The SoReMo Initiative at Illinois Institute of Technology

This student project was developed under the auspices of the Socially Responsible Modeling, Computation, and Design (SoReMo) Initiative at Illinois Tech in the Spring 2026 semester. SoReMo is a multidisciplinary forum that empowers student fellows to apply technical knowledge—in modeling, data science, design, etc.—toward solving pressing societal challenges. By connecting student research with a network of faculty advisors and community stakeholders, SoReMo ensures that technical innovation is guided by ethics, equity, and transparency.

“The People v. Hasty AI” embodies the SoReMo mission by taking the “invisible” data of the Illinois AI boom and translating it into actionable insights for the communities most impacted by this industrial expansion.

Research Areas/Relevant Topics: Artificial Intelligence (AI) Ethics, Urban Planning, Environmental Justice, Public Policy, Data Visualization

About the Authors

Taylor Washington – Project Lead. M.A.S Artificial Intelligence, B.S. Computer Science, Minor in Psychology. Interested in the intersection and implications of humanity and AI.

Laasya Priya Vemuri – Associate Fellow. M.S. Data Science. Interested in how large-scale AI systems operate in real-world environments and how their impacts can be made visible through data.

Background: AI Impact on Communities

Recent scholarship increasingly recognizes that AI infrastructure is not immaterial. It is deeply tied to physical systems from electricity grids and water reserves to regional environmental conditions. Researchers studying data center expansion emphasize that AI workloads are rapidly becoming a major driver of global electricity demand; on top of this, it is also introducing significant water consumption through cooling processes that are rarely visible to the public. Both de Vries-Gao (2025) and Patel et al. (2025) suggest that AI-related data center activity could generate tens of millions of tons of carbon emissions annually while consuming hundreds of billions of liters of water worldwide, highlighting the growing environmental footprint associated with large-scale computation. These findings challenge the popular perception of AI as a purely digital technology. We should instead position it as an industrial infrastructure with measurable ecological consequences.

Beyond energy use, researchers have begun examining how these environmental impacts translate into lived community experiences. Lifecycle analyses show that data centers contribute indirectly to air pollution through electricity generation, producing measurable public health burdens that may disproportionately affect nearby populations (Han et al., 2024). Social science research further argues that community concerns often stem not only from environmental exposure but from procedural issues such as limited transparency, restricted access to project information, and insufficient local participation in decision-making processes according to Sovacool et al. (2022). Policy analyses similarly note that rapid data center expansion in the United States frequently occurs faster than governance frameworks can adapt. This creates uncertainty about accountability, disclosure, and long-term community protections (Walker & Goldsmith, 2026).

Further exacerbating the issue, other sources connect these developments to broader questions of environmental and data justice. Researchers from both Vera et al (2019) and Lucivero (2019) argue that technological infrastructures distribute benefits and harms unevenly, especially when environmental monitoring data or operational disclosures remain inaccessible to affected communities. At the same time, emerging research on energy burden demonstrates that increases in large-scale electricity demand can intersect with socioeconomic vulnerability, potentially shifting infrastructure costs onto residents through higher energy expenditures (Garland et al., 2025). Together, these studies establish a foundation for examining AI data centers not only as technological innovations, but as socio-environmental systems whose impacts must be evaluated through environmental equality, governance transparency, and economic fairness.

Global energy trajectory of AI servers (Patel et al., 2025).

Figure 1: (Patel et al.)

Localized water and carbon footprints across the United States (Patel et al., 2025).

Figure 2: (Patel et al.)

Figures 1 and 2 are graphics taken from Patel et al., 2025; a study titled “The Environmental Impact of AI Servers and Sustainable Solutions”. While Figure 1 quantifies a global energy trajectory that demands haste, Figure 2 maps the localized footprints (water and carbon) that suffer because of it.⁴ As a pair, they demonstrate the inevitable collision between exponential computational growth and the finite physical resources of the communities that host these facilities. This provides a data-driven justification for the immediate implementation of our proposed annoyance threshold as a regulatory safeguard.

Background: Annoyance Threshold

To contextualize the study of the annoyance threshold, it is necessary to examine how industrial stressors have been historically quantified. Current research primarily frames these thresholds through an acoustic lens, providing a foundational understanding of human tolerance for industrial encroachment.

