Composite Stress Index
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Every new data center consumes water, draws power from the grid, and occupies land. But the communities hosting them rarely have the data to evaluate whether they are getting a fair deal. This tool changes that.
Every U.S. county hosting a data center receives four scores, each normalized on a 0–1 scale:
Measures the combined strain on local water, energy, and land. Higher = more burden on community resources. Weighted: 40% water, 35% energy, 25% land.
Measures how transparent the state regulatory environment is. Higher = less accountability in permitting, tax incentives, and environmental review.
Measures what the community gets back in local jobs and wages per megawatt of installed capacity. Higher = better return. Uses Monte Carlo simulation for confidence intervals.
Blends all three scores: 50% burden, 30% opacity, 20% inverted return. Counties receive letter grades A through F. This is the single number that ranks communities by overall data center stress.
The entire index is built from 22 public data sources, all free. No proprietary datasets. No gated APIs. Total cost: under $500.
Transparency requires honesty about gaps. Here is what we cannot yet measure:
Peer review identified the following structural limitations. We disclose them here because transparency is a core value of this project.
Each tab focuses on a different dimension of the data center landscape:
The Data Center Stress Index (DCSI) is a county-level composite ranking of U.S. counties hosting data centers. It measures three dimensions: the resource burden each facility places on local water, energy, and land; the regulatory opacity surrounding permitting and operations; and the economic return the community receives in jobs and wages per megawatt of installed capacity. Use this dashboard to explore which communities bear the heaviest costs — and which receive the least in return.
Interactive choropleth showing the Composite Stress Index for U.S. counties with data center facilities. Green = low stress, red = high stress. Sourced from PNNL, FracTracker, Epoch AI, WRI Aqueduct, EIA-923, and BLS. Scroll to zoom.
Market share of U.S. data center capacity by parent company headquarters country or corporate operator. Foreign-owned facilities may face additional CFIUS scrutiny. Data sourced from PNNL IM3 Atlas and Epoch AI ownership records.
Each bar decomposes a state's aggregate resource burden into water stress, grid load, and land use components, the three pillars of the RBS formula (40/35/25 weighting). Water stress comes from WRI Aqueduct 4.0, grid load from EIA-923 plant-level generation, and land use from PNNL facility footprints over USGS NLCD developed area. Click any state to see the raw data and calculation.
Districts ranked by composite stress index. This view connects resource burden to political accountability. Every district bar maps to a specific representative who can champion or block data center policy. District boundaries from Census TIGER/Line shapefiles, legislators from the @unitedstates project and Open States.
Counties delivering the most local economic value per megawatt of data center capacity. High ERS counties demonstrate that data centers can generate meaningful employment. Employment and wage data sourced from BLS Quarterly Census of Employment and Wages (NAICS 518210). MW capacity from PNNL, Epoch AI, FracTracker, and hand-researched overrides.
Reading this chart: Each bubble is a county hosting data center facilities. Bubble size reflects total MW capacity.
The top-left quadrant is the danger zone: high resource burden (water, energy, land) paired with low economic return (few jobs, low wages per MW). These counties subsidize the digital economy without proportional benefit.
The bottom-right quadrant is the sweet spot: modest resource consumption alongside meaningful local employment and wages.
Click any county bubble to see its full scorecard, elected officials, and facility-level breakdown.
Scatter plot of Resource Burden Score (x-axis) vs. Economic Return Score (y-axis) for all counties with data center facilities. RBS is derived from WRI Aqueduct water stress, EIA-923 energy data, and USGS land cover. ERS is derived from BLS QCEW employment and wage data. Bubble color reflects the Composite Stress Index. Use your scroll wheel to zoom into dense clusters.
Select a state to see its counties, facilities, ownership breakdown, resource burden, and economic return. All charts below filter to the selected state.
Reading this chart: Each bubble is a county. Size reflects MW capacity.
The top-left quadrant = high burden, low return. The bottom-right = low burden, high return.
Select a policy proposal to see how it would change county stress scores. The map adjusts to show before/after impact. Proposed data centers are shown with dashed outlines. Use your scroll wheel to zoom into any region of the map.
This timeline tracks moratoriums, legislation, and executive actions that shape where and how data centers can be built. Events are color-coded by type: green for enacted restrictions, amber for proposed bills, red for vetoed or failed measures, and blue for federal actions.
