IDEAS home Printed from https://ideas.repec.org/a/eee/enepol/v62y2013icp354-362.html
   My bibliography  Save this article

The potential estimation and factor analysis of China′s energy conservation on thermal power industry

Author

Listed:
  • Lin, Boqiang
  • Yang, Lisha

Abstract

At present, researches about energy conservation are focused on prediction. But there are few researches focused on the estimation of effective input and energy conservation potential, and there has been even no research on energy conservation of thermal power industry of China. This paper will try to fill in such a blank. Panel data on Chinese thermal power industry over 2005–2010 are established, and we adopt the stochastic frontier analysis approach to estimate the energy saving potential of thermal power industry. The results are as follows: (1) the average efficiency of energy inputs in China′s thermal power industry over 2005–2010 was about 0.85, and cumulative energy saving potential equals to 551.04 (Mtce); (2) by improving the non-efficiency factors, the relatively backward inland cities could achieve higher energy saving in thermal power industry; (3) the energy input efficiency of Eastern China Grid is shown to be the highest; (4) in order to realize the energy-saving goal of thermal power industry, one important policy method the government should adopt is to conduct a market-oriented reform in power industry and break the state-owned monopoly to provide incentives for private and foreign direct investment in thermal power sector.

Suggested Citation

  • Lin, Boqiang & Yang, Lisha, 2013. "The potential estimation and factor analysis of China′s energy conservation on thermal power industry," Energy Policy, Elsevier, vol. 62(C), pages 354-362.
    • Handle: RePEc:eee:enepol:v:62:y:2013:i:c:p:354-362
    DOI: 10.1016/j.enpol.2013.07.079
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0301421513007222
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.enpol.2013.07.079?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    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. Kumbhakar, Subal C., 1991. "Estimation of technical inefficiency in panel data models with firm- and time-specific effects," Economics Letters, Elsevier, vol. 36(1), pages 43-48, May.
    2. Stern, David I., 2012. "Modeling international trends in energy efficiency," Energy Economics, Elsevier, vol. 34(6), pages 2200-2208.
    3. Caudill, Steven B. & Ford, Jon M., 1993. "Biases in frontier estimation due to heteroscedasticity," Economics Letters, Elsevier, vol. 41(1), pages 17-20.
    4. Willam Greene, 2005. "Fixed and Random Effects in Stochastic Frontier Models," Journal of Productivity Analysis, Springer, vol. 23(1), pages 7-32, January.
    5. Hadri, Kaddour, 1999. "Estimation of a Doubly Heteroscedastic Stochastic Frontier Cost Function," Journal of Business & Economic Statistics, American Statistical Association, vol. 17(3), pages 359-363, July.
    6. Shenggen Fan, 1991. "Effects of Technological Change and Institutional Reform on Production Growth in Chinese Agriculture," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 73(2), pages 266-275.
    7. Lin, Boqiang & Wu, Ya & Zhang, Li, 2012. "Electricity saving potential of the power generation industry in China," Energy, Elsevier, vol. 40(1), pages 307-316.
    8. Stern, David I., 2010. "Modeling International Trends in Energy Efficiency and Carbon Emissions," Research Reports 94950, Australian National University, Environmental Economics Research Hub.
    9. Kneller, Richard & Andrew Stevens, Philip, 2003. "The specification of the aggregate production function in the presence of inefficiency," Economics Letters, Elsevier, vol. 81(2), pages 223-226, November.
    10. Kumbhakar, Subal C. & Wang, Hung-Jen, 2005. "Estimation of growth convergence using a stochastic production frontier approach," Economics Letters, Elsevier, vol. 88(3), pages 300-305, September.
    11. 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.
    12. 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.
    13. David Popp, 2002. "Induced Innovation and Energy Prices," American Economic Review, American Economic Association, vol. 92(1), pages 160-180, March.
    14. Wang, Hung-Jen, 2003. "A Stochastic Frontier Analysis of Financing Constraints on Investment: The Case of Financial Liberalization in Taiwan," Journal of Business & Economic Statistics, American Statistical Association, vol. 21(3), pages 406-419, July.
    15. Caudill, Steven B & Ford, Jon M & Gropper, Daniel M, 1995. "Frontier Estimation and Firm-Specific Inefficiency Measures in the Presence of Heteroscedasticity," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(1), pages 105-111, January.
    16. 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.
    17. Lin, Boqiang & Zhang, Li & Wu, Ya, 2012. "Evaluation of electricity saving potential in China's chemical industry based on cointegration," Energy Policy, Elsevier, vol. 44(C), pages 320-330.
    18. 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)

