Solution Model wildfire loss under multiple forest fuel-treatment scenarios using a catastrophe risk model modified for fuel landscape changes to quantify avoided average annual loss and net present value of mitigation. Key challenge Fuel reduction needs are vast and costly, and communities lack replicable, decision-grade quantification of avoided losses needed to prioritize and finance mitigation at scale. Context Recent wildfire seasons in the United States have produced severe impacts, including 10.2 million acres burned and an estimated $17.1 billion in losses in 2020. A century of fire suppression has increased fuel loads, while hotter and drier conditions linked to climate change exacerbate fire risk. With about 50 million homes in the wildland-urban interface and growth continuing, the scale of mitigation required is large and current public investment levels are far below estimated needs across the western U.S. Solution in practice AFF and Moody’s (formerly RMS) applied the RMS US Wildfire HD Model to Grass Valley, California (population at risk stated as 76,000) and modified the modeling to reflect changes to the fuel landscape from hazardous fuels reduction. The analysis tested eight treatment scenarios varying the share of landscape treated (3.7% to 18.4%), fuel types, and treatment directionality, then estimated impacts on average annual loss to infrastructure and the net present value of fuel modification over a 15-year period. The modeled scenarios indicated 21–59% avoided average annual loss to infrastructure and an estimated $13–$17 million net present value under certain treatment and maintenance assumptions. Impact / outcome 21-59% avoided average annual loss to infrastructure; USD 13-17m net present value over 15 years (modelled). Next steps Extend the modeling to additional asset classes (such as carbon, water quality, and human health) and incorporate changing climate conditions, while also developing a community-level financial mechanism that can fund fuel treatments at scale based on the quantified avoided-loss benefits. Organizations involved Authoring: Moody's RMS; American Forest Foundation (AFF) Implementation partners Data Providers