IDEAS home Printed from https://ideas.repec.org/a/spr/nathaz/v118y2023i1d10.1007_s11069-023-05997-w.html
   My bibliography  Save this article

A data-driven analysis and optimization of the impact of prescribed fire programs on wildfire risk in different regions of the USA

Author

Listed:
  • Esther Jose

    (University at Buffalo)

  • Puneet Agarwal

    (California Polytechnic State University)

  • Jun Zhuang

    (University at Buffalo)

Abstract

In the current century, wildfires have shown an increasing trend, causing a huge amount of direct and indirect losses in society. Different methods and efforts have been employed to reduce the frequency and intensity of the damages, one of which is implementing prescribed fires. Previous works have established that prescribed fires are effective at reducing the damage caused by wildfires. However, the actual impact of prescribed fire programs is dependent on factors such as where and when prescribed fires are conducted. In this paper, we propose a novel data-driven model studying the impact of prescribed fire as a mitigation technique for wildfires to minimize the total costs and losses. This is applied to states in the USA to perform a comparative analysis of the impact of prescribed fires from 2003 to 2017 and to identify the optimal scale of the impactful prescribed fire programs using least-cost optimization. The fifty US states are classified into categories based on impact and risk levels. Measures that could be taken to improve different prescribed fire programs are discussed. Our results show that California and Oregon are the only severe-risk US states to conduct prescribed fire programs that are impactful at reducing wildfire risks, while other southeastern states such as Florida maintain fire-healthy ecosystems with very extensive prescribed fire programs. Our study suggests that states that have impactful prescribed fire programs (like California) should increase their scale of operation, while states that burn prescribed fires with no impact (like Nevada) should change the way prescribed burning is planned and conducted.

