Encyclopedia of Wildfires and Wildland-Urban Interface (WUI) Fires

Living Edition
| Editors: Samuel L. Manzello

Cost of Suppression

  • Veronique FlorecEmail author
  • Matthew P. Thompson
  • Francisco Rodríguez y Silva
Living reference work entry
DOI: https://doi.org/10.1007/978-3-319-51727-8_96-1



Cost of suppression is defined as the money spent in suppression activities. Suppression is defined as the activities aimed at restricting the spread of a wildfire after its detection. The term suppression is broadly defined to encompass all activities aimed at putting the fire out and minimizing the area burned.


Suppression, or firefighting, is a fire agency’s response to a wildfire using a range of resources to limit its spread. Other expenditures incurred prior to the start of the fire (e.g., land management planning, fuel treatments, prepositioning of firefighting resources, detection systems, etc.) are considered to be part of the presuppression stage (Gebert et al. 2008). The type of resources needed for suppressing a wildfire and how long they are used for depend on where the fire occurs (e.g., close or far away from human habitations), what assets are located close to the ignition point (e.g., critical infrastructure, habitat for threatened species, towns), time of the year, weather conditions, and the predicted rate of spread (Katuwal et al. 2017).

The costs of suppression are a function of the amount and type of resources used over the duration of the fire. These may include money spent on firefighting equipment, vehicle or machinery use/hire and maintenance, aircraft use/hire and maintenance, salaries, payroll overheads, staff costs (e.g., travel, accommodation, and food costs), overtime, contracts paid to external organizations, and any other expenditures associated with the suppression of the fire. There are a range of responses to wildfires, all of which incur costs, but their implementation is contextual, and how frequently they are used may depend on the policy in effect, the fire agency’s capacity, the value of assets at risk, and other factors (Canton-Thompson et al. 2008; Calkin et al. 2013; Thompson 2014). Fire management policies vary substantially across and within countries. And although similar principles apply with regard to the protection of life and property, the way suppression resources are budgeted, allocated, and deployed can vary dramatically from one fire-prone region to another.

The costs of suppression have been the subject of extensive research and have increasingly been the focus of fire economists because of the dramatic increase in suppression expenditures in many countries since the mid-1980s, including the USA (Thompson et al. 2013a; Hand et al. 2016), Australia (Morgan 2009), Canada (Flannigan et al. 2005), and countries in Europe and Africa (Williams et al. 2011). Some of the questions that researchers have considered include (1) the likelihood of success of initial attack and (2) the drivers of suppression costs. The next section, “Different Suppression Resources: Types, Uses, and Costs”, provides a general overview of different resources used for suppression and how they affect total suppression costs. Sections “Initial Attack vs. Extended Attack” and “Drivers of Suppression Costs” address the two research questions above. Section “Suppression Cost Examples” presents examples from Australia, the USA, and Spain. Finally, section “Challenges and Concerns” outlines some of the key challenges for the economics of wildfire suppression.

Different Suppression Resources: Types, Uses, and Costs

Ground Resources

The costs of ground resources vary with size. Usually the larger the resource, the more expensive it is to operate. Fire trucks are usually the least expensive resource to operate in terms of costs per hour. Machinery or earthmoving equipment (i.e., front loaders and bulldozers) are generally more expensive to operate than fire trucks but remain within the same order of magnitude (approx. USD 100 to 400 per hour) (DFES 2017; FAMWEB 2018). In addition to hourly rates, there are travel costs and standby costs (i.e., the costs of having the resource ready and waiting to be deployed if needed). Travel costs range within a few dollars per kilometer, while standby costs range between USD 200 to 1,300 per day for fire trucks and USD 500 to 2,000 per day for earthmoving equipment (DFES 2017; FAMWEB 2018). How, when, and where ground resources are used depend on the location of the fire and the characteristics of the nearby landscape, the policies in place within fire agencies, weather conditions, and the availability of different resources.

Ground resources also include hand crews (i.e., firefighters), who operate the machinery and firefighting equipment. Firefighters can either be government employees with permanent positions (career firefighters) or people in the community with firefighting training that form part of a local fire brigade (volunteer firefighters). Depending on whether an agency uses mostly career or volunteer firefighters, suppression labor costs are very different, career firefighters being a lot more expensive. Because of sparse human settlement outside Australian capital cities, Australian communities rely heavily on volunteer firefighters (McLennan et al. 2009), and only about 7% of the 235,000 firefighters in the country are career firefighters (SCRGSP 2015). As a result, firefighting labor costs are less expensive in Australia compared to other countries. In contrast, US fire agencies rely more heavily on career firefighters and sustain higher suppression labor costs. About 30% of the country’s 1.2 million local firefighters are career firefighters, many of whom have wildland fire suppression duties (Haynes and Stein 2017).

