A stochastic programming model with endogenous uncertainty for incentivizing fuel reduction treatment under uncertain landowner behavior

Tanveer Hossain Bhuiyan, Maxwell C. Moseley, Hugh R. Medal, Eghbal Rashidi and Robert K. Grala

European Journal of Operational Research, 2019, vol. 277, issue 2, 699-718

Abstract: Reducing the potential damage caused by a wildfire is a problem of significant importance to land and fire managers. Fuel reduction treatment is a well-known method of reducing the risk of fire occurrence and spread on landscapes. However, officials seeking fuel reduction treatments on privately owned lands can only encourage it through incentive programs such as cost-share programs. This research developed a methodology that provides the basis for a decision-making tool to help managers allocate limited cost-share resources among a set of landowners to maximize wildfire risk reduction by implementing a hazardous fuel reduction treatment. A key feature of the methodology is that it incorporates uncertainty in the landowners’ decision of whether or not to implement treatment on their lands. The methodology is based on a stochastic programming model with endogenous uncertainty where the probability that a landowner accepts a cost-share offer to implement a fuel reduction treatment on their land depends on the offer amount. To estimate the probability that a landowner accepts a given cost-share offer amount, we used a predictive modeling technique to analyze landowner survey data. The results provide insight about the effects of different cost-share allocation strategies on the expected damage. Numerical experiments show that the risk-based allocation provides up to 37.3% more reduction in damage compared to other strategies that allocate equal cost-share amounts among landowners. Additionally, the results show that the solution quality is substantially sensitive to changes in the number of resource allocation levels.

Keywords: OR in natural resources; Two-stage stochastic programming; Predictive modeling; Monte Carlo simulation; Wildfire hazard (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:277:y:2019:i:2:p:699-718

DOI: 10.1016/j.ejor.2019.03.003

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