Port Carbon, PA
Port Carbon wildfire risk explained
USFS's Wildfire Risk to Communities model puts Port Carbon at the 51st national percentile for risk to structures, modestly above the national average for wildfire risk — a score built from 680 actual buildings. (Source: USFS's Wildfire Risk to Communities methodology.)
Fire likelihood alone (USFS's burn-probability figure) ranks Port Carbon at the 50th national percentile — 1 points below its risk-to-structures score, a gap driven by how much is actually built there.
Where Port Carbon's buildings actually sit
USFS puts 86.6% of Port Carbon's 680 buildings in the Indirect exposure zone, versus 13.4% Direct and 0% Minimal — a place where the fire doesn't need to reach the structure directly for embers to.
Where Port Carbon ranks
Within Pennsylvania, Port Carbon ranks higher (84th percentile) than it does nationally (51st) — one of the more fire-exposed places in a state that, overall, scores lower than that. Among the 31,521 US communities USFS scores, Port Carbon ranks 15,394 for wildfire risk (1 is highest) and 14,864 by building count (1 is largest). Within Pennsylvania alone, it ranks 320 of 1,991 places by risk. See the full county-by-county picture for Pennsylvania on its state page.
Shopping for coverage in Port Carbon
Port Carbon's elevated rating (51st percentile nationally) sits outside the range where wildfire risk alone reshapes an insurance market — still, a homeowner here loses nothing by comparing rates.
What would actually reduce this score
Because 86.6% of Port Carbon's buildings sit in the Indirect zone, sealing the ember pathway (vents, roofing, gutters) is the intervention this page's data actually supports — see the home-hardening guide.
Where Port Carbon's figures come from
Every one of the two percentiles behind Port Carbon's 15,394-place national rank traces to USFS's Wildfire Risk to Communities dataset — see the methodology guide. The exposure-zones guide covers what Port Carbon's dominant indirect exposure actually means, with real examples from across the dataset.