Given the significant difference in labor costs between volunteer and career firefighters, some authors (e.g., Donovan 2006) have investigated the optimal number of volunteer or career firefighters an agency should have. The optimal number of each type depends on the severity of the fire season and the flexibility in ordering additional contract crews when needed (Donovan 2006). However, optimal numbers are sensitive to prior assumptions about fire season severity and whether there is other work available for career firefighters on days with no fire events.

Aerial Resources

Aerial resources are the most expensive resource, costing in the order of thousands or tens of thousands of dollars per hour to operate (approx. USD 1,000 to 12,000 per hour) (Thompson et al. 2013b; DFES 2017). The cost of aerial resources varies significantly depending on the type of resource (fixed wing aircraft vs. helicopter) and the size or water lifting capacity of the resource. In addition, the total cost of aerial resources may be substantially increased by standby costs, which are much higher for aerial resources than for ground resources (approx. USD 1,000 to 20,000 per day).

Aerial resources perform tasks such as ferrying crew and equipment, reconnaissance, and dropping retardant to slow the spread of the fire. An advantage of aerial resources is their ability to rapidly reach fires, and for this reason, they may be activated early to manage the fire and minimize its spread while ground resources get to it. However, even though their impact on fire spread during extreme weather conditions may be minimal (Plucinski et al. 2007; Calkin et al. 2014a), they are often used for extended periods of time in large fires with the aim to protect high value assets (Thompson et al. 2013b; Calkin et al. 2014b).

Initial Attack vs. Extended Attack

Suppression is best seen as a continuum where managers select the amount and type of suppression resources in response to the changing fire environment. Fire managers will typically order additional suppression resources with increasing fire size or increasing damage potential. For a variety of reasons, however, suppression is usually divided into two phases: initial attack and extended attack. Many fire management agencies establish targets and measure performance on the basis of initial attack. Agencies also often plan a “standard response” for initial attack, i.e., a common package of suppression resources in response to detected ignitions, whereas managers may use a wide variety of resource packages for extended attack (Thompson et al. 2013b; Stonesifer et al. 2016; Katuwal et al. 2017).

Initial attack is defined as the beginning of suppression activities, i.e., the actions carried out by the first resources to arrive at the fire. The first resources deployed to the fire are generally trying to contain a relatively small fire; thus suppression costs for initial attack tend to be relatively low. Most fires are stopped at the initial attack phase and the area burned is kept relatively small. However, when a fire cannot be extinguished by initial attack resources, the fire is considered to have escaped, and suppression activities go into the next phase, namely, extended attack. Several interacting factors affect the likelihood of success of initial attack, including weather conditions, fuel moisture, fuel load, vegetation type, fire danger index, distance to population centers, and initial fire area (Arienti et al. 2006; Plucinski 2012, 2013). In severe or more extreme fire conditions, the likelihood of suppression success is lower, and thus more resources may be deployed during the initial attack phase.

When fires escape initial attack and enter the extended attack phase, they are much more difficult to control, their size can extend to very large areas (Liang et al. 2008), and they have the potential for a much longer duration, especially in areas with vast wildlands with continuous fuels to burn (Plucinski 2013; Christman and Rollins 2015). During extended attack, more resources are deployed, often resulting in a large number of firefighters, fire trucks, machinery, evacuation personnel, and large aerial resources operating concurrently. This generates a heavy load in terms of operations, planning, logistics, and analytics and also requires the coordination between several government agencies (e.g., roads management, power distribution, police, etc.). Consequently, suppression costs for extended attack are usually significantly higher.

Drivers of Suppression Costs

Suppression costs generally account for more than half of total wildland fire management costs (SCRGSP 2015; Forest Service 2017). Given the considerable weight of suppression costs in wildfire management and their dramatic increase over the last two decades, a large body of research has focused on investigating the key drivers of suppression costs and what has contributed to their increase over time (Donahue 2004; Calkin et al. 2005; Gebert et al. 2007; Canton-Thompson et al. 2008; Donovan et al. 2008, 2011; Liang et al. 2008; Prestemon et al. 2008; Prestemon and Donovan 2008; Abt et al. 2009; Gebert and Black 2012; Gude et al. 2013; Thompson et al. 2013b; Hand et al. 2017).