The Regulatory Opacity Score (ROS) measures how much information states disclose about data center permitting, environmental review, utility rates, tax incentives, and ownership. Higher opacity means less public transparency. Scores are derived from a 6-variable State Regulatory Index across 42 DC-hosting jurisdictions. Click any bar to filter the dashboard to that state.
$16.2 billion in disclosed tax incentives, abatements, and megadeals tracked by Good Jobs First’s Subsidy Tracker (through Nov 2025). Five companies — Amazon, Apple, Meta, Google, and Microsoft — account for 86% of disclosed value. An additional 299 records have undisclosed amounts. A May 2026 audit identified at least $9B+ in additional megadeals approved Dec 2025–Apr 2026 that have not yet been added to the GJF tracker — making the true total significantly higher.
A new pattern in 2026: voters, county commissions, and local ordinances are reversing or rejecting data center tax breaks. Five examples since February alone.
The DCSI flags 297 foreign-owned facilities in its current dataset. After a May 2026 sovereign-capital audit, we estimate the true count is closer to 400–450. The gap comes from acquisitions that have closed since the underlying source data was compiled, sovereign LPs that don’t show up in operator HQ fields, and crypto-to-AI conversions that fall outside the colocation taxonomy. This page documents what’s in the dataset today, what’s missing, and how to read the difference.
Force-directed graph of how foreign sovereign wealth funds anchor U.S. AI infrastructure operators. Click any node for expanded context. Built directly from May 2026 audit findings.
Each tile = a foreign sovereign or sovereign-fund-of-fund LP and the U.S. operators it touches. Click any tile for the full bio, asset list, and source citations.
Total facilities and capacity by HQ country, aggregated from current dataset (does not yet include audit-identified gaps).
In October 2025, the AI Infrastructure Partnership (AIP) consortium — co-founded by BlackRock GIP, Microsoft, Abu Dhabi’s MGX, and Nvidia, with xAI joining the acquiring group and Kuwait Investment Authority and Singapore’s Temasek as anchor LPs — agreed to acquire 100% of Aligned Data Centers from Macquarie at a $40B enterprise value. Some reporting now puts close in late 2026 rather than H1.
Aligned operates 50 campuses with >5 GW of operational and planned capacity (78 data centers per Aligned disclosures). Major U.S. sites: Plano TX (HQ), Phoenix, Ashburn VA, Salt Lake City, Atlanta, Dallas, Hillsboro OR, Columbus OH, Chicago, and Northern Virginia.
On close (H1 2026), Aligned’s controlling ownership flips from Australian (Macquarie) to a UAE / Kuwait / Singapore / U.S. mixed sovereign-controlled posture. The DCSI should consider every Aligned U.S. campus as foreign-controlled post-close. This single transaction may add 40–50 facilities to the foreign-owned count.
The current DCSI flag is binary: a facility’s operator HQ is either US or non-US. This misses three categories of foreign control:
Proposed methodology change: Add a control-weighted flag. Any facility whose operator has >25% foreign sovereign or sovereign-fund-of-fund LP triggers a “sovereign-exposed” tag, even if the operator HQ remains US.
CFIUS reform: extend mandatory review thresholds to sovereign-fund-of-fund LP positions >25% in critical infrastructure including AI data centers.
Mandate foreign-LP disclosure as a condition of any data center tax incentive. Florida HB 1007 (2026) attempted this but the NDA-prohibition provision was stripped before passage.
Track AIP, Mubadala, MGX, GIC, CPP cap-table announcements. Bloomberg’s Josh Saul, WSJ’s Sebastian Herrera, Inside Climate’s Kristoffer Tigue are among the reporters most actively covering this beat.
How the DCSI is calculated, what data it uses, and where human judgment enters.
For every U.S. county hosting a data center: what resource burden is that facility placing on the community, and what economic return is the community receiving?
Percentile-rank normalization across all 670 DC-hosting counties. Water draws on WRI Aqueduct 4.0 baseline water stress with seasonal indicators. Energy is estimated facility-level consumption (MW × utilization × 8,760 hours) following LBNL methodology, supplemented with EIA-923 generation where local power plants exist. Land is facility footprint as a share of county land area, with MW-derived estimates for counties missing observed acreage.
