontier model is estimated by employing data on 29 provinces observed over the period 1996 to 2008. Several econometric model specifications for panel data are used: the random effects model and the true random effects model along with other versions of these models. Our analysis shows that energy intensity cannot measure accurately the level of efficiency in the use of energy in Chinese provinces. Further, our empirical analysis shows that the average value of the persistent “underlying energy efficiency” is around 0.78 whereas the average value of the transient “underlying energy efficiency” is approximately 0.93.

Suggested Citation

  • Massimo Filippini & Lin Zhang, 2013. "Measurement of the “Underlying energy efficiency” in Chinese provinces," CER-ETH Economics working paper series 13/183, CER-ETH - Center of Economic Research (CER-ETH) at ETH Zurich.
  • Handle: RePEc:eth:wpswif:13-183
    as

    Download full text from publisher

    File URL: http://www.cer.ethz.ch/content/dam/ethz/special-interest/mtec/cer-eth/cer-eth-dam/documents/working-papers/WP-13-183.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    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. 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.
    3. Massimo Filippini & Lester C. Hunt, 2013. "'Underlying Energy Efficiency' in the US," CER-ETH Economics working paper series 13/181, CER-ETH - Center of Economic Research (CER-ETH) at ETH Zurich.
    4. 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.
    5. Terza, Joseph V. & Basu, Anirban & Rathouz, Paul J., 2008. "Two-stage residual inclusion estimation: Addressing endogeneity in health econometric modeling," Journal of Health Economics, Elsevier, vol. 27(3), pages 531-543, May.
    6. Liao, Hua & Fan, Ying & Wei, Yi-Ming, 2007. "What induced China's energy intensity to fluctuate: 1997-2006?," Energy Policy, Elsevier, vol. 35(9), pages 4640-4649, September.
    7. Mehdi Farsi & Massimo Filippini & Michael Kuenzle, 2006. "Cost Efficiency in Regional Bus Companies: An Application of Alternative Stochastic Frontier Models," Journal of Transport Economics and Policy, University of Bath, vol. 40(1), pages 95-118, January.
    8. Hang, Leiming & Tu, Meizeng, 2007. "The impacts of energy prices on energy intensity: Evidence from China," Energy Policy, Elsevier, vol. 35(5), pages 2978-2988, May.
    9. 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.
    10. Polachek, Solomon W & Yoon, Bong Joon, 1996. "Panel Estimates of a Two-Tiered Earnings Frontier," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 11(2), pages 169-178, March-Apr.
    11. Fisher-Vanden, Karen & Jefferson, Gary H. & Liu, Hongmei & Tao, Quan, 2004. "What is driving China's decline in energy intensity?," Resource and Energy Economics, Elsevier, vol. 26(1), pages 77-97, March.
    12. Necmiddin Bagdadioglu & Thomas Weyman-Jones, 2007. "Regulatory Benchmarking with Panel Data," Discussion Paper Series 2007_03, Department of Economics, Loughborough University, revised Jan 2007.
    13. Song, Feng & Zheng, Xinye, 2012. "What drives the change in China's energy intensity: Combining decomposition analysis and econometric analysis at the provincial level," Energy Policy, Elsevier, vol. 51(C), pages 445-453.
    14. 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.
    15. Jondrow, James & Knox Lovell, C. A. & Materov, Ivan S. & Schmidt, Peter, 1982. "On the estimation of technical inefficiency in the stochastic frontier production function model," Journal of Econometrics, Elsevier, vol. 19(2-3), pages 233-238, August.
    16. Astrid Cullmann & Jens Schmidt-Ehmcke & Petra Zloczysti, 2012. "R&D efficiency and barriers to entry: a two stage semi-parametric DEA approach," Oxford Economic Papers, Oxford University Press, vol. 64(1), pages 176-196, January.
    17. Joanne Evans & Lester C. Hunt (ed.), 2009. "International Handbook on the Economics of Energy," Books, Edward Elgar Publishing, number 12764.
    18. Chu Wei & Jinlan Ni & Manhong Shen, 2009. "Empirical Analysis of Provincial Energy Efficiency in China," China & World Economy, Institute of World Economics and Politics, Chinese Academy of Social Sciences, vol. 17(5), pages 88-103, September.
    19. 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.
    20. Mundlak, Yair, 1978. "On the Pooling of Time Series and Cross Section Data," Econometrica, Econometric Society, vol. 46(1), pages 69-85, January.
    21. Ryan Mutter & William Greene & William Spector & Michael Rosko & Dana Mukamel, 2013. "Investigating the impact of endogeneity on inefficiency estimates in the application of stochastic frontier analysis to nursing homes," Journal of Productivity Analysis, Springer, vol. 39(2), pages 101-110, April.
