PermitPortal reference memo · AHJ intelligence
How jurisdiction feasibility scores are built
The evidence, rubrics, weights, arithmetic and confidence behind every AHJ score, with worked examples you can check against the public record.
Executive summary
Higher always means more favorable for development.
Each AHJ receives one composite score built from six category scores, and each category from the research flows beneath it. Every flow score is tied to cited evidence and a written rubric. Thin evidence is pulled toward neutral; missing evidence is excluded, never treated as favorable. Conditions that can stop a project cap the result after the weighted score is calculated. The weights depend on the asset class.
What is measured, and what is not
The score does not estimate how people feel. It measures what a jurisdiction has already done on the public record: how its bodies voted, what its ordinances say, whether residents organized, what they filed and said at hearings, how much comparable development it already carries, and what its grid, land and tax regime look like. Every input is a dated, citable act.
Counted
Roll-call votes and their tallies. Ordinance text, amendments and repeals. Named opposition groups, petitions, lawsuits by residents, recall efforts. Hearing turnout and public comment records. Projects operating, approved and queued. Published hazard, wetland, flood and tax data.
Not counted
Private opinion, polling, and conversations that never reach a record. A quiet board majority is invisible until it votes. A township that publishes little gets a low-confidence score, not a favorable one.
How a score is produced
Research, judgment and arithmetic are separate steps, so each can be checked on its own.
Research: collect cited evidence for each flow
A research agent works each flow separately across official records (minutes, agendas, ordinances, dockets, state filings), news and local social channels. It returns dated facts with source links, a completeness estimate and a source-quality label. Voting History and Water Sentiment use the deepest research tier, because they require reading months of minutes.
Judgment: score the evidence against a written rubric
A separate model scores each flow from the research output alone. It has no web access, so it cannot add evidence that is not cited. Each flow has anchors (for example, "0 binding ordinance constraints = 95, 1 = 80, 2 = 65"), and the scorer states which anchor it applied. The rubric changes with the asset class: for solar, only solar and comparable votes count, and the grid question is whether the project can export power, not whether the utility can serve load.
Adjust each flow for completeness
Thin evidence cannot move a score far. Each raw score is pulled toward a neutral baseline in proportion to how incomplete its evidence is. Neutral is 50 for every flow except Grid Readiness, which falls back to 35, because an absence of grid evidence is itself a warning. A flow with no score is excluded.
adjusted = neutral + completeness × (raw − neutral)Roll flows into categories, categories into the composite
Each category is a weighted average of its scored flows, and the composite a weighted average of the six categories, with weights renormalized over whatever was scored. This step is plain code, the same code that produces the number on the AHJ card.
category = Σ(adjusted × flow weight) ÷ Σ(weights of scored flows)Apply red-flag caps
A verified condition that can stop the project caps the composite; the tightest cap wins.
final = min(weighted composite, binding caps)Weights depend on the asset class
The same six categories matter differently to a data center and a solar farm. A jurisdiction researched for one asset class is researched again, on that asset's own terms, before it is scored for another.
| Category | Data center | Utility-scale solar | Battery storage | Industrial |
|---|---|---|---|---|
| Regulatory | 25% | 35% | 30% | 30% |
| Grid Readiness | 23% | 15% | 30% | 15% |
| Community | 15% | 30% | 25% | 25% |
| Water | 13% | — | — | 10% |
| Environment | 12% | 15% | 10% | 10% |
| Incentives | 12% | 5% | 5% | 10% |
Data center: The platform default. Matches the stored composite, including moratorium caps.
The six categories and their flows
Regulatory
25% of the data center composite
Can you legally build here, and how fast?
How easy the jurisdiction is to entitle in — approval route, code trajectory, entitlement speed, and development standards.
Approval Simplicity7.5%
How simple the approval pathway is: by-right vs CUP (conditional use permit) vs rezoning. Higher score = simpler, more predictable, less discretionary. By-right is fastest; discretionary approvals add political risk and time.
