A transparent, primary-source method for scoring utility-scale solar permitting risk across all 3,143 U.S. counties — what we measure, how it's weighted, and how a 0–100 score becomes a letter grade.
Updated May 2026 · Reviewed quarterly · When the model changes, every historical score is recalculated so trends stay comparable.
0–100 scoreLower is better — a weighted blend of four factors.
A–F gradeDecision-useful bands, from 0–30 (A) to 67+ (F).
Primary-sourcedEvery published figure verified against the government document.
Quarterly review8 quarters of score history on National & higher plans.
The composite score
Every county receives a risk score from 0 to 100, where lower is better for solar developers. It's a weighted blend of four factors — the weights below sum to 100%.
33% Compliance
24% Trajectory
23% Saturation
20% Uncertainty
33%
Compliance Stringency
Setbacks, acreage caps, SUP requirements, and bans — plus a permit-record adjustment from actual approvals and denials.
24%
Regulatory Trajectory
The direction of change over the past 12 months — tightening, stable, or loosening.
23%
Market Saturation
Installed MW, land coverage, and developer density relative to county size.
20%
Data Uncertainty
Regulatory risk and our confidence in the data. A deliberately lower weight reduces the "state-halo effect."
Grade scale
Lower number = lower permitting risk. A score of 0 is the best possible outcome (Grade A); above 66 is Grade F. This is the opposite of academic grading — the lower the score, the more permitting-friendly the county.
A
0–30 · Low
B
31–42 · Moderate
C
43–52 · Elevated
D
53–66 · High
F
67+ · Very High
v2.4 recalibration (May 2026). Cutoffs tightened from the original 35/47/58/72 to 30/42/52/66. Two findings drove it: (a) the score distribution was compressed — roughly 65% of counties fell into the B band, washing out useful differentiation; and (b) community-sentiment and active-opposition signals were under-weighted relative to their real effect on project timelines. The underlying 0–100 score is unchanged — the new bands just produce a more decision-useful letter.
What changed in v2.4
Four changes came out of the May 2026 model review.
Hybrid weight formula
The original documented weights (35/30/25/10) under-emphasized permit-approval history, while the live weights over-emphasized data uncertainty — creating a "state-halo effect" where counties in strong-RPS states looked safer than their actual approval records warranted. The new whole-number formula — 33% Compliance + 24% Trajectory + 23% Saturation + 20% Uncertainty — restores saturation's weight and moderates uncertainty to fix both.
Permit-record adjustment (Bayesian smoothing)
We compute each county's approval rate from recorded utility-scale permit decisions, adding a penalty below 30% or a small bonus above 80%. For counties with sparse history (0–2 decisions), the observed rate is blended with the state's base rate using Beta-Binomial smoothing — every county starts from its state average and moves toward its own record as decisions accumulate. Each output carries a confidence level: high (≥3 decisions), moderate (1–2), or preliminary (0, shown with a "Preliminary score" badge). It moves grades toward actual board behavior without ever treating a quiet county as if it had rejected solar.
Moratorium hard cap
Any county with an active utility-scale solar moratorium is scored at the worst-possible value (100, Grade F) regardless of its sub-scores. An active moratorium means the site is functionally closed — the underlying numbers don't override that.
Two context badges
Shown next to the grade but kept separate from the score, so a weak county in a strong-policy state still reads as weak. State policy (Favorable / Mixed / Hostile) reads the state's RPS framework. Site constraints (Light / Moderate / Heavy) flags conservation easements, prime farmland, federal-land overlays, and FEMA floodplain — and only appears when we have constraint data; no badge means unknown, not unconstrained.
Worked example — Mecklenburg County, VA
How the four factors combine into a score of 84.0 (Grade F) for Mecklenburg County, Virginia (FIPS 51117) — well into the "Very High Risk" band, above the F threshold of 67.
Compliance Stringency · 33% · Very high
The Board of Supervisors voted unanimously on Apr 14, 2025 to remove utility-scale solar as a permitted use in all zoning districts — SitePath classifies this as a functional ban. Setbacks range 50–300+ ft. Sources: board minutes, Apr 14 2025; Article 20 of the county zoning ordinance.
Market Saturation · 23% · Very high
Three large projects (Seven Bridges 80 MW, Antlers Road 90 MW, Finneywood 97 MW) are permitted and proceeding. High installed MW relative to land area pushes saturation above average, capping viability beyond the grandfathered pipeline.
Regulatory Trajectory · 24% · Worsening
A unanimous April 2025 board vote reversed prior permissiveness. The 12-month direction is strongly negative with no signals of reversal — near the maximum for this factor.
Data Uncertainty · 20% · Low (well-documented)
Board minutes, ordinance text, and EIA project records are all verified, so the uncertainty penalty is minimal — the score reflects actual conditions, not estimates. Confidence: high (≥3 decisions on record).
The four weighted sub-scores combine to 84.0. The moratorium hard cap doesn't apply here — the board removed the use by right but didn't formally designate a moratorium — so the composite is used as-is → Grade F (threshold >66). Open the full county record →
Data sources
Monitoring and discovery sources. Every published data point is verified against the primary government document before it ships.
Primary documents anchor every score. Direct county-government URLs — ordinance text, board minutes, zoning amendments — sit beneath each grade, and all source links appear in each county's data panel.
Update cadence
Active moratoria are updated within 24 hours of filing. Ordinance changes are typically reflected within one week. Full scoring-model reviews run quarterly, and 8 quarters of score history are available on National and higher plans.
Explore the data
See how your target counties score — every grade is sourced to the underlying primary document.