From Global Expansion to Local Reality

AI infrastructure is often discussed at a global scale, framed through innovation, computational growth, and economic competitiveness. Yet data centers are ultimately local projects. They occupy land, draw electricity and water, alter traffic and construction patterns, and introduce new industrial rhythms into surrounding communities. Across the United States, recent development trends show that while AI systems appear digital, their impacts are material and geographically uneven. Communities experience these projects not as abstract technological progress but through everyday environmental conditions, planning decisions, and economic tradeoffs.

To maintain consistency, each of the following cases will be examined through the aforementioned three interconnected dimensions: environmental, social, and economic. The environmental dimension focuses on physical and operational impacts such as power infrastructure expansion, generator emissions, land conversion, lighting, and noise. The social dimension considers transparency, participation, and community consent during planning and approval processes. The economic dimension evaluates how projects are justified through investment and redevelopment narratives while also recognizing potential long-term costs or perceived inequities. Together, these dimensions help define the report’s concept of the annoyance threshold, the point at which cumulative impacts begin to reshape daily community experience.

A Glimpse at an Established Precedent: The Case of South Memphis and xAI

The city of Memphis provides a glimpse at the impact of unregulated AI infrastructure. Dubbed xAI’s “Colossus,” this project encapsulates the idea of hasty AI expansion, bypassing traditional multi-year planning cycles. This speed was achieved at a significant cost to the local community of Boxtown in South Memphis. Community members would report tangible impacts on their daily lives.

Before examining what’s at stake in Illinois, we will first assess what has already happened in Memphis, following the same environmental, social, and economic cascade of impacts.

The xAI Colossus data center in Memphis, Tennessee, as of May 2025.

Figure 3: (Lindwall, 2025)

Meta’s “Hyperion” data center footprint overlaid onto a map of Manhattan.

Figure 4: (Lindwall, 2025)

Figure 3 is of the xAI Colossus data center in Memphis, Tennessee, as of May 2025. Figure 4 overlays a different data center site, Meta’s “Hyperion”, onto a map of Manhattan. Showing that a single data center footprint can swallow nearly a third of the island, this image illustrates a scale that is difficult to comprehend in isolation.

This facility represents a demand for space and energy that dwarfs the residential neighborhoods surrounding it. Exemplifying this, when the Memphis electrical grid could not immediately meet the massive demands of the Colossus facility, xAI turned to a “quick and dirty” power source: 18 mobile natural gas turbines. As noted by Cyrus Farivar for Forbes, these turbines are roughly 50% less efficient than permanent plants and emit significantly higher levels of nitrous oxide and formaldehyde.

Using data from public NASA satellites as well as the European Space Agency, researchers at the University of Tennessee found that average concentrations of nitrogen oxide have increased by 9% in the Boxtown communities—compared to periods prior to June 2024 (when xAI began constructing Colossus). Further, average concentrations were found to be up by nearly 79% in the area immediately surrounding the facility—when compared to pre-Colossus levels (Chow, 2025).⁵ Because these turbines were labeled as “temporary,” they bypassed the Environmental Protection Agency’s (EPA) oversight required for permanent stationary plants.

In environmental law, a “temporary” source—often labeled as “temporary-mobile”—is one that remains on a site for less than one consecutive year (Environmental Protection Agency, 2016). It’s typically installed by mounting turbines onto flatbed trailers or shuffling units until the permitted time elapses. Companies have historically evaded the stringent pre-construction permits required for stationary power plants via this “temporary” loophole (Ireland, 2026).

From June 2024 onwards, this strategy in South Memphis has allowed the facility to force residents to breathe in pollutants linked to long-term respiratory issues. What follows is a list of anecdotal quotes from residents, taken from Chow (2025):

• One resident suffered her first severe asthma attack in 15 years, describing a smell of gas wafting into her neighborhood that made her feel like her “chest was caving in.”
• “Our lives and our lungs are being sacrificed on the altar of their capitalistic exploitation,” noted another.
• And finally: “There is no amount of money that can persuade me to accept pollution killing me and my family.”

Tennessee state representative Justin Pearson speaking at a rally against xAI, April 25th, 2025.

Figure 5: (Chow, 2025)

A march against xAI, June 17th, 2025.

