National baseline
Defines the metric and shows broad patterns. Local records answer for a specific facility.
Data centers use power, water, land, and public infrastructure. The effects change with location, design, and local rules. Check the evidence before accepting a warning or reassurance.
Every published conclusion keeps these questions in view.
What exactly is being counted?
Start with the reported value before weighing its interpretation.How was it measured or calculated?
Name the unit and calculation before comparing figures.Where and when does the figure apply?
Keep the facility, geography, time period, and system boundary in view.What choices fill the gaps?
Identify the estimates, conversions, and gaps shaping the result.Can you inspect the original evidence?
Trace the number to a reviewable filing, dataset, or primary document.Defines the metric and shows broad patterns. Local records answer for a specific facility.
Uses the tariff, permit, utility study, agreement, and operating record to assess a specific project.
National load is growing fast. Local effects depend on rate design, cooling, grid mix, siting, and public agreements. Those choices shape who carries the risk and who shares the value.
Each trail ends with records to request for a specific project. National statistics cannot settle a local proposal.
There is no universal bill effect.
02Large loads can strain capacity; outages are not inevitable.
03Who pays depends on the tariff and the asset.
04Water impact is site-specific and measurable.
05The footprint is real and varies by location and metric.
06Harmful noise is possible; setbacks alone prove nothing.
07One action cannot be mapped to a campus.
08No. Data centers serve many sectors beyond Big Tech.
09Siting requires more than acreage.
10Proximity is not proof of harm.
11Permanent jobs and public revenue are real; net benefits vary.
12Some tasks move to devices; data centers remain necessary.
This 50-state cross-section combines EPRI’s modeled 2024 data-center energy estimates with final EIA Form 861 data. The chart measures association only. It cannot establish cause.
In this descriptive cross-section, states with a larger EPRI-modeled data-center electricity share in 2024 did not have faster nominal residential price growth over the prior decade.
Pearson r = −0.12; R² = 0.015; Fisher 95% interval for r: −0.39 to +0.16; n = 50. Share = EPRI 2024 medium modeled energy ÷ EIA 2024 total retail sales. Price growth = EIA residential average revenue/kWh, 2014–2024. This association alone cannot establish causation.
| State | Modeled 2024 data-center electricity share (%) | Residential price growth, 2014–2024 (%) |
|---|---|---|
| AK | 0% | 29.7% |
| AL | 1.9% | 32.2% |
| AR | 0% | 29.5% |
| AZ | 8.1% | 25.3% |
| CA | 3.7% | 96.7% |
| CO | 3.4% | 22.5% |
| CT | 1% | 45.6% |
| DE | 0.2% | 24.7% |
| FL | 0.5% | 18.9% |
| GA | 5.4% | 20.9% |
| HI | 0.2% | 15.7% |
| IA | 14.4% | 20.1% |
| ID | 1% | 18.5% |
| IL | 6.9% | 33.2% |
| IN | 0.8% | 28.9% |
| KS | 0.2% | 16.3% |
| KY | 2% | 25.9% |
| LA | 0.1% | 22.6% |
| MA | 2.8% | 68.8% |
| MD | 0.3% | 31% |
| ME | 0.2% | 59.1% |
| MI | 0.4% | 33.5% |
| MN | 1% | 28.6% |
| MO | 1.7% | 21.3% |
| MS | 0.2% | 18.3% |
| MT | 3.8% | 24.4% |
| NC | 2.7% | 27.3% |
| ND | 6.5% | 25.8% |
| NE | 13.6% | 10.9% |
| NH | 0.2% | 33.5% |
| NJ | 5.7% | 22.6% |
| NM | 3.2% | 15.6% |
| NV | 12% | 16% |
| NY | 3.1% | 21.7% |
| OH | 4.4% | 27.9% |
| OK | 2.7% | 22% |
| OR | 18.4% | 40.4% |
| PA | 1.9% | 33.4% |
| RI | 0.2% | 66.9% |
| SC | 2.1% | 14.3% |
| SD | 0.8% | 22.8% |
| TN | 1.9% | 20.3% |
| TX | 6.6% | 26% |
| UT | 9.4% | 14.7% |
| VA | 24.6% | 29.8% |
| VT | 0.1% | 25.4% |
| WA | 7.1% | 37.3% |
| WI | 0.3% | 25.7% |
| WV | 0% | 61.3% |
| WY | 9.3% | 18.8% |
We ask the same five questions of every claim, including claims we expect to agree with.
Read the method and analysis notesNumber
What is being counted?Metric
How is it measured?Scope
Where does it apply?Assumptions
Which choices shape it?Source
Where did it come from?