Suggested Citation

  • Esther Jose & Puneet Agarwal & Jun Zhuang, 2023. "A data-driven analysis and optimization of the impact of prescribed fire programs on wildfire risk in different regions of the USA," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 118(1), pages 181-207, August.
    • Handle: RePEc:spr:nathaz:v:118:y:2023:i:1:d:10.1007_s11069-023-05997-w
    DOI: 10.1007/s11069-023-05997-w
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11069-023-05997-w
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11069-023-05997-w?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Kim, Young-Hwan & Bettinger, Pete & Finney, Mark, 2009. "Spatial optimization of the pattern of fuel management activities and subsequent effects on simulated wildfires," European Journal of Operational Research, Elsevier, vol. 197(1), pages 253-265, August.
    2. D. Evan Mercer & Jeffrey P. Prestemon & David T. Butry & John M. Pye, 2007. "Evaluating Alternative Prescribed Burning Policies to Reduce Net Economic Damages from Wildfire," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 89(1), pages 63-77.
    3. Torkayesh, Ali Ebadi & Alizadeh, Reza & Soltanisehat, Leili & Torkayesh, Sajjad Ebadi & Lund, Peter D., 2022. "A comparative assessment of air quality across European countries using an integrated decision support model," Socio-Economic Planning Sciences, Elsevier, vol. 81(C).
    4. Hunt, Kyle & Agarwal, Puneet & Zhuang, Jun, 2022. "On the adoption of new technology to enhance counterterrorism measures: An attacker–defender game with risk preferences," Reliability Engineering and System Safety, Elsevier, vol. 218(PB).
    5. James Minas & John Hearne & David Martell, 2015. "An integrated optimization model for fuel management and fire suppression preparedness planning," Annals of Operations Research, Springer, vol. 232(1), pages 201-215, September.
    6. Kumar, Shantanu & Mehany, Mohammed S.Hashem M., 2022. "A standardized framework for quantitative assessment of cities’ socioeconomic resilience and its improvement measures," Socio-Economic Planning Sciences, Elsevier, vol. 79(C).
    7. Adam Behrendt & Vineet M. Payyappalli & Jun Zhuang, 2019. "Modeling the Cost Effectiveness of Fire Protection Resource Allocation in the United States: Models and a 1980–2014 Case Study," Risk Analysis, John Wiley & Sons, vol. 39(6), pages 1358-1381, June.
    8. Nada Petrovic & David L Alderson & Jean M Carlson, 2012. "Dynamic Resource Allocation in Disaster Response: Tradeoffs in Wildfire Suppression," PLOS ONE, Public Library of Science, vol. 7(4), pages 1-9, April.
    9. Rebecca K. Miller & Christopher B. Field & Katharine J. Mach, 2020. "Barriers and enablers for prescribed burns for wildfire management in California," Nature Sustainability, Nature, vol. 3(2), pages 101-109, February.
    10. Matsypura, Dmytro & Prokopyev, Oleg A. & Zahar, Aizat, 2018. "Wildfire fuel management: Network-based models and optimization of prescribed burning," European Journal of Operational Research, Elsevier, vol. 264(2), pages 774-796.
    11. Dennis Anderson, 1972. "Models for Determining Least-Cost Investments in Electricity Supply," Bell Journal of Economics, The RAND Corporation, vol. 3(1), pages 267-299, Spring.
    12. Puneet Agarwal & Junlin Tang & Adithya Narayanan Lakshmi Narayanan & Jun Zhuang, 2020. "Big Data and Predictive Analytics in Fire Risk Using Weather Data," Risk Analysis, John Wiley & Sons, vol. 40(7), pages 1438-1449, July.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wang, Ning & Zhao, Shiyue & Wang, Sutong, 2024. "A novel clustering-based resampling with cost-sensitive boosting method to model and map wildfire susceptibility," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    2. Charles Sims & Betsy Heines & Suzanne Lenhart, 2017. "Assessing the Economic Tradeoffs Between Prevention and Suppression of Forest Fires," Working Papers 2017-05, University of Tennessee, Department of Economics.
    3. Bashiri, Mahdi & Nikzad, Erfaneh & Eberhard, Andrew & Hearne, John & Oliveira, Fabricio, 2021. "A two stage stochastic programming for asset protection routing and a solution algorithm based on the Progressive Hedging algorithm," Omega, Elsevier, vol. 104(C).
    4. Berna Tektaş & Hasan Hüseyin Turan & Nihat Kasap & Ferhan Çebi & Dursun Delen, 2022. "A Fuzzy Prescriptive Analytics Approach to Power Generation Capacity Planning," Energies, MDPI, vol. 15(9), pages 1-26, April.
    5. Axel Pierru, 2007. "Short-run and long-run marginal costs of joint products in linear programming," Recherches économiques de Louvain, De Boeck Université, vol. 73(2), pages 153-171.
    6. 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).
    7. Jude Bayham & Jonathan K. Yoder, 2020. "Resource Allocation under Fire," Land Economics, University of Wisconsin Press, vol. 96(1), pages 92-110.
    8. Madlener, Reinhard & Kumbaroglu, Gurkan & Ediger, Volkan S., 2005. "Modeling technology adoption as an irreversible investment under uncertainty: the case of the Turkish electricity supply industry," Energy Economics, Elsevier, vol. 27(1), pages 139-163, January.
    9. Tyron J. Venn & John Quiggin, 2017. "Early evacuation is the best bushfire risk mitigation strategy for south-eastern Australia," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 61(3), pages 481-497, July.
    10. Avci, Mualla Gonca & Avci, Mustafa & Battarra, Maria & Erdoğan, Güneş, 2024. "The wildfire suppression problem with multiple types of resources," European Journal of Operational Research, Elsevier, vol. 316(2), pages 488-502.
    11. Arthur Fishman & Doron Klunover, 2020. "To Act or not to Act? Political competition in the presence of a threat," Papers 2010.03464, arXiv.org, revised Nov 2020.
    12. Pelagie Elimbi Moudio & Cristobal Pais & Zuo-Jun Max Shen, 2021. "Quantifying the impact of ecosystem services for landscape management under wildfire hazard," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 106(1), pages 531-560, March.
    13. Hilsenroth, Jana & Grogan, Kelly A. & Crandall, Raelene M. & Bond, Ludie & Sharp, Misti, 2023. "Non-industrial private forest owners' preferences for fuel reduction cost-share programs in the southeastern U.S," Forest Policy and Economics, Elsevier, vol. 155(C).
    14. Benjamin A. Jones & Robert P. Berrens, 2021. "Prescribed Burns, Smoke Exposure, And Infant Health," Contemporary Economic Policy, Western Economic Association International, vol. 39(2), pages 292-309, April.
    15. repec:dui:wpaper:1305 is not listed on IDEAS
    16. Wang, Ning & Xu, Yan & Wang, Sutong, 2022. "Interpretable boosting tree ensemble method for multisource building fire loss prediction," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    17. Rashidi, Eghbal & Medal, Hugh & Gordon, Jason & Grala, Robert & Varner, Morgan, 2017. "A maximal covering location-based model for analyzing the vulnerability of landscapes to wildfires: Assessing the worst-case scenario," European Journal of Operational Research, Elsevier, vol. 258(3), pages 1095-1105.
    18. Adam Behrendt & Vineet M. Payyappalli & Jun Zhuang, 2019. "Modeling the Cost Effectiveness of Fire Protection Resource Allocation in the United States: Models and a 1980–2014 Case Study," Risk Analysis, John Wiley & Sons, vol. 39(6), pages 1358-1381, June.
    19. Hunt, Kyle & Zhuang, Jun, 2024. "A review of attacker-defender games: Current state and paths forward," European Journal of Operational Research, Elsevier, vol. 313(2), pages 401-417.
    20. Güner, Yusuf Emre, 2018. "The improved screening curve method regarding existing units," European Journal of Operational Research, Elsevier, vol. 264(1), pages 310-326.
    21. Prestemon, Jeffrey P. & Abt, Karen L. & Barbour, R. James, 2012. "Quantifying the net economic benefits of mechanical wildfire hazard treatments on timberlands of the western United States," Forest Policy and Economics, Elsevier, vol. 21(C), pages 44-53.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:nathaz:v:118:y:2023:i:1:d:10.1007_s11069-023-05997-w. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.