Figure 6: (Chow, 2025)

Figures 5 and 6 picture public unrest. The first captures Tennessee state representative Justin Pearson speaking at a rally against xAI on April 25th, 2025. The second is another march, again, against xAI, taken June 17th, 2025.

In January 2026, the EPA issued a final ruling clarifying that all gas turbines supplying steady power to a fixed facility—regardless of whether they are on trailers or intended for short-term use—must be permitted as stationary sources under the Clean Air Act (Kerr, 2026).

While xAI eventually secured stationary permits for some units in Memphis, it simply “copy-pasted” the unpermitted model just across the state line. As of March 2026, the company has been operating up to 27 unpermitted “temporary” turbines in Southaven, Mississippi, to power the same Colossus supercomputer network (Ireland, 2026).⁶

To suggest that those unhappy with the smog should “just leave” is not a solution; it is a normalization of corporate trespassing that treats local well-being as a secondary concern. No company should have the unilateral right to degrade a living situation without warrant; no person should be forced to make the choice between their homes and health. Some residents simply do not have the financial mobility to uproot their lives, making them captive to whatever pollutants the “invisible giant” breathes out. Within this framing, the choice of location for Colossus tells a more intentional story. Boxtown is a predominantly Black community with a median income of approximately $37,000—less resources to “fight back”. Critics argue it is no coincidence that the “biggest supercomputer in the world” was placed in a neighborhood with low political resistance and high economic vulnerability (Chow, 2025). Accepting this precedent in Memphis normalizes a concept that should be unthinkable, putting every community—including those in Illinois—at risk of becoming the next site.

The social dimension of the Memphis case is defined by a complete lack of transparency. The project was negotiated in secret, leaving the community completely in the dark until the facility was operational. This “ask-for-permission-later” approach is unfit for a community that has historically suffered from environmental hazards. The NAACP and Southern Environmental Law Center have since sent separate intent-to-sue notices for violations of the Clean Air Act, but the damage to the social fabric is already done (Chow, 2025; More Perfect Union, 2025; Hilt, 2026). Residents feel that their agency was traded for corporate speed, setting a dangerous precedent where tech companies can bypass local democratic processes as long as they promise “innovation”.

While the facility brings investment to the region, it also introduces massive ratepayer risk. If the grid requires billions in upgrades to support xAI’s expansion, those costs could be shifted onto the very families who are already struggling with the environmental fallout. Despite the outcry, the cycle of development continues: plans are already underway for an even larger data center in the nearby neighborhood of Whitehaven.

It is important to recognize the wider issue of industrial plants operating near residential areas. Abuse of residential zoning in Memphis predates xAI’s Colossus, and thus, the entire problem of industrial encroachment cannot be attributed to a single company. However, through the global excitement surrounding AI, this trend toward hasty infrastructure is being revived. The hasty model observed here is a repeatable strategy that can be applied to other vulnerable areas.

If Memphis represents this model at its most extreme, Illinois represents opportunity. Unlike South Memphis, where the industrial footprint is already deeply entrenched, the expansion in Illinois is not yet in an advanced stage. By examining local responses—from successful community opposition in Naperville to the rapid industrialization of DeKalb—this project will provide further context as to how the situation is unfolding in Illinois and brainstorm what can be done about the problem before it gets out of hand

In the Context of Illinois

Illinois provides a particularly useful setting for examining these dynamics because it contains examples across the full lifecycle of data center development. Within a single state, operational facilities coexist with newly proposed campuses and at least one high-profile rejected project. This range allows the analysis to move beyond hypothetical impacts and instead observe how expectations, negotiations, and outcomes differ depending on location and community context.

The following case studies therefore shift the discussion from general debates about AI infrastructure toward grounded observations drawn from Illinois communities themselves.

Existing Data Centers in Illinois: Environmental Baselines and Established Infrastructure

Existing facilities provide a baseline for understanding how data centers function once they become normalized parts of regional infrastructure.

Digital Realty’s ORD10 interconnection hub at 350 East Cermak Road, Chicago.

Figure 7: (350 East Cermak Road in Chicago | Digital Realty (19.5 MW), 2026)

Map view of the Digital Realty ORD10 site, Chicago.