6-variable State Regulatory Index across 42 DC-hosting jurisdictions: environmental review, resource disclosure, tax incentive accountability, permitting openness, utility rate transparency, and ownership disclosure. Each variable scored 0–3 against statute citations. Community pushback applies a −10% modifier; the Company Opacity Index applies up to a −5% modifier where local operators are demonstrably more transparent.
Wage Premium is data center industry average pay divided by county all-industry average pay. Monte Carlo error propagation (1,000 iterations) produces 90% credible intervals. Counties are tagged Tier A (high confidence), B (moderate), or C (imputed inputs). Mean wage premium across DC counties: ~1.9x.
The DCSI is an advocacy-grade composite built by a single analyst working in public, not a peer-reviewed academic index. Following an independent May 2026 methodology red-team, these caveats are now foregrounded:
These caveats are deliberately listed in the methodology section, not buried in a footnote. Readers should weigh letter grades alongside their data quality tier, and policy advocates citing the dashboard should disclose that the weights are policy-weighted rather than empirically derived.
12-indicator binary scoring of corporate transparency across 142 data center companies. Indicators span environmental reporting, energy disclosure, water disclosure, PUE reporting, renewable targets, cooling technology, community engagement, tax incentive disclosure, beneficial ownership, supply chain transparency, third-party audits, and incident reporting.
Scale: 0 = fully transparent, 1 = fully opaque. Tiers: Transparent (≤0.25), Open (0.26–0.50), Partial (0.51–0.65), Opaque (0.66–0.80), Dark (>0.80). County-level COI is capacity-weighted (larger facilities contribute more) and feeds the ROS calculation as a transparency credit of up to −5%.
Facility-level composite derived from PUE, grid dependency, cooling impact, backup emissions, and a transparency penalty. Quality flags: measured (real data for 3+ components), partial (1–2 real data points), insufficient (fully imputed — default 0.593).
Higher EES = more efficient. EES is currently displayed as an informational overlay and is not factored into the composite CSI; integration into RBS Energy is under evaluation.
Every county receives a data quality tier reflecting how much of its scoring input comes from observed source data versus estimates or imputations.
The four dimensions assessed are: (1) water stress (observed via Aqueduct vs. state-median imputed), (2) energy (EIA-923 supply data vs. MW demand proxy), (3) land (source acreage vs. MW-estimated), and (4) employment (BLS QCEW observed vs. state-residual imputed). Counties with Tier C data have the majority of their score driven by estimates and should be interpreted with caution. The tier is displayed in the county tooltip, scorecard, and sidebar.
Measures whether data center stress clusters geographically or distributes randomly. A positive Moran's I indicates clustering: counties near high-stress counties tend to also be high-stress, suggesting regional infrastructure strain rather than isolated incidents. Computed on the CSI values using queen contiguity weights from the Census TIGER county shapefile.
Local Moran's I (LISA) identifies specific hot-spot and cold-spot clusters. Counties flagged as hot spots (High-High) are surrounded by other high-stress counties. These are the regional pressure zones where infrastructure strain compounds.
Following the May 2026 red-team review, six methodological gaps were addressed. Each is documented below.
An independent second rater scored 10 randomly-selected states blind to the original DCSI ROS values. The rater used the same 6-variable rubric (permit transparency, environmental review, energy disclosure, water disclosure, tax incentive accountability, ownership disclosure) on a 0–3 scale.
Pilot finding: Reliable agreement on the extreme tiers (NJ Transparent, TN/SD/MS Highly Opaque). Rater identified 8 specific variable-state cells with genuine scoring ambiguity — primarily on environmental_review (Oregon, NJ, Wisconsin) where partial regimes (EFSC, EJ Law, WEPA) sit between “none” and “full NEPA-equivalent.” Cohen’s weighted κ computation against original DCSI scores is pending; preliminary indication is moderate agreement (ฮบ โ 0.55–0.70) — sufficient to call ROS reliable at the tier level but not at the variable level. Future work: third-rater calibration on the 8 ambiguous cells.
The DCSI’s 670 host counties are not homogeneous. ~15 fiber-mature peering hubs (Loudoun VA, Santa Clara CA, Cook IL, Manhattan NY, Maricopa AZ, Dallas TX, etc.) capture network-effect economic returns that the other ~655 counties cannot replicate. Loudoun’s $700M+ in DC tax revenue is the ceiling, not the median.
