Methodology

How this dataset was built, in plain English.

What we set out to do

Every U.S. local government has the legal authority to pause new development of certain kinds for a defined period. When a city council, county commission, or township board uses that authority, they typically do it through a public ordinance or resolution that is published online (sometimes), posted in a meeting agenda (often), and recorded in board minutes (almost always, eventually).

Our goal: identify every such moratorium adopted in the U.S. that targets data centers, battery storage, solar, wind, or cryptocurrency mining — and capture enough structured information about each one to support cross-jurisdictional comparison.

How we did it

Three phases.

Phase 1: Document collection

We deployed AI-assisted research agents (built on the OpenAI Codex CLI with web-search enabled) across all 50 states. Each agent operated within a single state's scope and was given a research brief for that state.

The agents searched:

• Municipal websites
• Agenda portals (Granicus, CivicEngage, Legistar, IQM2, CivicWeb, CivicClerk, etc.)
• County board meeting minutes
• Planning commission and zoning board records
• State legislative databases (LegiScan, official state legislatures)
• News archives (local TV, regional papers, trade press)
• Academic and policy research (UNC SOG, MSU Extension, NREL, EIA)
• Court records (where applicable)

Each agent was instructed to download original documents — PDFs of ordinances, HTML of agenda pages, Word documents — and save them locally with provenance metadata (source URL, download timestamp, retrieval method).

We supplemented this with a SerpAPI sweep for "<state>" "data center" moratorium and similar queries, which surfaced documents the per-state agents had missed.

Output of Phase 1: approximately 4,400 unique source documents, totaling ~12 GB, archived in their native formats. Each has a .meta sidecar JSON file recording its provenance.

Phase 2: Classification

Not every document we collected is a moratorium document. Many are project announcements, EIA reports, news articles unrelated to any specific ordinance. We classified each document with a small language model (gpt-5.4-mini at the OpenAI flex tier) using structured prompts that produced JSON-valid classifications:

• document_type: ordinance, resolution, agenda, minutes, news, report, policy_document, etc.
• subject_matter: data_center, cryptocurrency, bess, solar, wind, general_zoning, etc.
• jurisdiction: name + state of the issuing body
• is_primary_legal_source: whether the document is the operative instrument itself (vs. coverage of it)
• is_moratorium_related: yes/no
• confidence: 0.0 to 1.0

Output of Phase 2: 709 documents classified as moratorium-related across the corpus. About 1,123 of the 4,400 are primary legal sources of one kind or another.

Phase 3: Structured extraction

For each moratorium-related document, we used a larger language model (gpt-5.5 at the OpenAI flex tier) with a detailed extraction schema to produce a structured record.

The extraction schema captures 60+ fields per document, organized into five tiers that mirror the 44-clause taxonomy used in the working paper:

• Tier 1: Universal clauses (14 coded fields). Authority statement, findings (regulatory gap, threat enumeration, study intent, emergency declaration), definitions, prohibited actions, geographic scope, duration, exemptions, severability, effective date, repeal language.
• Tier 2: Common clauses (10 boolean fields). Extension mechanism, conflict-and-repeal, emergency declaration, open-meetings compliance, tolling, waiver provision, appeal process, vested-rights disclaimer, pending-application coverage, severability separately.
• Tier 3: Sector-specific clauses (12 boolean fields). Water-resource assessment, grid/energy-impact assessment, incentive guardrails, noise/generator provisions, fire-safety requirements, decommissioning bond, hazmat training, safety-incident trigger, farmland preservation, property-value guarantee, physical-hazard assessment, aviation clearance.
• Tier 4: Definitional approach. Whether the moratorium provides no definition, a functional definition, a bundled definition, or a size-threshold definition of the regulated use.
• Tier 5: Quality metadata. Confidence score, narrative summary, error flags, structural quality score.

Each extraction received a confidence score from the language model. We retained extractions with confidence ≥ 0.4 for downstream analysis. The cohort is n = 348, with mean confidence 0.72 and range 0.40 to 0.95.

Output of Phase 3: the JSONL file at data/structured_extractions.jsonl.

