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Map Shows States Where Data Centers Are on the Ballot

Data centers are on ballots in eight states as voters weigh AI growth, energy costs, tax breaks, and local control.

Voters in eight states will face or have recently voted on local ballot measures concerning data center development as infrastructure for artificial intelligence gains importance in the midterm elections. Ballotpedia has identified at least 39 measures in California, Florida, Illinois, Kansas, Michigan, Nevada, Ohio and Wisconsin.

Thirty-five of these will be decided during the November election, while four were voted on earlier in the year. These proposals vary from outright bans to advisory questions, zoning protections, and conditions on tax incentives. Concerns about data centers include strain on electric grids, water supplies, land, and potential tax breaks for residents.

Supporters argue they are crucial for America's AI ambitions and can create jobs and revenue. This debate is influencing midterm races, with candidates being judged on their support for tax incentives, projects or restrictions on ratepayers. In Ohio, Democratic Senate candidate Sherrod Brown has criticized Republican Senator Jon Husted for his role in the state's data center growth, while Husted has focused on consumer protections.

The Newsweek AI Policy Scorecard evaluates lawmakers' stances on AI-related issues, including data centers and energy. In California, Alhambra will vote to ban data centers, while Monterey Park has already banned them. In Edgewater, voters will decide to prohibit new data centers, expansions and operations. Illinois has seven measures, including advisory ones, in Bloomington and Madison County.

Kansas has three cities voting on prohibitions, with El Dorado targeting "high-impact" data centers and Emporia focusing on electricity consumption. Michigan's only confirmed measure is already decided, while Michigan's Augusta voters will decide on land use.

Written by urgent.news from Newsweek's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.

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