One of the main drivers of suppression costs is fire size. The larger the fire, the longer it lasts and the more resources are needed to contain it. Fire size alone has been found to explain between 25% and 70% of suppression costs (Calkin et al. 2005; Gebert et al. 2007; Liang et al. 2008). And as annual area burned has increased and become more erratic since the 1990s, so have annual suppression costs (Calkin et al. 2005). Another important driver of suppression costs is private land or proximity to houses (Gebert et al. 2007; Liang et al. 2008; Gude et al. 2013). Fire managers deploy more resources when a fire approaches houses in an effort to protect life and property. Models predicting suppression expenditures have estimated that the percentage of private land burned explains about 58% of variation in suppression expenditures (Liang et al. 2008). Other models have estimated that a 1% change in the number of houses close to the fire increases daily suppression costs by 7% (Gude et al. 2013). In fact, it has been found that when fire size and private land are considered together, other variables have little effect on suppression expenditures (Liang et al. 2008).

Other factors have also been shown to influence suppression costs, including the suppression strategy implemented, fire agency management style, fire managers’ risk aversion, media and political pressure, and fuel and terrain conditions. For instance, after controlling for a variety of fire and landscape characteristics, Hand et al. (2017) found that some incident management teams (IMTs) in the USA implement suppression strategies that consistently use more suppression resources compared to other ITMs. Differences between ITMs’ suppression strategies account for ∼14% of variation in resource use. Gebert and Black (2012) also showed that management strategies affect suppression costs. Donahue (2004) showed that managerial factors, such as the degree of participatory management, the amount of firefighting training conducted, and the use of cost and performance measures, also influence suppression costs.

Increased risk aversion among fire managers has also contributed to the increase in suppression costs (Canton-Thompson et al. 2008) along with increased newspaper coverage of wildfires and greater political pressure (Donovan et al. 2011). Calkin et al. (2013) showed that, all else being equal, managers actually prefer higher cost strategies in light of social and institutional expectations and tend to use more expensive resources (e.g., aerial resources). Thus social and political pressures promote more aggressive wildfire management strategies (Donovan and Brown 2005, 2007).

Extreme terrain conditions may require the use of more expensive (aerial) resources or additional equipment for firefighters to be able to get to the fire, effectively increasing suppression expenditures (Gebert et al. 2007). Fuel type can also affect suppression expenditures. In general, fires starting in pine forests are more expensive than fires starting in other fuel types (Gebert et al. 2007).

Research shows that the increasing trend in suppression costs is likely to continue. Increases in the number of extreme weather days due to climate change is likely to result in more intense fires that will pose a serious challenge to fire agencies (Bowman et al. 2009; Barbero et al. 2015). Population growth in the wildland urban interface (WUI) may also contribute to further increases in suppression costs (Mell et al. 2010). Today, fires that do not involve people and property are rare (Thomas and Butry 2014), and since proximity to the WUI has been shown to influence suppression costs, some authors have hypothesized that the expansion of the WUI will continue to challenge fire managers and their budgets (Stockmann et al. 2010; Chas-Amil et al. 2013).

Suppression Cost Examples

In this section, some examples are presented that illustrate the similarities/differences in suppression policies and decision-making, suppression resource types and use, and suppression costs in three different fire-prone environments: Western Australia, the USA, and Spain.

Western Australia

The state of Western Australia (WA) is an interesting example because of its diverse vegetation, its size, and its small pockets of highly populated areas surrounded by large (mostly) uninhabited areas. Most suppression efforts and expenditures are concentrated in the southwest of WA, where the vast majority of the population is located. Fire management in this area is commonly characterized by early detection systems, rapid initial attack, and the application of prescribed burning during the cooler months of the year (Burrows and McCaw 2013).

In WA, fire location has a significant influence on the type of resources first sent to a fire, thereby affecting suppression costs. If a fire starts in the WUI (threatening human life and property), it automatically triggers the activation of aerial resources, which are left on standby until their deployment is deemed necessary. However, if a fire starts in rural areas, usually small (less expensive) units are dispatched to determine whether additional resources are needed. More recently, however, aerial resources have been increasingly used in many rural areas for initial attack (Plucinski and Pastor 2013).

