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Analysis of U.S. residential wood energy consumption: 1967–2009

Author

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  • Song, Nianfu
  • Aguilar, Francisco X.
  • Shifley, Stephen R.
  • Goerndt, Michael E.

Abstract

The residential sector consumes about 23% of the energy derived from wood (wood energy) in the U.S. An estimated error correction model with data from 1967 to 2009 suggests that residential wood energy consumption has declined by an average 3% per year in response to technological progress, urbanization, accessibility of non-wood energy, and other factors associated with a time trend such as increasing income per capita and number of houses. But the rising price of non-wood energy has had a positive effect on the consumption and offset the downward trend effect in the last decade. Residential wood energy consumption has also been sensitive to changes in wage rate in both long-run and short-run, but the total estimated wage rate effect since 1967 is negligible. Wood energy is expected to continue to account for a small share of residential energy consumption unless public policies improve wood energy cost competitiveness relative to non-wood energy.

Suggested Citation

  • Song, Nianfu & Aguilar, Francisco X. & Shifley, Stephen R. & Goerndt, Michael E., 2012. "Analysis of U.S. residential wood energy consumption: 1967–2009," Energy Economics, Elsevier, vol. 34(6), pages 2116-2124.
  • Handle: RePEc:eee:eneeco:v:34:y:2012:i:6:p:2116-2124
    DOI: 10.1016/j.eneco.2012.03.004
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    References listed on IDEAS

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    Cited by:

    1. Damette, Olivier & Delacote, Philippe & Lo, Gaye Del, 2018. "Households energy consumption and transition toward cleaner energy sources," Energy Policy, Elsevier, vol. 113(C), pages 751-764.
    2. Daigneault, Adam J. & Sohngen, Brent & Kim, Sei Jin, 2016. "Estimating welfare effects from supply shocks with dynamic factor demand models," Forest Policy and Economics, Elsevier, vol. 73(C), pages 41-51.
    3. Kialashaki, Arash & Reisel, John R., 2013. "Modeling of the energy demand of the residential sector in the United States using regression models and artificial neural networks," Applied Energy, Elsevier, vol. 108(C), pages 271-280.

    More about this item

    Keywords

    Woody biomass; Energy consumption; Cointegration;

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • N72 - Economic History - - Economic History: Transport, International and Domestic Trade, Energy, and Other Services - - - U.S.; Canada: 1913-
    • Q23 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Renewable Resources and Conservation - - - Forestry
    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices
    • Q42 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Alternative Energy Sources
    • R15 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Econometric and Input-Output Models; Other Methods

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