Peering hubs (n≈15) collect a median of ~$400M+/year in DC-related local revenue. Greenfield counties (n≈655) collect a median of under $4M/year. When the dashboard cites “low ERS” outcomes for the median host county, the comparison set should be greenfield, not the peering-hub anchored mean. The list of 15 peering-hub FIPS is hardcoded in the dashboard JS as PEERING_HUB_FIPS and excluded from greenfield aggregations.
The composite’s default weights (RBS = 0.40 Water + 0.35 Energy + 0.25 Land; CSI = 0.50 RBS + 0.30 ROS + 0.20 inverted ERS) are author-selected. Plausible alternatives:
Most counties are weight-robust (grade does not flip across plausible perturbations). The ~10–15% that DO flip cluster at the D/F boundary — meaning marginal-call counties should not be cited as definitively “F-graded” without acknowledging weight sensitivity.
The DCSI “burden” framing implies data center development is net-negative. A fairer comparison is opportunity cost: what would the same 250 MW of grid capacity, 200 acres of industrial-zoned land, and $5B of capex produce if deployed elsewhere?
Data centers are a high-tax-base, low-employment use. The right comparison frame is not “$77M for 1 job is bad” but “the same MW + acres + capex deployed in advanced manufacturing creates 10–30x more permanent employment.” This frames the policy question as opportunity cost, not absolute waste.
Earlier dashboard versions reported county-level Granger tests for “DC announcements precede water stress.” On red-team review, the 11-year window (2015–2026) is borderline-underpowered for county-level testing, and 670 simultaneous tests at α=0.05 yields ~33 false positives by chance. The findings have been downgraded to exploratory and are no longer cited as confirmatory evidence. FDR correction (Benjamini-Hochberg) and pre-registration are V11 deliverables.
The DCSI tracks 62 moratoriums; datacenterbans.com tracks 222+ across 30 states. The gap is methodological:
Both are valid scopes; reading either headline number requires understanding the criteria. The DCSI 58 figure is the conservative estimate; 222 is the maximalist count. The dashboard’s reported number is footnoted with this distinction in the Community Resistance tab.
Rather than fixed weights (0.4/0.35/0.25), entropy weighting lets the data determine how much each component contributes to the composite score. Components with more variation across counties receive higher weights; components where all counties score similarly receive lower weights. This prevents a uniformly high-stress component from dominating the index without adding discriminatory power.
Both fixed and entropy-weighted composites are computed. The dashboard defaults to fixed weights (which are more interpretable for policy audiences) but allows toggling to entropy-weighted for analytical rigor.
Tests whether data center facility announcements (from FracTracker timeline data) Granger-cause changes in county-level water stress scores (from Aqueduct monthly data). If past facility announcements help predict future water stress beyond what water stress's own history predicts, this provides statistical evidence of a causal link between data center expansion and resource degradation.
Applied county-by-county where sufficient time-series data exists (2015 to 2026). Results reported as significant/not significant with lag selection via AIC.
Counties receive letter grades A through F based on their percentile rank within all 670 data-center-hosting counties:
Percentile grading is a relative ranking, not an absolute threshold. An “A” grade means a county has lower composite stress than 80% of DC-hosting counties — it does not mean the county experiences no resource burden. Tier C data quality counties (where ≤1 of 4 scoring dimensions uses observed data) should be interpreted with particular caution; their grades are driven primarily by estimates and imputations.
Does not rank counties without facilities. Does not make causal claims. Does not generate regulatory findings. Informs judgment; it does not replace it.
A running ledger of the choices, validations, and trade-offs that shaped each version of the index. Kept here so the methodology body stays focused on what the score is, not what it has been.
The primary weights (0.4/0.35/0.25 for RBS; 0.5/0.3/0.2 for CSI) reflect the analyst’s judgment about relative importance. PCA, entropy, and equal-weight validation runs all produce rankings highly correlated with the policy-weighted version (Spearman ρ > 0.96), but the choice of weights is ultimately a value judgment. The dashboard’s entropy-weighted toggle exposes a purely data-driven alternative.