    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. Giovanni Marin & Alessandro Palma, 2015. "Technology invention and diffusion in residential energy consumption. A stochastic frontier approach," IEFE Working Papers 81, IEFE, Center for Research on Energy and Environmental Economics and Policy, Universita' Bocconi, Milano, Italy.
    2. Ajayi, Victor & Weyman-Jones, Tom, 2021. "State-level electricity generation efficiency: Do restructuring and regulatory institutions matter in the US?," Energy Economics, Elsevier, vol. 104(C).
    3. Sabrina Auci & Laura Castellucci & Manuela Coromaldi, 2021. "How does public spending affect technical efficiency? Some evidence from 15 European countries," Bulletin of Economic Research, Wiley Blackwell, vol. 73(1), pages 108-130, January.
    4. Getu Hailu & B. James Deaton, 2016. "Agglomeration Effects in Ontario’s Dairy Farming," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 98(4), pages 1055-1073.
    5. Paul, Satya & Shankar, Sriram, 2018. "Modelling Efficiency Effects in a True Fixed Effects Stochastic Frontier," MPRA Paper 87437, University Library of Munich, Germany.
    6. Satya Paul & Sriram Shankar, 2020. "Estimating efficiency effects in a panel data stochastic frontier model," Journal of Productivity Analysis, Springer, vol. 53(2), pages 163-180, April.
    7. 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.
    8. Vanesa D’Elia & Gustavo Ferro, 2019. "Empirical Efficiency Measurement in Higher Education: An Overview," CEMA Working Papers: Serie Documentos de Trabajo. 708, Universidad del CEMA.
    9. Ajayi, V. & Weyman-Jones, T., 2021. "State-Level Electricity Generation Efficiency: Do Restructuring and Regulatory Institutions Matter in the US?," Cambridge Working Papers in Economics 2166, Faculty of Economics, University of Cambridge.
    10. Cristian Barra & Nazzareno Ruggiero, 2022. "How do dimensions of institutional quality improve Italian regional innovation system efficiency? The Knowledge production function using SFA," Journal of Evolutionary Economics, Springer, vol. 32(2), pages 591-642, April.
    11. Salem Gheit, 2022. "A Stochastic Frontier Analysis of the Human Capital Effects on the Manufacturing Industries’ Technical Efficiency in the United States," Athens Journal of Business & Economics, Athens Institute for Education and Research (ATINER), vol. 8(3), pages 215-238, July.
    12. Efecan, Volkan & Temiz, İzzettin, 2023. "Assessing the technical efficiency of container ports based on a non-monotonic inefficiency effects model," Utilities Policy, Elsevier, vol. 81(C).
    13. Barra, Cristian & Zotti, Roberto, 2015. "Regional innovation system (in)efficiency and its determinants: an empirical evidence from Italian regions," MPRA Paper 67067, University Library of Munich, Germany.
    14. Cristian Barra & Roberto Zotti, 2018. "The contribution of university, private and public sector resources to Italian regional innovation system (in)efficiency," The Journal of Technology Transfer, Springer, vol. 43(2), pages 432-457, April.
    15. Dipanwita Sarkar & Trevor C. Collier, 2019. "Does host-country education mitigate immigrant inefficiency? Evidence from earnings of Australian university graduates," Empirical Economics, Springer, vol. 56(1), pages 81-106, January.
    16. Ali M. Oumer & Amin Mugera & Michael Burton & Atakelty Hailu, 2022. "Technical efficiency and firm heterogeneity in stochastic frontier models: application to smallholder maize farms in Ethiopia," Journal of Productivity Analysis, Springer, vol. 57(2), pages 213-241, April.
    17. Paul, Satya & Shankar, Sriram, 2018. "On estimating efficiency effects in a stochastic frontier model," European Journal of Operational Research, Elsevier, vol. 271(2), pages 769-774.
    18. Christopher F. Parmeter & Hung-Jen Wang & Subal C. Kumbhakar, 2017. "Nonparametric estimation of the determinants of inefficiency," Journal of Productivity Analysis, Springer, vol. 47(3), pages 205-221, June.
    19. Mark Andor & Frederik Hesse, "undated". "The StoNED age: The Departure Into a New Era of Efficiency Analysis? An MC study Comparing StoNED and the "Oldies" (SFA and DEA)," Working Papers 201285, Institute of Spatial and Housing Economics, Munster Universitary.
    20. Farsi, Mehdi & Filippini, Massimo, 2009. "An analysis of cost efficiency in Swiss multi-utilities," Energy Economics, Elsevier, vol. 31(2), pages 306-315, March.

    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:enepol:v:62:y:2013:i:c:p:354-362. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/enpol .

    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.