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To evaluate a data center proposal, separate temporary construction work from permanent operating jobs, compare public revenue with incentives and documented public costs, and test energy and water claims against the project’s location and operating assumptions. For every headline number, ask who reported it, what it measures, what period and geography it covers, and whether anyone independent verified it.

Start by defining what each claim measures

A project announcement may combine projected jobs, estimated tax revenue, and environmental benefits in one headline. Those figures are not directly comparable unless their boundaries are clear. A national electricity estimate, for example, cannot establish the effect of one facility on a local grid; a regional economic-impact estimate is not a count of people hired by the operator.

For each claim, record the following before comparing it with another project or using it to assess a public incentive:

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What to record Why it matters
Source, publisher, publication date, and reporting period Shows who made the claim and whether it describes a forecast, a recent result, or a historical period.
Geography and government level Separates project, local, state, and national effects instead of treating them as interchangeable.
Measure, units, and denominator Clarifies whether a jobs figure is people or full-time equivalents, or whether a power figure is annual energy or peak demand.
Method, baseline, and counterfactual Reveals whether the number comes from direct records or a model, and what would have happened without the project or incentive.
Project phase and duration Keeps construction-period effects separate from continuing operations.
Incentives and public costs Shows whether a fiscal estimate includes tax relief and the public infrastructure or services needed to support the facility.
Verification and uncertainty Distinguishes independently checked results from operator-reported data or estimates with incomplete disclosure.

Use this as a claim-by-claim record, not a single score. If a source does not state a key field, mark it as “not stated” and avoid treating the figure as more precise than its method allows.

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Check whether job claims distinguish construction from operations

Construction employment and operating employment describe different phases. A large build may temporarily employ thousands of workers, but those jobs end when construction is complete. The number of people needed to operate a finished facility is typically much smaller. A claim that says only “jobs created” obscures that distinction.

Ask for a project-level staffing breakdown

  • Request separate construction and steady-state operating estimates, with expected start and end dates.
  • Ask whether the figures are direct hires, full-time equivalents, unique people, or jobs supported by an economic model. Keep modeled indirect or induced employment separate from direct positions.
  • Clarify whether contractors and tenant companies are included, and whether the count is for the operator alone or the wider project.
  • Ask how many positions are expected to go to local residents, what wages and skills are associated with them, and whether the estimate reflects actual hiring or a projection.
  • For operating jobs, request a staffing plan tied to the facility’s capacity and explain how capacity is measured.

Colorado Legislative Council Staff’s March 2026 memorandum cites a study estimate of about 43 workers per 100 MW of data center capacity. That is a cited estimate, not a universal staffing ratio and not a substitute for a project’s own staffing plan.

Read reported job counts with their verification status

Virginia’s Department of Taxation report for FY2024–FY2025 presents figures reported by operators participating in the data center tax exemption: 1,197 net new jobs for FY2024 and 1,610 for FY2025. The report says the figures were self-reported and were not independently validated by the Virginia Economic Development Partnership (VEDP) or the Department. They are aggregate operator reports, not independently verified jobs shown to have been caused by the exemption.

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Separate gross tax receipts from net public value

“Tax revenue” can refer to different taxes, different governments, or modeled effects rather than money already collected. Ask for a year-by-year ledger that separates state and local receipts and identifies recurring taxes, exemptions, abatements, and documented public costs.

Build a public-revenue ledger

  • List property and business-personal-property taxes, sales and use taxes, utility taxes, and relevant income or corporate taxes. Identify which government receives each.
  • List each exemption or abatement, its statutory duration, the period actually claimed, and the value attributed to it.
  • Where records support an estimate, include public spending on roads, water and sewer systems, grid connections, emergency services, or other infrastructure.
  • Separate taxes paid by the operator from taxes associated with workers, suppliers, tenants, or modeled induced spending.
  • If an economic-impact model is used, ask for its geography, baseline, treatment of displacement, local procurement assumptions, and multiplier method. Do not add modeled effects to direct receipts as if both were cash collected.

Also ask what a reported “return on investment” means: return to a public budget, total regional economic activity, or private financial return. A modeled estimate over a chosen period is not an audited account of realized net revenue.

Virginia: two different fiscal lenses

Virginia’s 2026 tax-exemption report uses a model comparing estimated state and local revenue with incentive cost over five-year periods. It attributes much of the modeled state revenue to worker income and sales taxes, and much of the local revenue to real-estate and business-property taxes. The report estimates a 1.7 revenue-to-cost ratio and $2.1 billion in cumulative revenue above exemption value over two overlapping five-year periods; these are modeled results, not audited realized net revenue.

The same report presents operator-reported tax savings of $1.3 billion in FY2024 and $1.9 billion in FY2025, corresponding to negative revenue impact. Those figures describe reported savings to operators; they are not, by themselves, a complete accounting of the incentive’s net public value.

