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Economics — regional cloud-consumer demand & the public benefits extended to it

A demand-side companion to HYDROLOGY.md. Unlike HYDROLOGY, this document is not watermark-generated — it is hand-assembled analysis over cited records. Every figure is tagged: [verified] (read from a committed extraction or cited record), [inference] (a labelled derivation), [assumption], or [open] (a question, not a finding). The spine is civil/land/hydrology; this is the second axis — what the campus consumes, and what the public gives it — not a claim about who benefits.

Localized baseline. For the quantitative ground beneath this — Allen County’s employment by industry, its export-orientation, and the employment trend — see the generated localized economic baseline (BLS QCEW), the place the ~50 promised jobs and the 75% abatement actually land.

1. The load — what the campus draws

The one hard, document-anchored magnitude is electrical:

A ~275 MW IT load is a large consumer — roughly the scale of a mid-size city’s electricity demand, sited on one corridor. The water consequence is already modeled in HYDROLOGY (net basin loss ≈ 24–77× the Ottawa 7Q10). This page is the power and tax-base half of that consumption story.

2. The public benefits extended to it

What the public side committed, from the county’s own production [verified: data/extracted/legal/prr-mandamus/bosc-prr-production-2026-06-05.response-index.yaml]:

3. The mismatch — benefit vs. jobs vs. consumption [inference]

Set the verified columns side by side:

• ~275 MW IT load and 3.1–10 MGD consumptive water, against
• ~50 permanent jobs and a 15-yr/75% abatement on a ~$500M build.

That is on the order of ~5–6 MW per job and a multi-MGD basin draw for a headcount a single big-box store would exceed. The economic argument the corpus substantiates is structural: the public subsidizes load and consumption, not employment — and does so for a counterparty it cannot name (the Delaware shell; see DOSSIER §2). [inference] This is the demand-side mirror of HYDROLOGY’s “burden already maxed” finding (the 1996 SSO consent decree, the $11.8M I/I backlog).

4. Why this load exists here — demand-side drivers [open]

These explain the incentive to site authorized cloud capacity in a low-cost, low-scrutiny jurisdiction. The magnitudes are now document-backed industry reference ranges — from the relator’s data appendix, with its cited sources — though whether each applies to this campus stays [inference]/[open]:

• Authorized-region premium. Government/sovereign cloud (GovCloud-class, FedRAMP / DoD IL2–IL6) runs ~20–30% above commercial (BCG: up to 30%; AWS GovCloud EC2/S3 examples) — a recurring premium per hour and per GB. That rewards building dedicated, hardened capacity. [verified: appendix §1] / application-to-campus [open]
• Tax-base forecasting risk. Ohio’s data-center sales-tax exemption (DCTE) is scored against an equipment-purchase forecast — but AI-class hardware breaks that forecast: GPU servers $200k–$515k, replaced on a short cycle, ~30–40% of cost annually in opex. The abated base may never materialize against the consumption. [verified: appendix §2] / fiscal outcome [open]
• Refresh / AI-rack cost curve. Rack power density jumps 5–15 kW → 40–140 kW (conventional → AI/GB200), with projections of 250–900 kW/rack by 2027 — i.e. MW/water per rack trend up, not flat, across the abatement window. [verified: appendix §2]
• Facility footprint. A single site is a community-scale draw: 25 MW (the Ohio tariff/amendment reference) to 100 MW–1 GW, WUE ~1.8–1.9 L/kWh, up to ~5M gal/day evaporative — and blowdown discharge ~20–40% of cooling water, the wastewater tie-in to the WWTP capacity in HYDROLOGY. [verified: appendix §3]

These drivers are the substance of the relator’s committee data appendix (reproduction; prepared but not submitted). The figures are industry reference ranges with cited sources — real, documented magnitudes — not facility-specific values for the Bistrozzi campus.

5. Document-backed vs. analysis — the discipline line

6. Consumer energy-price pressure — the demand spillover [inference]

The 2026-06-10 facility-design call asked to “bring in fuel costs at the consumer level due to macro pressures and data-center demand.” The watermark.economics.energy thread sizes that spillover against committed EIA consumer prices (watermark eia → data/reference/eia/): Ohio residential electricity (¢/kWh), residential natural gas ($/Mcf), and total state retail electricity sales.

The link is the facility’s first-class total facility_draw (§1 + the PUE model, issue #87 — IT load × PUE), not IT load alone. derive_demand_pressure persists this sensitivity to data/reference/eia/demand-pressure.yaml (per-site, facility-gated) and exposes it as the economics-demand-pressure bundle feed (issue #1105), so the frontend sources these figures rather than the docs hand-copying a console printout:

The demand share and households-equivalent are the robust, EIA-cited headline; the price-pressure band is a deliberately stylized screening sensitivity, not a forecast (retail price formation is far more complex than one coefficient, and the campus buys at wholesale/industrial rates, not the residential price shown). This is the consumer-cost mirror of the §3 “subsidizes load, not jobs” finding.

Nothing on this page promotes a defense-intelligence thesis. Defense-ecosystem actors enter only as [open] context where the public record already names them (see COURSE §1.4); the load, the benefits, and the consumption are the findings.