Figure 8: (Google Maps, 2021a)

In downtown Chicago, sites such as Digital Realty’s ORD10 interconnection hub—figures 7 and 8—operate within dense commercial corridors where large industrial buildings and energy infrastructure have long histories. Public materials emphasize connectivity and modernization rather than environmental transformation, reflecting how impacts in urban environments are often absorbed into existing infrastructure systems. Environmental concerns in these settings tend to center on operational factors such as generator testing, rooftop mechanical systems, and construction disruptions rather than land-use change.

The Meta data center campus in DeKalb, Illinois.

Figure 9: (Google Maps, 2021b)

A different pattern appears in suburban and regional locations. The Meta data center campus in DeKalb—pictured in figure 9—illustrates how environmental impacts can develop incrementally through expansion. City communications describing the campus growth frame the project primarily through investment and development language while implicitly signaling increased utility capacity and infrastructure coordination (Meta Expanding DeKalb Data Center, 2016).

Stream Data Centers facility, Elk Grove Village, Illinois.

Figure 10: (Google Maps, 2024)

Similarly, Elk Grove Village has evolved into a concentrated data center cluster where multiple facilities operated by companies, such as Stream Data Centers, reinforce the area’s identity as a connectivity hub (Stream Data Centers, 2025). See figures 10 and 11.

In these environments, the environmental question shifts from individual projects to cumulative infrastructure demand, including repeated construction cycles and sustained energy requirements.⁷

Map view of the Stream Data Centers cluster, Elk Grove Village.

Figure 11: (Google Maps, 2024)

Socially, existing sites often have limited visible public debate, suggesting that once infrastructure becomes established, development is perceived as routine. Economically, municipalities frequently present these facilities as indicators of technological relevance and regional competitiveness, reinforcing acceptance over time.

Proposed and Under-Development Projects: Negotiation and Uncertainty

Proposed projects reveal impacts more clearly because communities encounter them before normalization occurs. The Edged “Project Vector” proposal near the existing Meta campus in DeKalb demonstrates how environmental concerns emerge during planning stages. Public hearings and local reporting highlight discussions surrounding lighting mitigation, stormwater management, and infrastructure integration, indicating attempts to anticipate nuisance impacts before construction begins (Edged Development Approved • DeKalb, IL, 2022).

In Minooka, planning materials and community outreach events surrounding a proposed Equinix data center show how environmental and economic narratives intersect. Village documents emphasize investment potential while residents raise questions about farmland conversion and long-term water use (Village of Minooka, 2025). Rural proposals such as the CyrusOne campus in Sangamon County further illustrate how large projected electrical loads and utility planning become central issues when projects enter agricultural landscapes, where industrial infrastructure represents a visible shift in land identity (Sangamon County, 2025).

Socially, these projects generate structured participation through hearings, open houses, and public comment periods, creating clearer records of negotiation between developers and residents. Economically, redevelopment proposals in Hoffman Estates and Mount Prospect are often framed as adaptive reuse of former corporate campuses, presenting data centers as successors to earlier economic eras rather than entirely new industrial activity (Compass Kicks off Development of Its First Campus in Illinois, 2024; ComEd and CloudHQ Break Ground on New Hyperscale Data Center in Mount Prospect, 2022). These cases highlight uncertainty, where benefits remain projected while impacts remain speculative.

Denied Development in Illinois: Community Limits and Upholding the Annoyance Threshold

The rejected Karis Critical proposal in Naperville provides an important counterpoint to both operational and proposed projects. Located on the former Lucent campus, the proposal underwent months of hearings and revisions before the city council ultimately denied the conditional use permit in January 2026. News coverage consistently identifies resident concerns related to diesel backup generators, air quality, noise, and proximity to homes as central factors influencing the decision (Pirc, 2026; Piekos, 2026).

Environmentally, the debate focused on routine operational impacts rather than catastrophic risk, demonstrating how cumulative everyday stressors can shape perception. Socially, the extended review process and high public turnout reflected sustained community engagement and skepticism toward mitigation assurances. Economically, supporters emphasized redevelopment and investment potential, while opponents questioned whether benefits justified long-term neighborhood impacts.

