Document


Title

A stochastic programming model with endogenous uncertainty for incentivizing fuel reduction treatment under uncertain landowner behavior
Document Type: Journal
Author(s): Tanveer Hossain Bhuiyan ; Maxwell C. Moseley ; Hugh R. Medal ; Eghbal Rashidi ; Robert K. Grala
Publication Year: 2019

Cataloging Information

Keyword(s):
  • decision making
  • fire management
  • fire spread
  • fuel reduction treatments
  • land management
  • landowners
  • Monte Carlo simulations
  • natural resources
  • predictive modelling
  • risk reduction
  • stochastic programming
  • wildfire hazard
Record Maintained By:
FRAMES Staff; catalog@frames.gov
Record Last Modified: April 11, 2019
FRAMES Record Number: 57665

Description

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.

Online Link(s):
Citation:
Bhuiyan, Tanveer Hossain; Moseley, Maxwell C.; Medal, Hugh R.; Rashidi, Eghbal; Grala, Robert K. 2019. A stochastic programming model with endogenous uncertainty for incentivizing fuel reduction treatment under uncertain landowner behavior. European Journal of Operational Research 277(2):699-718.