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

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  • 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
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    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. 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.
    4. Caudill, Steven B. & Ford, Jon M., 1993. "Biases in frontier estimation due to heteroscedasticity," Economics Letters, Elsevier, vol. 41(1), pages 17-20.
    5. Willam Greene, 2005. "Fixed and Random Effects in Stochastic Frontier Models," Journal of Productivity Analysis, Springer, vol. 23(1), pages 7-32, January.
    6. David Popp, 2002. "Induced Innovation and Energy Prices," American Economic Review, American Economic Association, vol. 92(1), pages 160-180, March.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. Stern, David I., 2010. "Modeling International Trends in Energy Efficiency and Carbon Emissions," Research Reports 94950, Australian National University, Environmental Economics Research Hub.
    13. 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.
    14. 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.
    15. 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.
    16. 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.
    17. 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.
    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.
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