As a case study, Naperville illustrates that acceptance of AI infrastructure is not inevitable. When environmental concerns align with strong social mobilization and uncertain economic tradeoffs, communities may determine that a project exceeds their acceptable threshold. The denial therefore functions not as an anomaly but as evidence that local governance remains a decisive factor in shaping the trajectory of AI development.⁸

Platform Design and Analytical Methodology

Live platform: The People v. Hasty AI

GitHub repository: https://github.com/Laasya-73/SoReMo-S26-people-v-hasty-ai.git

Workspace Implementation

The deployed platform is organized as five workspaces:

Home: quick orientation and how to use the platform

Home workspace.

Figure 12.

Illinois Map: interactive layers and county/site inspection. Scroll down on the left-hand side of the screen to see all options for interacting with the map. Hover over counties and points for more details.

Illinois Map workspace.

Figure 13.

County Intelligence Briefing: county-specific summaries for communication. Adjust via the “Brief Settings” area; click “Generate County Brief” once done. See the “Download Brief (.pdf)” button at the bottom of the generated report.

County Intelligence Briefing workspace.

Figure 14.

Impact Scenario Studio: “what-if” testing with adjustable assumptions. Adjust via the “Studio Controls” area; click “Run Simulation” once done. See the “Download Note (.pdf)” button at the bottom of the generated report.

Impact Scenario Studio workspace.

Figure 15.

Data Center Registry Explorer: broader US/global context with source confidence. Adjust settings at the top.

Data Center Registry Explorer workspace.

Figure 16.

Analytical Architecture

The platform implements a layered narrative architecture with four interpretive stages:

1. Infrastructure footprint (where sites are now/planned),
2. Baseline stress context (air, water, energy, heat),
3. Population vulnerability context (demographic and socioeconomic),
4. Incremental burden under growth assumptions (scenario delta).

This design supports policy-facing interpretation by moving from descriptive mapping to structured burden comparison.

Platform Workflow Diagram: Home Workspace (project purpose + navigation) → Illinois Map Workspace (footprint + county stress layers) → Impact Scenario Studio (weight/assumption testing + delta), with County Intelligence Briefing (county-specific narrative + benchmarks) and Data Center Registry Explorer (Illinois + US/Global comparative coverage) feeding into Policy/Community Outputs (briefs, watchlists, decision prompts).

Figure 17: Platform Workflow Diagram

Mathematical Formulation

County Pressure Model

County pressure is modeled as a weighted linear index over site status counts.

\[P_{\text{current},\,c} = E_c \cdot w_E\]

\[P_{\text{planned},\,c} = E_c \cdot w_E + Pr_c \cdot w_{Pr} + D_c \cdot w_D\]

\[\Delta P_c = P_{\text{planned},\,c} - P_{\text{current},\,c}\]

Where for county \(c\):

• \(E_c\) = existing site count
• \(Pr_c\) = proposed site count
• \(D_c\) = denied site count
• \(w_E,\; w_{Pr},\; w_D\) = status weights

Default (recommended baseline) weights:

\[w_E = 1.0, \quad w_{Pr} = 1.5, \quad w_D = 0.5\]

Registry points are reference coverage and excluded from pressure scoring.

Pressure-score calculation example for Cook County: Existing sites 6 × 1 = 6, Proposed sites 4 × 1.5 = 6, Denied sites 0 × 0.5 = 0, for an active scenario total pressure score of 12.

Figure 18: Pressure-score Calculation Example for Cook County

Annoyance Threshold Model

To formalize cumulative burden, we define our take on the annoyance threshold as a normalized composite score:

\[A_c = \frac{1}{K} \sum_{k=1}^{K} Z_{c,k}\]

where \(Z_{c,k} \in [0, 100]\) is the percentile-normalized county value for indicator \(k\), and \(K\) includes selected burden dimensions (air quality, water stress, energy burden, social vulnerability, heat stress where available).

A county is flagged if:

\[A_c \ge \tau_A \quad \text{or} \quad \Delta P_c \ge \tau_{\Delta P}\]

with \(\tau_A\) (annoyance threshold) and \(\tau_{\Delta P}\) (pressure-growth trigger) user-adjustable in scenario analysis.

Annoyance Threshold Screening with threshold X = 67; counties exceeding the threshold are highlighted, with Kane, Cook, Winnebago, DuPage, and Lake among those flagged.

