A sweeping new study published in Science finds that forest thinning and prescribed burns prevented $2.8 billion in wildfire damages across the western U.S. — returning roughly $3.75 for every dollar spent on prevention.
Thinning forests and conducting controlled burns before wildfires strike is not just effective — it’s one of the most cost-efficient investments the government can make, according to a major new study led by researchers from the University of California, Davis.
The team analyzed nearly 300 wildfires that swept through USDA Forest Service fuel-treated areas across 11 western states between 2017 and 2023. Their findings: every dollar spent on preventive fuel treatments returned approximately $3.75 in avoided wildfire damages, adding up to $2.8 billion in total losses prevented.
The study, published May 7 in Science, is the first large-scale economic assessment of Forest Service fuel treatments in the West that draws directly from real wildfire data rather than relying solely on simulation models — giving the findings an unusual level of empirical grounding.
“Fuel treatments and forest management are critically underfunded public goods,” lead author Frederik Strabo, a postdoctoral scholar in the Department of Agricultural and Resource Economics at UC Davis, said in a news release. “Our results suggest that when fewer resources are available to agencies like the Forest Service, more of the economic burden of wildfires falls on the public.”
How Treatments Changed Fire Behavior
To isolate the effect of fuel treatments, researchers compared how wildfires behaved when they entered treated versus untreated forest areas, while controlling for weather conditions and firefighting suppression efforts. The results were striking.
Fires were more than 13 percentage points less likely to keep spreading after reaching a treated area. High-severity fires — those that kill more than 75% of the tree canopy — were reduced by 20% to 35% in treated zones. Across all fires studied, treatments cut total burned area by 36%, or about 152,000 acres, compared to a scenario without any fuel management.
Not all treatment methods performed equally. Prescribed burning proved significantly more effective than mechanical thinning alone at curbing wildfire spread. Treatment size also mattered: landscape-scale treatments covering more than 2,400 acres delivered the greatest reductions in fire spread.
A Breakdown of the $2.8 Billion in Avoided Losses
The researchers tallied damages across three categories: structural losses, carbon emissions, and the health impacts of wildfire smoke. The avoided costs broke down as follows:
More than 4,000 buildings were saved from destruction, preventing roughly $895 million in property losses. Treatments also prevented the release of 2.7 million tons of CO2, avoiding approximately $503 million in climate-related costs. Perhaps most significantly, reducing the 25,757 tons of fine particulate matter that would have entered the air is estimated to have prevented 59 premature deaths and saved $1.39 billion in health and productivity costs.
Why It Matters for Public Policy
The findings arrive at a pivotal moment for federal forest management. Wildfire damages in the United States are already estimated to total $185 billion to $540 billion annually, a figure expected to climb as climate change intensifies fire conditions across the West.
Strabo argues that current funding for preventive forest management falls well short of what the evidence demands.
“Wildfire policy has historically focused on suppression, but our results suggest greater investment in prevention could substantially reduce wildfire damages,” Strabo said. “That will become even more important as the climate continues to change and forests face more large wildfires and other disturbances.”
Co-authors of the study include Matthew N. Reimer, an associate professor in UC Davis’s Department of Agricultural and Resource Economics, and Calvin Bryan, an assistant professor of economics at Washington and Lee University.
Source: University of California, Davis
