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An econometric model for ex ante prediction of wildfire suppression costs

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  • Yoder, Jonathan
  • Gebert, Krista

Abstract

This paper develops an econometric model that can provide predictions of fire suppression costs (per acre and in total) for a given large fire before final fire acreage is known. The model jointly estimates cost per acre and acreage equations via Maximum Likelihood, accounting for sample truncation based on final fire size. Formulas and results are shown for predictions of costs and fire size for wildfires in general, and for large fires in particular. Marginal effects of explanatory variables on cost and acreage are discussed. The distribution of these model predictions illustrates the importance of accounting for sample truncation when generating predicted outcomes based on ex ante information.

Suggested Citation

  • Yoder, Jonathan & Gebert, Krista, 2012. "An econometric model for ex ante prediction of wildfire suppression costs," Journal of Forest Economics, Elsevier, vol. 18(1), pages 76-89.
    • Handle: RePEc:eee:foreco:v:18:y:2012:i:1:p:76-89
    DOI: 10.1016/j.jfe.2011.10.003
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    References listed on IDEAS

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    1. Lien, Da-Hsiang Donald, 1985. "Moments of truncated bivariate log-normal distributions," Economics Letters, Elsevier, vol. 19(3), pages 243-247.
    2. Kees Jan Van Garderen & Chandra Shah, 2002. "Exact interpretation of dummy variables in semilogarithmic equations," Econometrics Journal, Royal Economic Society, vol. 5(1), pages 149-159, June.
    3. Kennedy, Peter E, 1981. "Estimation with Correctly Interpreted Dummy Variables in Semilogarithmic Equations [The Interpretation of Dummy Variables in Semilogarithmic Equations]," American Economic Review, American Economic Association, vol. 71(4), pages 801-801, September.
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    Cited by:

    1. Kim, Yeon-Su & Rodrigues, Marcos & Robinne, François-Nicolas, 2021. "Economic drivers of global fire activity: A critical review using the DPSIR framework," Forest Policy and Economics, Elsevier, vol. 131(C).
    2. Dexen DZ. Xi & C.B. Dean & Stephen W. Taylor, 2020. "Modeling the duration and size of extended attack wildfires as dependent outcomes," Environmetrics, John Wiley & Sons, Ltd., vol. 31(5), August.
    3. Michael Shucheng Huang & Bruno Wichmann, 2024. "Machine learning estimates on the impacts of detection times on wildfire suppression costs," PLOS ONE, Public Library of Science, vol. 19(11), pages 1-20, November.
    4. Hand, Michael S. & Thompson, Matthew P. & Calkin, David E., 2016. "Examining heterogeneity and wildfire management expenditures using spatially and temporally descriptive data," Journal of Forest Economics, Elsevier, vol. 22(C), pages 80-102.
    5. Dexen D. Z. Xi & Charmaine B. Dean & Stephen W. Taylor, 2021. "Modeling the duration and size of wildfires using joint mixture models," Environmetrics, John Wiley & Sons, Ltd., vol. 32(6), September.
    6. Bayham, Jude & Yoder, Jonathan K., "undated". "A Real Barn Burner: The Effectiveness of Home Protection During Wildfire," 2018 Annual Meeting, August 5-7, Washington, D.C. 274302, Agricultural and Applied Economics Association.
    7. Aric Shafran, 2016. "Urban Sprawl and the Public Provision of Fire Suppression," Working Papers 1603, California Polytechnic State University, Department of Economics.
    8. Thompson, Matthew P. & Haas, Jessica R. & Finney, Mark A. & Calkin, David E. & Hand, Michael S. & Browne, Mark J. & Halek, Martin & Short, Karen C. & Grenfell, Isaac C., 2015. "Development and application of a probabilistic method for wildfire suppression cost modeling," Forest Policy and Economics, Elsevier, vol. 50(C), pages 249-258.
    9. Hjerpe, Evan & Kim, Yeon-Su & Dunn, Leah, 2016. "Forest density preferences of homebuyers in the wildland-urban interface," Forest Policy and Economics, Elsevier, vol. 70(C), pages 56-66.

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    JEL classification:

    • Q20 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Renewable Resources and Conservation - - - General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General

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