Estimated facility-level consumption uses LBNL methodology (MW × utilization × 8,760 hours). Utilization defaults to 50% per LBNL 2024 national average, adjusted by facility type (hyperscale 58%, colocation 50%, enterprise 43%). PUE sourced from operator reports where available (Google per-campus, Meta fleet 1.08, AWS per-region, Microsoft 1.16) or estimated from facility type. Replaces V8’s 100% utilization with flat PUE 1.3. EIA-923 generation data supplemented with an MW-based demand proxy for 89 counties with data centers but no local power plants. Renewable energy credit reduces energy burden for facilities reporting above-median renewable sourcing (up to −10%).
For 247 counties missing facility footprint data, land use is estimated using an industry heuristic of ~20 acres per 100 MW. Source acreage from PNNL and FracTracker is preferred where available.
All 6 regulatory variables across 42 jurisdictions were scored by a single rater (anna_claude). Inter-rater reliability pilot (Cohen’s Weighted Kappa > 0.60 per variable) deferred. Cronbach’s Alpha was dropped as a gate because the six variables are formative (independent dimensions), not reflective.
Company Opacity Index now acts as a transparency modifier on ROS: ROS × (1 + pushback_mod + COI_mod) where COI_mod = −0.05 × (1 − county_avg_coi). Counties whose operators are demonstrably more transparent receive up to a 5% reduction in regulatory opacity, recognizing that corporate transparency partially compensates for weak state regulation.
The Energy Efficiency Score is currently displayed as an overlay only. It is not factored into the composite CSI. Integration into the RBS Energy sub-score is under evaluation for a future version.
CUSUM change-point detection was removed because the available Aqueduct data covers a single year of seasonal variation, not a multi-year time series. Running CUSUM on 12 monthly values detected seasonal patterns (summer vs. winter), not genuine acceleration of water stress. The 38% flag rate confirmed this was noise, not signal. CUSUM has been replaced by the data quality tier system and facility-level mapping.
Percentile grading means exactly 20% of counties will always fall into each tier. There are no empirically validated “safe” or “critical” stress levels for data center hosting. Natural-breaks (Jenks) classification is under evaluation as a potential alternative.
ERS now uses Wage Premium (DC industry average pay divided by county all-industry average pay) instead of raw wages_per_mw. Mean wage premium across DC counties is approximately 1.9x. Monte Carlo error propagation (1,000 iterations) produces 90% credible intervals with Tier A/B/C confidence flags.
Regional Transmission Organizations (RTOs) and Independent System Operators (ISOs) maintain queues of power generation and large-load interconnection requests. These queues reveal where data centers plan to draw hundreds of megawatts years before construction begins — LBNL estimates over 200 GW of data center capacity is currently waiting in U.S. interconnection queues. However, this data is fragmented across 7 RTOs (PJM, ERCOT, MISO, SPP, CAISO, NYISO, ISO-NE) and dozens of non-RTO utilities, each with different reporting formats, naming conventions, and update schedules. No free, unified public dataset exists. The DCSI currently uses EIA-923 for actual generation and MW estimates for demand, but cannot model the pipeline of future grid load from interconnection requests. FERC’s proposed large-load rule (finalization deadline April 30, 2026) may create a standardized reporting framework that makes this data tractable.
A growing number of data centers operate on-site natural gas turbines or fuel cells that are not connected to the grid and therefore invisible to federal energy data. The Clean Energy Buyers Alliance estimates that approximately 56 GW of behind-the-meter (BTM) gas generation is either planned or under construction for U.S. data centers, primarily in Texas, Georgia, and Virginia. These facilities bypass utility metering, EIA-923 reporting, and state public utility commissions. The DCSI’s energy burden calculations are based on grid-connected power data (EIA-923) and MW capacity estimates. BTM generation represents a significant and growing blind spot: counties hosting BTM-powered data centers may have their energy burden systematically underestimated. Until federal disclosure mandates (such as the Durbin S. 4213 or the Clean Cloud Act) create reporting requirements for BTM facilities, this gap cannot be closed with public data alone.
Intelligence-grade analysis. Built in public. Under $500. The analyst never abdicated to the machine.
The Data Center Stress Index answers a question that should be simple: when a data center moves into a county, what does that county actually get in return?
Every county in the United States that hosts a data center is scored across three dimensions: the resource burden it bears (water consumption, grid load, and land use), the transparency of its regulatory environment (permitting visibility and diesel generator reliance), and the economic return it receives (local jobs and wages per megawatt of installed capacity). These three scores combine into a single composite index that ranks counties on a letter-grade scale from A (low burden, high return) to F (high burden, low return).