Manual review and cleaning

We manually reviewed every extraction record to:

• Verify jurisdiction names and state codes
• Flag edge cases (regulatory ordinances mischaracterized as moratoria, rejected proposals, withdrawn projects, etc.)
• Resolve [VERIFY] flags by re-checking primary sources via real-Chrome browser sessions
• Add moratoria identified through news coverage but missed by automated extraction

The final cleaned inventory has 222 entries across 30 states (data/moratorium_inventory.csv).

Phase 4: Geocoding (added v2026.04.2)

Each row in the cleaned inventory was assigned WGS84 latitude and longitude coordinates representing the jurisdiction's centroid. Two-tiered approach:

1. Primary geocoder: OSM Nominatim. Free, open-source, with reasonable U.S. administrative boundary coverage. Rate-limited to 1 request/second per the public API usage policy.
2. Fallback: U.S. Census Geocoder. Used when Nominatim returns no result. The Census Geocoder is authoritative for U.S. jurisdictions but works best for street addresses; for "Jurisdiction, State" queries we found Nominatim more reliable.

Of 222 rows, 220 (99.1%) were successfully geocoded. The 2 blanks are aggregate meta-rows (Other Reported Local Moratoria, Michigan and Proposed or Rejected Local Pauses, Maryland) that aren't real geographic points.

After geocoding, a triple-check audit ran 89 verifications across three independent methods:

1. Random sampling against geographic knowledge (24 rows): manually verify each coordinate matches a well-known location.
2. Wikipedia GeoSearch reverse-lookup (50 rows): query Wikipedia for pages within 10 km of our coordinates; verify the jurisdiction name appears among them.
3. Targeted high-risk subset (15 rows): the 4 manual within-state-ambiguity fixes plus other generic township names where ambiguity is most likely.

Across all 89 verifications, zero confirmed wrong geocodes (after the 4 manual Ohio corrections in v2026.04.2). The audit caught and corrected:

• Lake Township, OH (geocoder picked Logan County → corrected to Wood County)
• Plain Township, OH (Franklin County → Stark County)
• Spencer Township, OH (Lorain County → Lucas County)
• Waterville Township, OH (Stark County → Lucas County)

Each correction used article-context disambiguation (legal_basis, trigger, and news-source mentions). Treat the lat/lon column as ≥99% accurate. The script is scripts/geocode_inventory.py; re-run after adding new rows to fill in their coordinates.

Why the inventory (n=222) is bigger than the extraction cohort (n=348)... wait, that's smaller

Right — the numbers can be confusing. Here's the difference:

• Inventory (n=222): the cleaned, deduplicated count of unique moratorium instruments (one per local-government action). One DeKalb County resolution = 1 row, even if there are 5 documents about it.
• Structured-extraction cohort (n=348): the count of confidence-filtered structured extractions. A single moratorium can produce multiple extractions: the ordinance text, the meeting minutes, the agenda packet, etc. Plus the cohort includes some duplicate adoptions and extensions captured separately.

The two numbers measure different things and don't need to match. The 222 is the headline count of moratoria; the 348 is the size of the line-coded sample used for clause-prevalence percentages.

What we don't claim

• We don't claim to have every moratorium. Small townships without online minutes are nearly impossible to find systematically. Where we know our coverage is incomplete, we say so in docs/known-gaps.md.
• We don't claim our percentages are statistical inferences. They describe the corpus we have, not a probability sample of all moratoria. The corpus is biased toward jurisdictions that post things online.
• We don't claim to predict outcomes. This is a descriptive dataset, not a causal one.

Reproducibility

Every step of the pipeline can be re-run. The scripts are in scripts/ with a README explaining each one. To regenerate every table and figure from the source data:

python -m scripts.generate_tables   # rebuilds all tables/*.tex
python -m scripts.moratorium_maps all   # rebuilds all figures/

The original document corpus (~12 GB) is not in this repository (it's hosted separately on Zenodo as the supplementary data deposit) but the cleaned inventory + structured extractions are sufficient to reproduce all published statistics.

Tooling and models

Updates

Each refresh of the dataset is a tagged GitHub release (v2026.04, v2026.10, ...) with a corresponding Zenodo DOI (planned). Refresh cadence is roughly quarterly while the moratorium wave is active.