    22. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Broadstock, David C. & Li, Jiajia & Zhang, Dayong, 2016. "Efficiency snakes and energy ladders: A (meta-)frontier demand analysis of electricity consumption efficiency in Chinese households," Energy Policy, Elsevier, vol. 91(C), pages 383-396.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Filippini, Massimo & Hunt, Lester C., 2015. "Measurement of energy efficiency based on economic foundations," Energy Economics, Elsevier, vol. 52(S1), pages 5-16.
    2. Massimo Filippini & Lester C. Hunt, 2013. "'Underlying Energy Efficiency' in the US," CER-ETH Economics working paper series 13/181, CER-ETH - Center of Economic Research (CER-ETH) at ETH Zurich.
    3. 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.
    4. Lv, Yulan & Chen, Wei & Cheng, Jianquan, 2020. "Effects of urbanization on energy efficiency in China: New evidence from short run and long run efficiency models," Energy Policy, Elsevier, vol. 147(C).
    5. Dayong Zhang and David C. Broadstock, 2016. "Club Convergence in the Energy Intensity of China," The Energy Journal, International Association for Energy Economics, vol. 0(Number 3).
    6. Lei Jiang & Henk Folmer & Minhe Ji & Jianjun Tang, 2017. "Energy efficiency in the Chinese provinces: a fixed effects stochastic frontier spatial Durbin error panel analysis," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 58(2), pages 301-319, March.
    7. Tajudeen, Ibrahim A., 2021. "The underlying drivers of economy-wide energy efficiency and asymmetric energy price responses," Energy Economics, Elsevier, vol. 98(C).
    8. Massimo Filippini & Luis Orea, 2014. "Applications of the stochastic frontier approach in Energy Economics," Economics and Business Letters, Oviedo University Press, vol. 3(1), pages 35-42.
    9. Yan, Huijie, 2015. "Provincial energy intensity in China: The role of urbanization," Energy Policy, Elsevier, vol. 86(C), pages 635-650.
    10. Sun, Huaping & Edziah, Bless Kofi & Sun, Chuanwang & Kporsu, Anthony Kwaku, 2019. "Institutional quality, green innovation and energy efficiency," Energy Policy, Elsevier, vol. 135(C).
    11. Macharia, Kenneth Kigundu & Gathiaka, John Kamau & Ngui, Dianah, 2022. "Energy efficiency in the Kenyan manufacturing sector," Energy Policy, Elsevier, vol. 161(C).
    12. Chen, Xiude & Qin, Quande & Wei, Y.-M., 2016. "Energy productivity and Chinese local officials’ promotions: Evidence from provincial governors," Energy Policy, Elsevier, vol. 95(C), pages 103-112.
    13. Akihiro Otsuka, 2020. "How do population agglomeration and interregional networks improve energy efficiency?," Asia-Pacific Journal of Regional Science, Springer, vol. 4(1), pages 1-25, February.
    14. Lester C. Hunt & Paraskevas Kipouros, 2023. "Energy Demand and Energy Efficiency in Developing Countries," Energies, MDPI, vol. 16(3), pages 1-26, January.
    15. 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.
    16. Amjadi, Golnaz & Lundgren, Tommy, 2022. "Is industrial energy inefficiency transient or persistent? Evidence from Swedish manufacturing," Applied Energy, Elsevier, vol. 309(C).
    17. Anna Alberini & Massimo Filippini, 2015. "Transient and Persistent Energy Efficiency in the US Residential Sector: Evidence from Household-level Data," CER-ETH Economics working paper series 15/220, CER-ETH - Center of Economic Research (CER-ETH) at ETH Zurich.
    18. Romero-Jordán, Desiderio & del Río, Pablo, 2022. "Analysing the drivers of the efficiency of households in electricity consumption," Energy Policy, Elsevier, vol. 164(C).
    19. Joanne Evans & Massimo Filippini & Lester C. Hunt, 2013. "The contribution of energy efficiency towards meeting CO2 targets," Chapters, in: Roger Fouquet (ed.), Handbook on Energy and Climate Change, chapter 8, pages 175-223, Edward Elgar Publishing.
    20. Akihiro Otsuka, 2023. "Stochastic demand frontier analysis of residential electricity demands in Japan," Asia-Pacific Journal of Regional Science, Springer, vol. 7(1), pages 179-195, March.

    More about this item

    Keywords

    Chinese energy demand; Stochastic frontier analysis: Underlying energy efficiency; Energy intensity.;
    All these keywords.

    JEL classification:

    • D - Microeconomics
    • D2 - Microeconomics - - Production and Organizations
    • Q - Agricultural and Natural Resource Economics; Environmental and Ecological Economics
    • Q4 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy
    • Q5 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eth:wpswif:13-183. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: the person in charge (email available below). General contact details of provider: https://edirc.repec.org/data/iwethch.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.