Figure 19: Annoyance Threshold Screening (X = 67)

Results

Results overview

This section presents empirical outputs from the platform in a staged sequence. We first report county-level quantitative screening results (pressure change and burden context) and then interpret those numbers through spatial outputs from the Illinois Map and Scenario Studio. Finally, we summarize results from public engagement. This ordering links numerical evidence to geospatial patterns and policy-relevant interpretation.

County pressure screening under baseline scenario

Table 3 reports modeled pressure dynamics for focal Illinois counties under the planned-buildout baseline. For each county, we show current pressure, planned pressure, and pressure change (\(\Delta P\)), followed by a categorical delta class and decision flag. This table is used as the primary screening output for identifying counties where projected infrastructure growth is most likely to increase cumulative burden.

Illinois County	Current Pressure	Planned Pressure	Pressure Delta (\(\Delta P\))	Delta Class	Status Flag
Cook	6.0	12.0	+6.0	High Burden	+ Pressure Alert
DeKalb	1.0	2.5	+1.5	Moderate-High	Pressure Alert
Sangamon	0.0	1.5	+1.5	Moderate-High	Pressure Alert
DuPage	0.0	0.5	+0.5	Low	Monitored

Table 3: County pressure screening under planned-buildout baseline scenario. Note: Delta class reflects relative increase in planned burden (higher ΔP = larger modeled increase).

Burden context snapshot for screened counties

To contextualize pressure change, Table 4 summarizes baseline burden indicators for the same screened counties. Poverty rate, minority share, and AQI P90 are shown as interpretable county-level exposure context variables. Together, Tables 3–4 distinguish where growth pressure is rising and whether it coincides with pre-existing social and environmental stress.

Illinois County	Poverty (%)	Minority (%)	AQI P90
Cook	13.3	59.5	94.0
DeKalb	15.9	29.1	0.0
Sangamon	6.4	36.6	76.0
DuPage	12.9	23.4	61.0

Table 4: Baseline burden context snapshot for screened counties. Note: Cook combines the largest modeled pressure increase with the strongest burden context among the four counties shown. Counties with both high (\(\Delta P\)) and elevated burden indicators are priority candidates for enhanced review, mitigation, and phased permitting.

Spatial Concentration of Infrastructure

The Illinois map shows spatial concentration in major corridor counties and metro-adjacent zones. Existing facilities cluster in already infrastructure-dense regions, while proposed/denied records identify areas of planning pressure and social contestation.

Infrastructure footprint view of Illinois with no county fill, showing site points clustered in northeastern corridor counties.

Figure 20: Infrastructure Footprint View (No County Fill)

Scenario-Driven Pressure Change

Under the recommended baseline weights, counties with both existing and proposed footprints show the largest positive \(\Delta P\). The scenario workspace confirms that score movement is transparent and mechanically traceable to explicit count and weight changes.

Scenario output in the Impact Scenario Studio for DeKalb County, showing baseline pressure, projected pressure, and the resulting delta alongside heat, grid-carbon, and confidence context.

Figure 21: Scenario Output in Impact Scenario Studio (DeKalb County)

Spatial Burden Overlap and Threshold Signaling

Overlay analysis shows counties where elevated pressure coincides with high percentile burden indicators (e.g., AQI, minority share, poverty, energy burden, water stress). These overlap counties constitute priority candidates for enhanced review, mitigation, and phased permitting.

Burden overlap layer mapping cumulative burden (air + energy) across Illinois counties by quintile.

Figure 22: Burden Overlap Layer (Cumulative Burden: Air + Energy)

Sensitivity to Assumptions

Changing weight profiles (e.g., cautious permitting vs fast-growth assumptions) shifts county rank order and watchlist size, demonstrating that uncertainty should be handled through explicit scenario comparison rather than hidden model defaults.

County watchlist under recommended baseline weights (Existing 1 | Proposed 1.5 | Denied 0.5), with Cook, Kane, DeKalb, Sangamon, Lake, and Champaign flagged.

Figure 23: Watchlist Under Recommended Baseline Weights

County watchlist under custom assumption weights (Existing 1 | Proposed 1.9 | Denied 0.6), showing a shifted rank order and watchlist composition.