Policy Favorability7.5%
How favorable the legislative stance is toward DC development: recent zoning text amendments, overlay districts, streamlined permitting laws, moratoria. Higher score = more pro-development policy. An active enacted moratorium triggers a red-flag cap on the overall score.
Entitlement Velocity6.3%
How fast DC and large-industrial projects historically move from application to entitlement. Higher score = faster entitlement. Prioritizes the last 24 months; falls back to comparable-industrial estimates when DC-specific cases are thin.
Design Flexibility3.8%
How much flexibility the jurisdiction offers around physical development standards — setbacks, height limits, noise caps, landscape buffers — via PUD/PAD overlays, variance grants, and administrative relief. Higher score = more flexible for DC site layout and equipment placement.
Grid Readiness
23% of the data center composite
Can you get power, and at what cost?
Whether you can get power and at what cost — interconnection queue, energization timeline, and large-load tariff terms.
Grid Readiness13.5%
Grid readiness for new DC load: utility interconnection queue position and depth, substation / transmission proximity, energization timeline, and any queue closures. Availability and timeline only — cost is scored separately in Power Economics. Higher score = grid can energize the site on a schedule you can underwrite. Asymmetric null handling — absence of evidence counts as grid-bad.
Power Economics9%
Large-load power cost: serving-utility tariff terms (demand ratchets, minimum-bill, exit fees), pending PUC rate cases, whether a DC-specific tariff class exists, and industrial rate level versus the regional benchmark. Higher score = cheaper, more predictable power cost with less rate-case exposure.
Community
15% of the data center composite
Will stakeholders fight you?
Political and social risk — voting record, organized opposition, local pipeline saturation, and social sentiment.
Voting History5.3%
Recorded votes by governing bodies (councils, planning commissions, boards of zoning appeals) on DC and heavy industrial projects over the last 24 months. Higher score = more approvals, fewer denials. Tracks approvals, denials, continuances, tablings, and withdrawals.
Public Opposition (Data Centers)3%
Level of organized community resistance to DC projects specifically. Higher score = less opposition. Counts coordinated opposition with named groups or petitions more heavily than scattered comments.
Market Saturation3%
How crowded the local DC pipeline already is — approved/pending/operating projects, MW in the interconnection queue attributable to the corridor, and land absorption. Higher score = more headroom; lower = congestion and density-driven backlash rising (the Prince William pattern).
Social Sentiment2.3%
Net social-media sentiment about DC / heavy-industrial development in the jurisdiction: opposition artifacts (petitions, legal-defense fundraisers, Facebook/Reddit/X groups) balanced against supportive signals (pro-development groups, chamber of commerce endorsements, local jobs advocates). Higher score = more favorable sentiment.
Public Opposition (Industrial)1.5%
Level of organized opposition to other heavy industrial projects (warehouses, solar farms, fulfillment centers, biogas, etc.). Higher score = less generalized NIMBY posture. Separate signal from DC-specific sentiment.
Water
13% of the data center composite
Will the bodies that approve water use let you draw it?
Whether the bodies that approve water use will let you draw it. An enacted water moratorium caps the overall score.
Water Sentiment12.5%
Sentiment toward data center water use among the bodies that approve water service, allocation, and discharge for the jurisdiction and at the state level (water/sewer authorities, utility boards, groundwater conservation districts, councils, state water boards, environmental agencies). Reads their minutes, agendas, and hearing records over the last 24 months for approvals, denials, conditions, drought/aquifer concerns, and water moratoria. Higher score = water-approving bodies are more favorable to supplying data center water demand. An enacted water moratorium triggers a red-flag cap.
Environment
12% of the data center composite
Are there physical or environmental constraints?
Environmental constraint and hazard exposure — species, wetlands, contamination, floodplains, and natural-hazard risk.
Environmental Clearance8.1%
Likelihood of clearing environmental review without major delays. Higher score = cleaner site with fewer binding constraints. Considers endangered species, contamination, wetlands, floodplains, protected habitats, environmental justice areas, brownfield / Superfund sites, air quality non-attainment, plus analogous projects that cleared review successfully.
