Governors’ races are being increasingly buffeted by the toxic politics of data centers
As the midterm elections approach, governors across the United States are increasingly caught in the crosshairs of growing public opposition to data centers. These massive server warehouses, built to support artificial intelligence and cloud computing, are facing scrutiny over their energy consumption and impact on local communities.
In Pennsylvania, Governor Josh Shapiro has implemented stricter guardrails for data center projects, requiring them to meet certain standards, such as paying for their electricity, limiting water usage, and gaining local approval before state approval. This move comes as a response to the backlash from communities across the state, who are protesting proposed data centers.
Governor Shapiro, a potential 2028 presidential candidate, is facing pressure from his Republican opponent, Stacy Garrity, as residents in towns like Archbald Borough express their discontent over proposed data center projects. The developer behind six campuses of about 50 server warehouses in Archbald Borough has faced a community uprising and a lawsuit from one developer, with motions filed to force the recusal of six council members.
In Texas, Democratic challenger Gina Hinojosa has launched a TV ad targeting Republican Governor Greg Abbott, accusing him of "selling you out" to data center executives and companies. Hinojosa has been exploiting the growing discontent in rural, Republican strongholds, where residents are worried about the impact of data centers on rural life, ranchland, and dwindling water supplies.
As data center projects have been met with rejection in local zoning and permitting board votes across the U.S., residents are increasingly concerned about the loss of open space, farmland, forest, and rural character, as well as the potential damage to quality of life, property values, and health from on-site diesel generators and constant server hum.
Written by urgent.news from Winnipeg Free Press's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.