The index draws from 22 public data sources, all free, totaling more than 1 GB of raw data. The V9.3 facility dataset (4,984 facilities across 50+ variables, deduplicated) merges PNNL, FracTracker (April 2026), and Epoch AI (April 2026) records with company-level transparency, efficiency metrics, and estimated energy and water consumption. Water stress scores come from the World Resources Institute's Aqueduct 4.0 atlas, spatially joined to county boundaries using Census TIGER shapefiles. Energy burden uses LBNL methodology to estimate facility-level consumption (MW × utilization × PUE × 8,760 hours), supplemented with EIA-923 plant-level generation where local power plants exist. Land use intensity is calculated from PNNL facility footprints divided by county total land area (Census TIGER ALAND), with MW-derived estimates for counties missing observed acreage. Employment and wage data come from the Bureau of Labor Statistics Quarterly Census of Employment and Wages (NAICS 518210 and all-industry), and regulatory opacity is derived from a 6-variable State Regulatory Index covering 42 jurisdictions, modulated by a Company Opacity Index transparency credit. Foreign ownership attribution uses a structured field from the V8 facility dataset, covering 297 foreign-flagged facilities across 14 countries.
Each component is normalized to a 0-to-1 scale and combined using expert weights validated against three alternative weighting schemes (PCA-derived, entropy, and equal-weight). The Resource Burden Score uses 40% water, 35% energy, and 25% land; PCA validation confirms water and energy dominate variance. The Regulatory Opacity Score uses a 6-variable State Regulatory Index covering environmental review, resource disclosure, tax incentive accountability, permitting openness, utility rate transparency, and ownership disclosure across 42 jurisdictions, modulated by a community pushback modifier. The Economic Return Score combines jobs per MW and a wage premium ratio (data center pay vs. county all-industry average), with Monte Carlo error propagation producing 90% credible intervals and confidence tiers. The composite index blends these three scores (50% burden, 30% opacity, 20% inverted return) to produce a single ranking. V8 added a Company Opacity Index (12-indicator corporate transparency) and Energy Efficiency Score (PUE, grid dependency, cooling impact) at the facility level. V9 replaced the flat energy estimation with LBNL type-adjusted utilization and operator-reported PUE, added WUE-based water consumption modeling (Siddik et al. 2021), and introduced a Good Jobs First subsidy tracker covering $16.2 billion in disclosed incentives.
Beyond the core index, the tool applies several analytical methods to surface patterns that a simple ranking would miss. Moran’s I spatial autocorrelation identifies geographic clusters of high-stress counties, revealing regional infrastructure strain rather than isolated incidents. Entropy-weighted scoring offers a data-driven alternative to fixed weights, giving more influence to components that vary most across counties. Granger causality testing (pipeline-side) examines whether data center facility announcements statistically predict future changes in local energy generation patterns using EIA-923 time-series data. Every county is also assigned a data quality tier (A/B/C) so users can see how much of the score relies on observed versus imputed inputs.
The Policy Impact Lens lets users simulate the effect of 12 real legislative proposals on county stress scores. Each policy is decomposed into the specific formula parameters it would affect, with percentage adjustments derived from bill text and regulatory intent. The policies span the full spectrum from federal moratoriums that would halt all new construction to executive orders that accelerate permitting on federal land. This is not prediction; it is structured scenario analysis designed to inform advocacy, journalism, and legislative debate.
V9.3 introduced a Community Resistance tracker covering 119 cancelled, suspended, withdrawn, or denied data center projects and 62 moratoriums across 18 states (now including Florida). The cancelled projects data combines FracTracker Alliance records (89 facilities with community pushback, advocacy, and project cost fields) with 41 additional projects identified through independent research, including major Florida cancellations (Project Jarvis St. Lucie, Silver Fox Indiantown, Okee-One Okeechobee) and the Apr 2026 wave (Apple Valley MN, Coweta OK, Archbald PA, Tulsa OK Phase 2). Moratorium records span city, county, township, and state-level actions from California and Colorado to North Carolina and Florida. The dashboard surfaces this data in a dedicated Community Resistance tab with filterable tables, by-state summaries, and integration into county-level modal popups. The tool has 8 top-level tabs (DCSI Home, National, State, Policy, Subsidies, Foreign Ownership, Community Resistance, About) with the About tab consolidating Methodology, Data Sources, and AI Accountability under sub-toggles.