Hazard Exposure4.4%
Natural-hazard exposure from the FEMA National Risk Index and flood mapping — tornado/wind, riverine and flash flood, seismic, wildfire, hurricane, and winter storm. Higher score = lower hazard exposure and less insurance/hardening cost and repeated-loss risk.
Incentives
12% of the data center composite
How heavy is the tax and cost burden?
Financial load and upside — structural tax burden plus discretionary incentives.
Tax Burden6.9%
Structural tax burden on the asset class: business personal property (BPP) tax on servers and M&E, sales-and-use tax treatment of DC equipment (statutory vs negotiated), and real-property rates across the refresh cycle. Higher score = structurally lighter carrying cost.
Incentives5.6%
Economic incentives available to DC developers: tax abatements, fee waivers, infrastructure support. Higher score = more and better incentives. Categorized: Targeted → Strong General → Limited → Minimal → None.
Worked examples, flow-by-flow rubrics and an outcome check are published for utility-scale solar. Open the solar memo
A seventeenth flow, Governing Structure, records who decides (board composition, which body hears which application) and is shown on the AHJ page but never scored.
Score interpretation
The bands are descriptive labels, not probabilities of approval. Scores are built for ranking and comparing jurisdictions; a difference of a few points is not meaningful, because rubric anchors move in steps of 5.
Red-flag caps
A weighted average can hide a binary blocker. Caps stop strength in unrelated categories from averaging away a condition that can stop the project. A moratorium or prohibition caps a score only when it covers the asset class being scored: the research records what the enacting instrument actually covers, so a data center moratorium does not cap a solar score.
Confidence is separate from feasibility
Confidence says how much weight the score can bear, not whether the jurisdiction is favorable. It never changes the score. For each scored flow it multiplies three things the research reports, then averages across flows.
flow confidence = completeness × source quality × min(1, evidence items ÷ 5)High 0.70 and above
Broad, current evidence supports the scored pattern.
Medium 0.40 to 0.69
Useful directional evidence, with material gaps to review.
Low below 0.40
Treat the score as preliminary until missing evidence is resolved.
Refresh, history and reproducibility
Refresh
Jurisdictions on a watch list are re-scored every week; others are re-scored on request. Between scoring runs, monitoring reads new agendas, minutes, filings and local news and attaches dated events to the jurisdiction and to affected projects.
History
Every run is saved as a snapshot, so the trend of each flow and category can be inspected. Trends only compare snapshots researched for the same asset class; a switch from data center to solar research is a change of question, not a change in the jurisdiction.
Stability
Weekly re-runs receive the previous snapshot as context, which damps run-to-run drift: a change in a flow score should trace to a change in its evidence, and the flow summary says what the new evidence is.
Checking a score
Every flow on the AHJ page shows its raw score, completeness, summary with any arithmetic, and the source links it relied on. Any number on the card can be rebuilt by hand from those, as in the worked example above.
Limitations
The public record, not private opinion
The score sees what reaches minutes, filings, press and public social channels. It cannot see landowner negotiations, a board majority that has not voted, or sentiment nobody records.
Judgment is constrained, not removed
Rubric scoring is done by a model applying written anchors to cited evidence, with arithmetic shown. That makes each score checkable, but two runs on the same evidence can still differ by an anchor step. Confidence and the flow summaries are there to expose this.
Coarse steps
Scores are quantized: solar flows in steps of 5, data center flows on anchors of 0, 25, 50, 75 and 100. Rank and compare; do not read decimals.
Uneven records
Some jurisdictions publish far more than others. Low confidence can reflect a thin record rather than an unfavorable jurisdiction.
Current conditions
Ordinances, boards, queues and sentiment change. Check the snapshot date and re-score before a major commitment.
Jurisdiction, not site
Parcel geometry, interconnection, adjacency, project design and timing can change the result for an individual site. Site screening and due diligence cover that.