Economics of wildfire suppression: Estimation of drivers of suppression expenditure and Risk preference experiments with wildfire management

• Author / Creator

  Huang, Michael S.

• Public wildfire management agencies are presented with a momentous responsibility: to protect life, property and infrastructure from the devastation of wildland fire, while operating at a level of expenditure justifiable to taxpayers. At a time when climate change drives more extreme fire behaviour, agencies such as Alberta Wildfire must also be prepared to respond despite uncertain budgets. This thesis contributes by offering new empirical insights on wildfire suppression through two directions. Firstly, Chapter 2 focuses on the drivers of wildfire suppression costs. An empirical model seeks to explain how costs are affected by a series of environmental factors, such as time-variant weather variables and time-invariant landscape characteristics, as well as by operational policy variables based on Alberta Wildfire’s organizational capacity and priorities. Results from regression analysis and machine learning show that while policy decisions have measurable impacts on abating costs, the bulk of expenditures is driven by environmental factors. Chapter 3 studies risk aversion of Alberta Wildfire Incident Commanders (ICs). Through laboratory economic experiments, I seek to determine whether ICs, who are in a risky profession, exhibit risk preferences in laboratory experiments that are different from a control group. Results show that ICs’ experiment choices are not significantly different from those of typical experimental subjects across all risk elicitation tasks. However, among their colleagues, ICs with additional operational deployment experience tend to exhibit significantly lower levels of risk aversion. Findings from this study motivate further research into wildland firefighters’ risk preferences that will help decisionmakers better understand how individual risk perceptions impact resource allocation, and by extension, costs. Taken together, the novel insights generated from this thesis contribute to the multi-disciplinary field of wildfire suppression research.

• Subjects / Keywords

  • wildfire
  • experimental economics
  • machine learning
  • risk aversion
  • risk preferences
  • economic experiments
  • wildfire suppression
  • wildfire management
  • wildfire costs

• Graduation date

  Spring 2022

• Type of Item

  Thesis

• Degree

  Master of Science

• DOI

  https://doi.org/10.7939/r3-c850-t225

• License

  This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.