Governing AI’s Footprint: A Scalable Human-AI Workflow to Extract Zoning Codes for Data Centers and Renewable Energy Sitting

(In Progress)

This work is in development with Zhixuan Qi, Sarah Mills.

Abstract

As generative AI systems drive demand for computational infrastructure, their societal impacts increasingly intersect with environmental strain, land use conflict, and opaque regulatory frameworks. Data centers consume vast energy and water resources, while facing fragmented, outdated, and inconsistent zoning regulations. Similarly, renewable energy facilities intended to offset these environmental costs are hindered by slow permitting processes and ambiguous land use designations. Although both are critical to supporting AI systems, the siting and permitting processes for data centers and renewables remain opaque to researchers, industries, and policy makers. Zoning codes—the key policy instruments governing land use—are decentralized, poorly structured, and highly heterogeneous, making them difficult to parse through manual review or rule-based approaches.

We propose a human-AI collaboration workflow to extract structured information from zoning codes related to data centers and renewable energy facilities. Our aims are threefold: (1) design and evaluate a human-AI workflow for extracting structured, high-validity zoning data from complex legal documents; (2) build and validate a multi-state zoning database covering six states: Illinois, Indiana, Michigan, Minnesota, Ohio, and Wisconsin; and (3) develop a publicly accessible web tool that enables users to search, filter, and trace regulatory citations back to original zoning PDFs.

This workflow has the potential to scale zoning data creation and analysis across jurisdictions and land use topics—enabling more agile, transparent, and equitable policy planning. The resulting dataset will support immediate planning efforts in the Midwest and offer a replicable framework for responsible AI governance in land use, helping ensure that AI-era infrastructure development is sustainable, publicly accountable, and policy-aligned.

This project is funded by MIDAS PODS and Microsoft.

Keywords: data center; renewable energy; zoning; AI governance