Carnot-Moon, PA
Carnot-Moon, PA's wildfire risk, in USFS's own numbers
USFS's Wildfire Risk to Communities model puts Carnot-Moon at the 26th national percentile for risk to structures, close to the middle of USFS's national wildfire-risk range — a score built from 3,562 actual buildings. (Source: USFS's Wildfire Risk to Communities methodology.)
Fire likelihood alone (USFS's burn-probability figure) ranks Carnot-Moon at the 27th national percentile — 1 points above its risk-to-structures score, a gap driven by how much is actually built there.
Where Carnot-Moon's buildings actually sit
Indirect exposure is dominant in Carnot-Moon (49.4% of 3,562 buildings): far enough from burnable vegetation to avoid flame contact, close enough for wind-blown embers. Only 38.3% sit in the Direct zone.
Carnot-Moon against the rest of the country
Carnot-Moon scores 26th nationally and 32nd within Pennsylvania — close enough that its state context doesn't change the picture the national number already gives. Among the 31,521 US communities USFS scores, Carnot-Moon ranks 23,211 for wildfire risk (1 is highest) and 4,702 by building count (1 is largest). Within Pennsylvania alone, it ranks 1,365 of 1,991 places by risk. See the full county-by-county picture for Pennsylvania on its state page.
What this risk score means for insurance
At the 26th national percentile, Carnot-Moon rates moderate for wildfire risk — well short of the threshold where insurers have been withdrawing coverage, though comparing a quote costs nothing either way.
What would actually reduce this score
Carnot-Moon's 49.4% Indirect-exposure share points at embers, not flame contact, as the main pathway — ember-resistant vents and non-combustible roofing rank ahead of defensible space here. Detail in the home-hardening guide.
Where Carnot-Moon's figures come from
The methodology guide shows exactly how USFS turned 3,562 counted buildings into the percentiles shown above for Carnot-Moon. The exposure-zones guide covers what Carnot-Moon's dominant indirect exposure actually means, with real examples from across the dataset.