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A narrower state-only finding should not be confused with that broader model. Virginia’s Joint Legislative Audit and Review Commission, as quoted in Colorado Legislative Council Staff’s March 2026 memorandum, said: “Like most economic development incentives, the data center exemption does not pay for itself when considering just the state portion of the exemption cost and the state return in revenue.” The Commission was also quoted as finding 48 cents in state revenue per dollar spent on the exemption, compared with a 41-cent average return for Virginia incentives. Those figures concern the state portion, not the combined state-and-local model.

Washington: a specific preference with a different result

Washington’s Joint Legislative Audit and Review Committee reported in July 2026 that beneficiaries of the state’s urban-county data center preference saved an estimated $42.4 million from 2023 to 2026, reported adding 53 family-wage jobs, and supported nearly 300 temporary construction jobs. The audit found the preference had been used only for refurbishment projects, not new data center construction, and recommended that it expire. This is a finding about that preference and period; it does not establish how other incentive programs perform.

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Test electricity and emissions claims against the local grid

Electricity consumption alone does not determine a facility’s emissions. The result depends on the electricity supply, the location and timing of demand, and what a renewable-energy or emissions claim actually counts. An annual renewable purchase or matching claim does not establish that the facility’s demand was matched with low-emissions electricity in every hour at that location.

Request the inputs behind the claim

  • Ask for annual energy use and peak-load forecasts, the commissioning schedule, and relevant grid-connection or utility-planning documents.
  • Ask whether the claimed renewable supply is matched annually or by time and location, and which facilities or contracts support the claim.
  • Check whether backup generation is included and whether direct facility emissions are reported separately from grid-related emissions.
  • Compare forecast demand with the local grid’s expected mix and the hours when the data center will operate, rather than applying a national average as a project-specific result.

For national context, Colorado Legislative Council Staff’s March 2026 memorandum cites a 2024 Lawrence Berkeley National Laboratory (LBNL) estimate that U.S. data center electricity use rose by about 100 TWh from 2018 to 2023. LBNL estimated the sector’s share of annual U.S. electricity consumption increased from 1.9% to 4.4% over that period. The same report projected a 6.7% to 12% share in 2028; that range is a forecast, not an observed 2028 result. National figures describe scale, not a particular facility’s grid emissions.

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Evaluate water use by source, season, and accounting boundary

A water number is hard to interpret without knowing whether it measures withdrawal or consumption, which water source is involved, when demand peaks, and whether it includes water used to generate electricity. A facility’s effect also depends on local watershed availability, competing demands, drought conditions, and seasonal constraints.

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Ask for facility-specific water data

  • Request withdrawals and consumptive use separately, with units and reporting period.
  • Identify whether water is potable, reclaimed, surface water, or groundwater.
  • Ask for the cooling design, seasonal peak use, discharge, and reuse figures.
  • Confirm whether the figure covers onsite cooling only or also upstream water used in electricity generation.
  • Compare projected use with local watershed conditions and other users, rather than relying on a national total.

Colorado Legislative Council Staff’s March 2026 memorandum cites a U.S. Department of Energy estimate of 66 billion liters of direct data center water consumption in 2023 and an LBNL estimate of 21.2 billion liters in 2014. Those totals exclude water consumed upstream to generate electricity. The memorandum notes that exact facility-level electricity and water impacts are difficult to assess because reporting is limited and some information may be proprietary; the national figures should not be read as a facility’s expected use.

Make comparisons use the same service and full delivery chain

Claims that digital delivery is more or less energy-intensive than a physical alternative depend on what service is being compared and which parts of each delivery chain are counted. A comparison that includes only a data center on one side and omits networks or user devices, for example, has different boundaries from one that counts the whole service.

UK Department for Energy Security and Net Zero studies compare representative end-user services across digital and physical alternatives. Their digital boundary includes data centers, transmission networks, and user devices. Depending on the physical alternative, that boundary includes relevant manufacturing, transport, retail, office delivery, and user devices. The studies examine streaming versus Blu-ray, e-books versus printed books, and AI translation versus human translation; a 2026 follow-up adds ten further use cases. Their results differ by use case, so one comparison should not be generalized to every digital service or used as a direct estimate of a local data center’s impact.

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Use a practical test before accepting a headline claim

  1. Write the claim precisely. Replace “creates jobs” with the stated number, geography, period, job category, and source.
  2. Find the underlying method. Determine whether the figure is a measured result, an operator report, a forecast, or a model. Note the baseline and counterfactual.
  3. Match the boundary to the question. Keep construction separate from operations, state revenue separate from local revenue, and onsite water separate from indirect water.
  4. Account for public costs and incentives. Compare receipts with incentive value and documented infrastructure or service costs, while distinguishing modeled effects from actual cash flows.
  5. Check local conditions. For energy and water claims, use the relevant grid, watershed, season, cooling system, and operating assumptions.
  6. State what remains unknown. If a key figure is unverified, missing, or not project-specific, say so plainly rather than treating it as a settled result.

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