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Energy savings potential in China's industrial sector: From the perspectives of factor price distortion and allocative inefficiency

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  • Ouyang, Xiaoling
  • Sun, Chuanwang

Abstract

China's industrial energy consumption accounted for 70.82% of national and 14.12% of world energy usage in 2011. In the context of energy scarcity and environmental pollution, the industrial sector in China faces unsustainable growth problems. By adopting the stochastic frontier analysis (SFA) framework, this paper analyzes the factor allocative efficiency of China's industrial sector, and estimates the energy savings potential from the perspective of allocative inefficiency. This paper focuses on three issues. The first is examining the factor allocative inefficiency of China's industrial sector. The second is measuring factor price distortion by the shadow price model. The third is estimating the energy savings potential in China's industrial sector during 2001–2009. Major conclusions are thus drawn. First, factor prices of capital, labor and energy are distorted in China due to government regulations. Moreover, energy price is relatively low compared to capital price, while is relatively high compared to labor price. Second, the industry-wide energy savings potential resulted from energy allocative inefficiency was about 9.71% during 2001–2009. The downward trend of energy savings potential implies the increasing energy allocative efficiency in China's industrial sector. Third, a transparent and reasonable pricing mechanism is conducive to improving energy allocative efficiency.

Suggested Citation

  • Ouyang, Xiaoling & Sun, Chuanwang, 2015. "Energy savings potential in China's industrial sector: From the perspectives of factor price distortion and allocative inefficiency," Energy Economics, Elsevier, vol. 48(C), pages 117-126.
    • Handle: RePEc:eee:eneeco:v:48:y:2015:i:c:p:117-126
    DOI: 10.1016/j.eneco.2014.11.020
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    as
    1. Ma, Hengyun & Oxley, Les & Gibson, John & Kim, Bonggeun, 2008. "China's energy economy: Technical change, factor demand and interfactor/interfuel substitution," Energy Economics, Elsevier, vol. 30(5), pages 2167-2183, September.
    2. Lovell, C A Knox & Sickles, Robin C, 1983. "Testing Efficiency Hypotheses in Joint Production: A Parametric Approach," The Review of Economics and Statistics, MIT Press, vol. 65(1), pages 51-58, February.
    3. Lin, Boqiang & Jiang, Zhujun, 2011. "Estimates of energy subsidies in China and impact of energy subsidy reform," Energy Economics, Elsevier, vol. 33(2), pages 273-283, March.
    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. Haller, Stefanie A. & Hyland, Marie, 2014. "Capital–energy substitution: Evidence from a panel of Irish manufacturing firms," Energy Economics, Elsevier, vol. 45(C), pages 501-510.
    6. 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.
    7. Zhou, P. & Ang, B.W. & Han, J.Y., 2010. "Total factor carbon emission performance: A Malmquist index analysis," Energy Economics, Elsevier, vol. 32(1), pages 194-201, January.
    8. Atkinson, Scott E & Cornwell, Christopher, 1994. "Estimation of Output and Input Technical Efficiency Using a Flexible Form and Panel Data," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 35(1), pages 245-255, February.
    9. Zhou, P. & Sun, Z.R. & Zhou, D.Q., 2014. "Optimal path for controlling CO2 emissions in China: A perspective of efficiency analysis," Energy Economics, Elsevier, vol. 45(C), pages 99-110.
    10. 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.
    11. 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.
    12. Atkinson, Scott E & Halvorsen, Robert, 1984. "Parametric Efficiency Tests, Economies of Scale, and Input Demand in U.S. Electric Power Generation," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 25(3), pages 647-662, October.
    13. Zhou, P. & Ang, B.W. & Poh, K.L., 2008. "Measuring environmental performance under different environmental DEA technologies," Energy Economics, Elsevier, vol. 30(1), pages 1-14, January.
    14. Yuan, Jia-Hai & Kang, Jian-Gang & Zhao, Chang-Hong & Hu, Zhao-Guang, 2008. "Energy consumption and economic growth: Evidence from China at both aggregated and disaggregated levels," Energy Economics, Elsevier, vol. 30(6), pages 3077-3094, November.
    15. Atkinson, Scott E & Cornwell, Christopher, 1994. "Parametric Estimation of Technical and Allocative Inefficiency with Panel Data," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 35(1), pages 231-243, February.
    16. Schmidt, Peter & Knox Lovell, C. A., 1979. "Estimating technical and allocative inefficiency relative to stochastic production and cost frontiers," Journal of Econometrics, Elsevier, vol. 9(3), pages 343-366, February.
    17. Fare, Rolf & Knox Lovell, C. A., 1978. "Measuring the technical efficiency of production," Journal of Economic Theory, Elsevier, vol. 19(1), pages 150-162, October.
    18. Yiping Huang & Bijun Wang, 2010. "Cost Distortions and Structural Imbalances in China," China & World Economy, Institute of World Economics and Politics, Chinese Academy of Social Sciences, vol. 18(4), pages 1-17, July.
    19. Zhou, Peng & Poh, Kim Leng & Ang, Beng Wah, 2007. "A non-radial DEA approach to measuring environmental performance," European Journal of Operational Research, Elsevier, vol. 178(1), pages 1-9, April.
    20. Lau, Lawrence J & Yotopoulos, Pan A, 1971. "A Test for Relative Efficiency and Application to Indian Agriculture," American Economic Review, American Economic Association, vol. 61(1), pages 94-109, March.
    21. Zhou, P. & Zhou, X. & Fan, L.W., 2014. "On estimating shadow prices of undesirable outputs with efficiency models: A literature review," Applied Energy, Elsevier, vol. 130(C), pages 799-806.
    22. Zhou, P. & Ang, B.W. & Poh, K.L., 2008. "A survey of data envelopment analysis in energy and environmental studies," European Journal of Operational Research, Elsevier, vol. 189(1), pages 1-18, August.
    23. Atkinson, Scott E. & Halvorsen, Robert, 1986. "The relative efficiency of public and private firms in a regulated environment: The case of U.S. electric utilities," Journal of Public Economics, Elsevier, vol. 29(3), pages 281-294, April.
    24. Wang, H. & Zhou, P. & Zhou, D.Q., 2013. "Scenario-based energy efficiency and productivity in China: A non-radial directional distance function analysis," Energy Economics, Elsevier, vol. 40(C), pages 795-803.
    25. Burki, Abid A. & Khan, Mahmood-ul-Hasan, 2004. "Effects of allocative inefficiency on resource allocation and energy substitution in Pakistan's manufacturing," Energy Economics, Elsevier, vol. 26(3), pages 371-388, May.
    26. Yiping Huang & Bijun Wang, 2010. "Cost Distortions and Structural Imbalances in China," China & World Economy, Institute of World Economics and Politics, Chinese Academy of Social Sciences, vol. 18(s1), pages 1-17.
    27. Khademvatani, Asgar & Gordon, Daniel V., 2013. "A marginal measure of energy efficiency: The shadow value," Energy Economics, Elsevier, vol. 38(C), pages 153-159.
    28. Christopoulos, Dimitris K. & Tsionas, Efthymios G., 2002. "Allocative inefficiency and the capital-energy controversy," Energy Economics, Elsevier, vol. 24(4), pages 305-318, July.
    29. Khiabani, Nasser & Hasani, Karim, 2010. "Technical and allocative inefficiencies and factor elasticities of substitution: An analysis of energy waste in Iran's manufacturing," Energy Economics, Elsevier, vol. 32(5), pages 1182-1190, September.
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    More about this item

    Keywords

    Energy savings potential; Shadow price model; Allocative efficiency; Energy price distortion;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes
    • Q48 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Government Policy

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