Figure 24: Watchlist Under Custom Assumption Weights

Key Findings Summary

Across all analyses, five findings emerge. First, infrastructure concentration is spatially uneven, with the highest site density in northeastern Illinois and selected corridor counties. Second, county pressure scores increase most where existing and proposed footprints co-occur, indicating that cumulative burden is primarily driven by overlap rather than isolated projects. Third, burden-overlap mapping shows that several high-pressure counties also exhibit elevated environmental and social-context indicators (e.g., AQI, minority share, poverty, and energy burden), supporting priority screening for enhanced review. Fourth, sensitivity testing confirms that county ranking and watchlist composition change under alternative weighting assumptions; therefore, policy conclusions should be drawn from scenario comparison rather than a single fixed profile. Fifth, the county-level briefing and scenario workspaces translate model outputs into transparent, auditable decision artifacts, enabling nontechnical stakeholders to interpret pressure changes, threshold flags, and uncertainty in a consistent framework.

Public Engagement and Verification

To evaluate the real-world resonance of our “annoyance threshold” framework, a simplified version of this report and the interactive tool were shared across hyperlocal digital forums, including Reddit (r/Illinois) and Nextdoor. This outreach served a dual purpose: increasing civic awareness of AI infrastructure expansion and verifying the platform’s utility for community stakeholders.

The following table summarizes the reach of the project across public platforms as of May 2026. Links to each post are provided:

Platform	Total Views	Likes/Reactions	Comments/Engagements
r/Illinois (Reddit)	4,900	44	0
Nextdoor	431	6	2

Table 5: Results from Online Posts

Qualitative Analysis of Community Feedback

Resident feedback frequently highlighted persistent “humming noises” emanating from local facilities, supporting the inclusion of noise as a primary indicator of industrial nuisance.

While initial outreach on Reddit and Nextdoor achieved significant reach (over 5,000 combined views), the conversion to active engagement (comments and likes) remained low. Future work should investigate more effective interactive social media components that lower the friction for residents to move from passive reading to active participation, such as one-click representative contact tools integrated directly into the platform.

The disparity between views and engagement suggests that broad regional posts (e.g., r/Illinois) may capture general interest but lack the urgency of local neighborhood groups. Next steps should involve a more granular “neighborhood-by-neighborhood” strategy, focusing specifically on the digital hubs of communities where the “annoyance” is a daily physical reality rather than a speculative policy issue.

Discussion and Policy Implications

The “Keeler Garden” Green Wall

The concept of a “green wall” or vegetative buffer—inspired by this semester’s partnership with the Chicago-based organization Keeler Gardens—transforms a data center’s exterior into a multifunctional environmental asset. This works by mandating a minimum “Green Coverage” ratio (or some similar metric) in local zoning codes.

By implementing a dense strip of native trees and shrubs (16-32 feet thick), the facility gains a natural filtration system for nitrogen oxides and particulate matter emitted during generator testing—the exact pollutants documented as a primary stressor in the Memphis xAI case study. To tie it back to the concept of the annoyance threshold, this living barrier can lead to an increase in the auditory annoyance threshold by providing a psychological noise reduction effect (Lu et al., 2024), as it serves to muffle high-frequency mechanical noise. It can also mitigate the “Urban Heat Island” effect (United States Environmental Protection Agency, 2024), lowering the ambient temperature for both the residents and the facility itself.

• Community Win: Improved local aesthetics, buffered air quality, and the restoration of local biodiversity.
• Company Win: Lowered cooling costs due to reduced ambient heat and the security of a “social license”, per say, to operate without constant PR crises.

Ultimately, this initiative builds a bridge of trust between the industry and its neighbors. As a compromise, in areas where space is too tight for a full 32-foot buffer, companies can implement vertical “living facades” or provide direct funding to local organizations to develop off-site green spaces in the immediate neighborhood. (Note: Keeler Gardens can serve as a placeholder for any professional landscaping or state-funded ecological project.)

Operational Accountability During Emergency Situations

Operational accountability ensures that when local resources or the power grid are under pressure, the “invisible giant” is an active participant in the community’s stability, rather than a drain on it.