Suppression expenditures in the southwest of WA have increased substantially in recent years (see Fig. 1), mostly due to (a) population growth, fires are more likely to happen close to population centers, intensifying suppression activities to protect people and property; (b) increasingly fragmented landscapes, making suppression strategies more complex and more difficult to implement (Burrows and McCaw 2013); (c) climate change, causing decreases in average annual rainfall and increases in temperature in the region (McCaw 2013); (d) aggressive suppression strategies, resulting in fuel accumulation that causes more intense wildfires (Williams et al. 2011); and (e) greater use of aerial resources, partly due to community pressures and expectations (Morgan 2009).
Fig 1

DBCA bushfire suppression costs 2003/2004 to 2012/2013. (Source: DPaW 2014)


In the USA, the costs of wildfire suppression have increased remarkably in the past decades. This is particularly true for the U.S.D.A. Forest Service, which manages over 78 million hectares of national forests and grasslands, and is responsible for the majority of federal expenses on wildfire suppression (Hand et al. 2014). Over time, the escalating costs of suppression have resulted in reduced agency budgets for other programs, such as vegetation and watershed management, roads and trail maintenance, and land and fire management planning (Forest Service 2017). As a result, there has been a growing interest in determining ways to reduce suppression costs. One area of focus has been examining how fire managers make decisions surrounding suppression resource use. Based on this body of research, some authors have recommended that agencies strengthen incentives for cost containment, in part to counterbalance sociopolitical pressures that can push managers toward more aggressive suppression strategies (e.g., Donovan and Brown 2005; Donovan et al. 2011; Thompson et al. 2013a, 2015).

Increasingly, policymakers and managers have focused on how preventative management may reduce future costs, particularly in the realm of fuels and vegetation management. Several model-based studies have demonstrated the potential for fuel treatments to reduce future suppression costs, based on changes in factors such as fire size and burn severity (Thompson et al. 2013c; Taylor et al. 2015; Barnett et al. 2016). However, due to the relative rarity of wildfire in any given place at any given time, the rate at which fuel treatments encounter wildfires can be very low (Barnett et al. 2016; Thompson et al. 2017). The lower the odds a fuel treatment strategy will ever interact with fire, the lower the odds the upfront costs of treatment will be offset by future savings (Thompson and Anderson 2015). Further, fire economics theory does not necessarily indicate that increasing fuel treatment investments will always lead to reducing suppression costs. For example, treated areas may increase the marginal effectiveness of suppression, leading managers to invest in more suppression effort and thereby increasing cost (Rideout et al. 2008). Recent empirical research suggests this may be the case, finding that daily management costs increased when fires burned into previously treated areas (Naughton and Barnett 2017).

Although the costs of suppression remain important to the U.S.D.A. Forest Service, the agency’s focus in recent years has evolved from better managing costs to better managing firefighter exposure. This shift in emphasis reflects the priority of protecting human life and safety, and the agency has offered a new definition of success that stresses achieving objectives with the “least exposure necessary” (Forest Service 2017). Thus fire managers need to evaluate and balance trade-offs between suppression costs, firefighter exposure, and fire impacts. To help them design suppression response strategies that minimize firefighter exposure while achieving land and fire management objectives, recent research innovations include the development of tools that help managers better identify operationally relevant features such as safety zones, egress routes, potential fire control locations, and areas of high suppression difficulty (Rodriguez y Silva et al. 2014; Campbell et al. 2017; O’Connor et al. 2017).


In the last two decades, a substantial increase in suppression costs has been observed in Spain. Rodriguez y Silva and Molina (2016) compiled data for major fires occurring in the south of Spain between 1993 and 2012 and showed that, despite significant reductions in area burned (or only moderate increases), suppression costs have more than doubled. For instance, suppression costs for a large fire in Beas in 1993 were estimated at €180,660 (in 2012 €, USD 205,750) for a fire that burned 6180 hectares, while suppression costs for a fire in Coín-Marbella in 2012 were estimated at €510,000 (USD 580,829) for a fire that burned 8230 hectares. This increase in suppression costs is largely due to an increase in the use of aerial resources, not only in terms of the number of aircrafts used per fire but also in the number of hours flown by each aircraft (Rodriguez y Silva and Molina 2016). Furthermore, the cost per hour of fire duration and the cost per hectare burned have also increased, indicating an increase in the complexity and difficulty of suppression activities for large forest fires in the last 20 years.

Today, new challenges arise for incident-level operational management due to higher levels of uncertainty associated with the more intense and severe forest fires occurring in southern Spain. Operational management tends to respond to more severe fires with higher numbers of both ground and aerial resources in an effort to reduce operational uncertainty (see Table 1). As a result, suppression costs continue to rise. However, Spain is not an isolated case. A similar situation can be observed in other fire-prone countries in the EU (Montiel-Molina 2013).
Table 1

Average suppression costs and resource use per fire size

Fire size (ha)

Average number of hours to control the fire

Average area controlled per hour (ha/h)

Average suppression costs (euro)

Average suppression costs/ha (euro/ha)

Average number of ground resourcesa

Average number of aircrafts



































































































aGround resources include fire trucks, bulldozers and fire brigades.

