IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v76y2014icp884-890.html
   My bibliography  Save this article

Measuring energy efficiency under heterogeneous technologies using a latent class stochastic frontier approach: An application to Chinese energy economy

Author

Listed:
  • Lin, Boqiang
  • Du, Kerui

Abstract

The importance of technology heterogeneity in estimating economy-wide energy efficiency has been emphasized by recent literature. Some studies use the metafrontier analysis approach to estimate energy efficiency. However, for such studies, some reliable priori information is needed to divide the sample observations properly, which causes a difficulty in unbiased estimation of energy efficiency. Moreover, separately estimating group-specific frontiers might lose some common information across different groups. In order to overcome these weaknesses, this paper introduces a latent class stochastic frontier approach to measure energy efficiency under heterogeneous technologies. An application of the proposed model to Chinese energy economy is presented. Results show that the overall energy efficiency of China's provinces is not high, with an average score of 0.632 during the period from 1997 to 2010.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:energy:v:76:y:2014:i:c:p:884-890
    DOI: 10.1016/j.energy.2014.08.089
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544214010548
    Download Restriction: Full text for ScienceDirect subscribers only

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Stern, David I., 2012. "Modeling international trends in energy efficiency," Energy Economics, Elsevier, vol. 34(6), pages 2200-2208.
    2. 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.
    3. Choi, Ki-Hong & Ang, B.W. & Ro, K.K., 1995. "Decomposition of the energy-intensity index with application for the Korean manufacturing industry," Energy, Elsevier, vol. 20(9), pages 835-842.
    4. 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.
    5. 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.
    6. Lin, Boqiang & Wang, Xiaolei, 2014. "Promoting energy conservation in China's iron & steel sector," Energy, Elsevier, vol. 73(C), pages 465-474.
    7. Jiang, Zhujun & Lin, Boqiang, 2012. "China's energy demand and its characteristics in the industrialization and urbanization process," Energy Policy, Elsevier, vol. 49(C), pages 608-615.
    8. Khoshnevisan, Benyamin & Rafiee, Shahin & Omid, Mahmoud & Mousazadeh, Hossein, 2013. "Applying data envelopment analysis approach to improve energy efficiency and reduce GHG (greenhouse gas) emission of wheat production," Energy, Elsevier, vol. 58(C), pages 588-593.
    9. 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.
    10. 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.
    11. 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.
    12. Lin, Boqiang & Wang, Xiaolei, 2014. "Exploring energy efficiency in China׳s iron and steel industry: A stochastic frontier approach," Energy Policy, Elsevier, vol. 72(C), pages 87-96.
    13. 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.
    14. Wang, Chunhua, 2007. "Decomposing energy productivity change: A distance function approach," Energy, Elsevier, vol. 32(8), pages 1326-1333.
    15. Rahman, Sanzidur & Hasan, M. Kamrul, 2014. "Energy productivity and efficiency of wheat farming in Bangladesh," Energy, Elsevier, vol. 66(C), pages 107-114.
    16. 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.
    17. Léopold Simar, 2003. "Detecting Outliers in Frontier Models: A Simple Approach," Journal of Productivity Analysis, Springer, vol. 20(3), pages 391-424, November.
    18. 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.
    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. Luis Orea & Subal C. Kumbhakar, 2004. "Efficiency measurement using a latent class stochastic frontier model," Empirical Economics, Springer, vol. 29(1), pages 169-183, January.
    21. 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.
    22. Antonio Alvarez & Julio del Corral, 2010. "Identifying different technologies using a latent class model: extensive versus intensive dairy farms," European Review of Agricultural Economics, Foundation for the European Review of Agricultural Economics, vol. 37(2), pages 231-250, June.
    23. Ang, B.W. & Zhang, F.Q., 2000. "A survey of index decomposition analysis in energy and environmental studies," Energy, Elsevier, vol. 25(12), pages 1149-1176.
    24. Lin, Boqiang & Moubarak, Mohamed, 2014. "Estimation of energy saving potential in China's paper industry," Energy, Elsevier, vol. 65(C), pages 182-189.
    25. 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.
    26. Wei, Yi-Ming & Liao, Hua & Fan, Ying, 2007. "An empirical analysis of energy efficiency in China's iron and steel sector," Energy, Elsevier, vol. 32(12), pages 2262-2270.
    27. 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.
