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Long-run estimates of interfuel and interfactor elasticities

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  • Chunbo Ma

    (School of Agricultural and Resource Economics, University of Western Australia)

  • David I. Stern

    (Crawford School of Public Policy, The Australian National University)

Abstract

Meta-analyses of interfuel and capital-energy elasticities of substitution show that elasticity estimates are dependent on the type of data – time series, panel, or cross-section – and the estimators used. Econometric theory suggests that the between estimator might generate the best estimates of long-run elasticities but no existing estimates of elasticities of substitution have used it. Alternatively, Chirinko et al. argued in favor of estimating long-run elasticities of substitution using a long-run difference estimator. We provide estimates of China’s interfuel and interfactor elasticities of substitution using the between and long-run difference estimators. To address potential omitted variables bias, we add province level inefficiency and national technological change terms to our regression model. The results show that demand for coal and electricity in China is very inelastic, while demand for diesel and gasoline is elastic. With the exception of gasoline and diesel, there are limited substitution possibilities among the fuels. Substitution possibilities are greater between energy and labor than between energy and capital. The results are quite different to some previous studies for China but coincide well with the patterns found in meta-analyses for long-run estimates of elasticities of substitution.

Suggested Citation

  • Chunbo Ma & David I. Stern, 2016. "Long-run estimates of interfuel and interfactor elasticities," CCEP Working Papers 1602, Centre for Climate & Energy Policy, Crawford School of Public Policy, The Australian National University.
  • Handle: RePEc:een:ccepwp:1602
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    Blog mentions

    As found by EconAcademics.org, the blog aggregator for Economics research:
    1. Long-run Estimates of Interfuel and Interfactor Elasticities
      by noreply@blogger.com (David Stern) in Stochastic Trend on 2016-01-20 16:11:00
    2. Annual Review 2016
      by noreply@blogger.com (David Stern) in Stochastic Trend on 2016-12-26 17:08:00

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    6. Wang, H. & Zhou, P., 2018. "Multi-country comparisons of CO2 emission intensity: The production-theoretical decomposition analysis approach," Energy Economics, Elsevier, vol. 74(C), pages 310-320.
    7. Huntington, Hillard G. & Barrios, James J. & Arora, Vipin, 2019. "Review of key international demand elasticities for major industrializing economies," Energy Policy, Elsevier, vol. 133(C).
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    9. Zachlod-Jelec, Magdalena & Boratynski, Jakub, 2016. "How large and uncertain are costs of 2030 GHG emissions reduction target for the European countries? Sensitivity analysis in a global CGE model," MF Working Papers 26, Ministry of Finance in Poland.
    10. Weiwei Xiong & Liang Yan & Teng Wang & Yuguo Gao, 2020. "Substitution Effect of Natural Gas and the Energy Consumption Structure Transition in China," Sustainability, MDPI, vol. 12(19), pages 1-20, September.
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    15. Magdalena Zachlod-Jelec & Jakub Boratyński, 2016. "How large and uncertain are costs of 2030 emission reduction target for the European countries? Sensitivity analysis in a global CGE model," EcoMod2016 9449, EcoMod.
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    Keywords

    energy; substitution; elasticity; demand; China;
    All these keywords.

    JEL classification:

    • D24 - Microeconomics - - Production and Organizations - - - Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity
    • Q40 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - General

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