Challenges and Concerns

Despite numerous studies investigating the costs of suppression, there is a need for more econometric analyses to better understand the effectiveness and productivity of different suppression resources and help fire managers with the allocation of resources (Plucinski et al. 2007; Plucinski 2012; Calkin et al. 2014b; Duff and Tolhurst 2015). The effectiveness of suppression efforts in containing wildfires is still poorly understood, particularly for large wildfires (Finney et al. 2009). To advance this field of research, fire agencies need to improve their data collection and reporting standards and improve their monitoring and evaluation of suppression strategies and associated costs (Thompson et al. 2013b). Without continuous monitoring and evaluation, and better data for economic analyses, fire agencies will not have the capacity to determine what is effective other than through anecdotal accounts. For improved decision-making, this needs to be determined through data-driven evidence. The lack of incentives for fire managers to limit costs is also related to this lack of monitoring (Thompson et al. 2013a; Calkin et al. 2014a).

In recent years, the use of aerial resources in wildfire suppression has increased in many fire-prone regions (Calkin et al. 2014b), with the aim being to stop the fires as soon as possible. However, in landscapes that have evolved with frequent fire (e.g., Australian eucalypt forests, US dry pine, and mixed conifer forests), there may be some disadvantages to aiming for the immediate suppression of all fires. In these landscapes, large investments in initial attack generate immediate benefits but may result in considerable fuel buildup, leading to larger and more expensive wildfires in the future. Thus, this strategy may not generate benefits in the long term, and large expenditures in initial attack may be defeating the purpose of fire management, which is to reduce the long-term impacts of wildfires on life, property, and the environment. In some landscapes, previous wildfires reduce the ignition and spread of subsequent wildfires (Parks et al. 2016) and allowing wildfires to burn may actually reduce future suppression costs and damages (Houtman et al. 2013). However, this may not be applicable to all fire-prone landscapes, where fire may not have been as frequent in the past (e.g., Canadian boreal forests), but forest fire activity is likely to increase due to climate change (Wotton et al. 2010).

Another issue is that the economic studies that have been conducted so far have rarely been applied. In essence, they provide a motivating or guiding basis for discussion and evaluation alternatives (Thompson et al. 2017), but they have not had a significant influence on how resource mix decisions are made at the incident or strategic levels (Duff and Tolhurst 2015). For this to happen, it will be necessary for these models to be expanded from experimental to broad operational implementation and have a degree of localized measurements, especially since costs and fire behavior drivers vary greatly between jurisdictions (Duff and Tolhurst 2015). Also, despite a large body of literature looking at the optimal use of suppression resources (i.e., optimal fleet size, most efficient mix of firefighting resources to be deployed) or investigating the key factors that affect initial attack success (Donovan and Rideout 2003; Fried et al. 2006; Plucinski 2012), none of these studies have included economics in their analysis, and only a handful of papers have looked at the implications for suppression expenditures and the short- and long-term benefits generated by different resource mixes (Gebert et al. 2007; Petrovic and Carlson 2012; Calkin et al. 2014b). Therefore, there is a need to better incorporate economic efficiency criteria into optimization modelling efforts, for the research to be applied more and influence resource mix decisions.


This contribution explored the cost of wildfire suppression activities, examining the different types of resources used, the two stages of suppression, the drivers of suppression costs, and the current challenges in wildfire suppression economics research. One key observation in suppression economics research is that suppression costs have increased substantially in most fire-prone landscapes in the last two decades, mostly due to increases in the frequency and severity of large wildfires and increases in the use of aerial resources (the most expensive type of resource) to suppress these fires. As a result, this topic has been the subject of extensive research. But despite extensive research on the costs of suppression and the drivers of suppression costs, operational adoption of the frameworks and decision support systems developed remains limited. Existing models may need to be expanded to include broader operational implementation approaches before efficiency gains in suppression are observed.



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Copyright information

© This is a U.S. Government work and not under copyright protection in the U.S.; foreign copyright protection may apply 2019

Authors and Affiliations

  • Veronique Florec
    • 1
    Email author
  • Matthew P. Thompson
    • 2
  • Francisco Rodríguez y Silva
    • 3
  1. 1.Centre for Environmental Economics and PolicyThe University of Western AustraliaPerthAustralia
  2. 2.U.S.D.A. Forest Service (Rocky Mountain Research Station)Fort CollinsUSA
  3. 3.Forest Engineering Department, Forest Fire LaboratoryUniversity of CordobaCórdobaSpain

Section editors and affiliations

  • Raphaele Blanchi
    • 1
  1. 1.Land & WaterCSIROMelbourneAustralia