    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. Ying Li & Yung-ho Chiu & Tai-Yu Lin, 2019. "Energy and Environmental Efficiency in Different Chinese Regions," Sustainability, MDPI, Open Access Journal, vol. 11(4), pages 1-26, February.
    2. Feng, Chao & Wang, Miao, 2017. "The economy-wide energy efficiency in China’s regional building industry," Energy, Elsevier, vol. 141(C), pages 1869-1879.
    3. Jianglong Li & Boqiang Lin, 2016. "Green Economy Performance and Green Productivity Growth in China’s Cities: Measures and Policy Implication," Sustainability, MDPI, Open Access Journal, vol. 8(9), pages 1-21, September.
    4. Fei, Rilong & Lin, Boqiang, 2016. "Energy efficiency and production technology heterogeneity in China's agricultural sector: A meta-frontier approach," Technological Forecasting and Social Change, Elsevier, vol. 109(C), pages 25-34.
    5. Mardani, Abbas & Zavadskas, Edmundas Kazimieras & Streimikiene, Dalia & Jusoh, Ahmad & Khoshnoudi, Masoumeh, 2017. "A comprehensive review of data envelopment analysis (DEA) approach in energy efficiency," Renewable and Sustainable Energy Reviews, Elsevier, vol. 70(C), pages 1298-1322.
    6. Sugathan, Anish & Malghan, Deepak & Chandrashekar, S. & Sinha, Deepak K., 2019. "Downstream electric utility restructuring and upstream generation efficiency: Productivity dynamics of Indian coal and gas based electricity generators," Energy, Elsevier, vol. 178(C), pages 832-852.
    7. Liu, Xiao-Yan & Pollitt, Michael G. & Xie, Bai-Chen & Liu, Li-Qiu, 2019. "Does environmental heterogeneity affect the productive efficiency of grid utilities in China?," Energy Economics, Elsevier, vol. 83(C), pages 333-344.
    8. Yan, Zheming & Ouyang, Xiaoling & Du, Kerui, 2019. "Economy-wide estimates of energy rebound effect: Evidence from China's provinces," Energy Economics, Elsevier, vol. 83(C), pages 389-401.
    9. Hang, Ye & Sun, Jiasen & Wang, Qunwei & Zhao, Zengyao & Wang, Yizhong, 2015. "Measuring energy inefficiency with undesirable outputs and technology heterogeneity in Chinese cities," Economic Modelling, Elsevier, vol. 49(C), pages 46-52.
    10. 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.
    11. He, Yongda & Lin, Boqiang, 2018. "Forecasting China's total energy demand and its structure using ADL-MIDAS model," Energy, Elsevier, vol. 151(C), pages 420-429.
    12. Zhang, Lin & Adom, Philip Kofi, 2018. "Energy Efficiency Transitions in China: How persistent are the movements to/from the frontier?," MPRA Paper 94797, University Library of Munich, Germany.
    13. 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.
    14. Corsatea, Teodora Diana & Giaccaria, Sergio, 2018. "Market regulation and environmental productivity changes in the electricity and gas sector of 13 observed EU countries," Energy, Elsevier, vol. 164(C), pages 1286-1297.
    15. Lin, Boqiang & Du, Kerui, 2015. "Modeling the dynamics of carbon emission performance in China: A parametric Malmquist index approach," Energy Economics, Elsevier, vol. 49(C), pages 550-557.
    16. Yan, Huijie, 2015. "Provincial energy intensity in China: The role of urbanization," Energy Policy, Elsevier, vol. 86(C), pages 635-650.
    17. Ates, Seyithan A., 2015. "Energy efficiency and CO2 mitigation potential of the Turkish iron and steel industry using the LEAP (long-range energy alternatives planning) system," Energy, Elsevier, vol. 90(P1), pages 417-428.
    18. Zhu, Lin & Wang, Yong & Shang, Peipei & Qi, Lin & Yang, Guangchun & Wang, Ying, 2019. "Improvement path, the improvement potential and the dynamic evolution of regional energy efficiency in China: Based on an improved nonradial multidirectional efficiency analysis," Energy Policy, Elsevier, vol. 133(C).
    19. Li, Ke & Lin, Boqiang, 2015. "Metafroniter energy efficiency with CO2 emissions and its convergence analysis for China," Energy Economics, Elsevier, vol. 48(C), pages 230-241.
    20. Lin, Boqiang & Du, Kerui, 2015. "Energy and CO2 emissions performance in China's regional economies: Do market-oriented reforms matter?," Energy Policy, Elsevier, vol. 78(C), pages 113-124.
    21. Ouyang, Xiaoling & Wei, Xiaoyun & Sun, Chuanwang & Du, Gang, 2018. "Impact of factor price distortions on energy efficiency: Evidence from provincial-level panel data in China," Energy Policy, Elsevier, vol. 118(C), pages 573-583.
    22. Feng, Chao & Wang, Miao, 2018. "Analysis of energy efficiency in China's transportation sector," Renewable and Sustainable Energy Reviews, Elsevier, vol. 94(C), pages 565-575.

    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:eee:energy:v:76:y:2014:i:c:p:884-890. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Dana Niculescu). General contact details of provider: http://www.journals.elsevier.com/energy .

    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 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.

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.