The DCSI does not rank counties that have no data center facilities. It does not make causal claims about whether data centers caused resource degradation. It does not generate regulatory findings or legal conclusions. It informs judgment; it does not replace it. Every data source is public, every formula is disclosed, and every assumption is documented in the Methodology page.
The Data Center Stress Index is a project by Anna R. Dudley. The entire tool was built using publicly available data for under $500. No proprietary datasets. No gated APIs. No corporate sponsorship. The purpose is to put the same analytical capability in the hands of local officials, community advocates, and journalists that the industry's own lobbyists already have.
For questions, speaking inquiries, or data requests, contact Anna R. Dudley. Power moves before policy does.
Data center projects worth tens of billions of dollars have been cancelled, suspended, or withdrawn after community opposition. Moratoriums are spreading. This page tracks what the data shows: when communities organize, they win.
Projects tracked from FracTracker Alliance and independent research. Filter by state or status.
| Project | County | State | Status | Operator | Cost | Pushback |
|---|
Jurisdictions that have enacted, proposed, or considered restrictions on new data center development.
| Jurisdiction | State | Type | Status | Date | Duration |
|---|
Community lawsuits and environmental challenges currently in court. Each represents a procedural avenue communities are using when zoning and political channels close.
Sources: FracTracker Alliance Data Centers Database (April 2026), independent web research, Good Jobs First moratorium tracker, DataCenterBans.com. This tracker is maintained as a public resource. For corrections or additions, contact Anna R. Dudley.
This page is dedicated to my dear friend and mentor, J.R. Four years ago, sitting in an old basement, you taught me how to set up my first virtual machine and how to install packages. In this new age of AI, you continue to teach the entire floor about the importance of never abdicating to the machine. Let AI work for you. Don’t let it think for you.
Every score, label, formula and visualization in this dashboard was reviewed by a human before it shipped. The errors logged below were caught during that review — some by Anna, some by collaborators, none by automated tests. They are kept in public for the same reason climate scientists publish their model uncertainty: the only way to trust a number is to know how it was made.
If you find a number on this dashboard that looks wrong, that is the experience this page is supposed to make possible. Tell us, and you will be added to the log.
An “error” in this log is anything an AI assistant produced that would have shipped wrong if a human hadn’t caught it. That includes:
Most data dashboards are presented as finished objects. The methodology page tells you the formula, the source, the date — and asks you to trust that the implementation matched. AI-assisted analysis breaks that assumption. Code that looks right can be wrong in ways that are completely invisible until you check the answer against reality.
Listing what went wrong — including the things that were embarrassing or that took weeks to find — is the only honest way to show the work. If a county’s grade changes between versions, the reason should be readable. If a formula was wrong for three weeks, the fact that it was wrong should be on the same page as the corrected number. That’s the contract.
11 errors caught the night before a governor briefing. Two parallel background-agent audits ran simultaneously: one reconciling every dashboard number against its source-of-truth file, the other fact-checking high-visibility external claims (lawsuit jurisdictions, capex figures, MW totals, dates) against authoritative sources.
The most exposed find was ERR-062: AI placed a high-profile NAACP v. xAI Clean Air Act lawsuit in Tennessee when the actual Apr 14 2026 federal filing is in the Northern District of Mississippi, targeting a 27-turbine gas plant in Southaven that powers the Colossus 2 supercomputer across the state line in Memphis. AI also fabricated a plaintiff (“Memphis Community Against Pollution” is not party to that filing). Both errors would have been visible to anyone in the room with knowledge of the case.
Other items in the batch: a ReferenceError that logged on every page load (ERR-063); a Resistance hero stat showing 46 moratoriums when the canonical count is 62 (ERR-064); five places that said “17 states” when the source CSV has 18 (ERR-065); a Subsidies card that quoted a forward projection as live data (ERR-066); the Manassas case appellant misidentified as “Multiple developers” when QTS is the named appellant (ERR-067); and four lower-severity stale-number / framing items (ERR-068 through ERR-072). All 11 fixed and re-deployed before the briefing. The audits cost three minutes of agent time and would have been weeks of editorial damage if they’d caught at the podium.
Each entry includes the error ID, severity, status, the description of what was wrong, and how it was fixed. Sorted newest first. Filterable by status above.