A water budgeting and drought-proof operations plan would require facilities to operate on a strict water budget, incorporating real-time leak detection and prioritizing reclaimed water for cooling. It would work by requiring developers to submit a “Water Impact Plan” as part of the zoning process. As a compromise, if reclaimed water infrastructure is not immediately available, the company must commit to a phased retrofit plan as municipal infrastructure matures.

• Community Win: Eliminates hidden strain on the municipal drinking water supply and protects local aquifers.
• Company Win: Reduces long-term water supply risks and prevents emergency operational restrictions during droughts/water scarcity scenarios.

Moving away from a “diesel-by-default” model, we propose that batteries handle all routine testing and short-term outages. Policies would cap annual generator testing hours and require public reporting of backup power events. Generators would be strictly reserved as a “true last resort” for catastrophic grid failures.

• Community Win: Significantly less diesel exhaust, reduced noise pollution from testing, and lower particulate emissions in residential corridors.
• Company Win: Improves overall system reliability, reduces fuel logistics/storage risks, and lowers wear and tear on backup generators.

Building on SB 4016: Ending “Cross-Subsidy”

To ensure that technological progress does not lead to economic injustice, we propose implementing structural protections that prevent residents from subsidizing industrial growth.

Transmission security agreements would require “letters of credit” from developers before breaking ground on massive grid upgrades. This ensures that if a “phantom” project is canceled mid-build, the developer—not the local ratepayers—is responsible for the multimillion-dollar substation already under construction.

Community benefit agreements could be triggered for any facility exceeding a specific Megawatt (MW) threshold. Fees would be indexed to a “dollar-per-MW per year” formula, ensuring the community’s benefit scales with the facility’s power consumption.

• Community Win: Direct funding for home weatherization, industrial-grade air filters for nearby schools, and local bill relief programs.
• Company Win: Secures the “social license” to operate and reduces the risk of costly litigation or PR crises.

Enforcing zoning to data centers where they’d “fit” is a strategy that uses data to pre-designate industrial brownfields and reuse sites as “preferred” for data center development. Minimum distance buffers from residential edges would be mandated unless extra, high-tier mitigation (like the Green Wall) is fully funded.

• Community Win: Prevents the “surprise” factor of industrial giants appearing next to homes, schools, or parks.
• Company Win: Provides a predictable roadmap with less public backlash and fewer expensive site redesigns.

Transparent reporting, but with a “safe harbor” (for legitimate security needs) would require data, but it must be balanced with operational safety. Companies would be required to publish aggregated monthly resource data (energy, water, generator runtime) to keep the community informed. This provision would ensure that specific tenant data or sensitive security details remain confidential while the impacts remain public.

• Community Win: Real data on energy, water, and generator runtime.
• Company Win: Avoids oversharing sensitive internal details while still meeting accountability.

Infrastructure funded by developers (i.e. grid upgrades) should serve the surrounding neighborhood as well. When a developer funds a grid upgrade, a portion of that work (such as feeder hardening) must directly improve local residential reliability. Incentives could be provided if these upgrades specifically integrate clean energy sources.

• Community Win: Reliability improvements and hard protections against bill spikes.
• Company Win: Guaranteed interconnection timeline and clearer cost responsibilities.

Limitations

Despite the promise of these solutions, several hurdles remain. Legacy facilities are much harder to retrofit than “greenfield” projects; while a new building in DeKalb can easily integrate a 32-foot vegetative buffer, a 20-year-old facility in Chicago’s South Loop faces significant spatial constraints. This means older neighborhoods may continue to experience a higher “annoyance factor” than areas with newer developments.

Furthermore, there is a significant enforcement asymmetry. Without the independent monitoring proposed in SB 4016, residents are still forced to rely on company-provided data, which historically lacks the granularity needed for true accountability. Finally, the speed of the AI race often outpaces the growth of nature-based solutions—a tree buffer takes years to reach its full filtration potential, while a data center can be operational in mere months. Recognizing these gaps is essential to ensuring our recommendations remain grounded in reality, serving as a framework for persistent advocacy rather than a one-time fix.

SoReMo Partnership: Keeler Gardens

A cornerstone of this project’s “recommendations” is our collaboration with Keeler Gardens, a Chicago-based organization dedicated to improving urban community health through nature-based connections. Their expertise in environmental sustainability provided the foundation for our “Green Wall” mitigation strategy, and gave us a good jump off point for the ideas that followed.