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Centers\n\n[![Explorer](https://img.shields.io/badge/explorer-live-brightgreen)](https://civic-interconnect.github.io/decision_explorer_data_centers/explorer/)\n[![Docs](https://img.shields.io/badge/docs-GitHub%20Pages-blue)](https://civic-interconnect.github.io/decision_explorer_data_centers/)\n[![CI Status](https://github.com/civic-interconnect/decision_explorer_data_centers/actions/workflows/ci-python-zensical.yml/badge.svg?branch=main)](https://github.com/civic-interconnect/decision_explorer_data_centers/actions/workflows/ci-python-zensical.yml)\n[![Link Check](https://github.com/civic-interconnect/decision_explorer_data_centers/actions/workflows/links.yml/badge.svg?branch=main)](https://github.com/civic-interconnect/decision_explorer_data_centers/actions/workflows/links.yml)\n[![Python 3.14+](https://img.shields.io/badge/python-3.14%2B-blue?logo=python)](#)\n[![MIT](https://img.shields.io/badge/license-see%20LICENSE-yellow.svg)](./LICENSE)\n\n\u003e Putting Data Centers in Context: Promise, Burden, and the Case for Sound Governance\n\n**[Try the Interactive Explorer](https://civic-interconnect.github.io/decision_explorer_data_centers/explorer/)**\nTo accept the example data and see the results, click the green \"Evaluate\" button in the lower right-hand corner of either:\n\n- The Example Policies data (TOML)\n- The Example Sites data (CSV)\n\n![View Results](./docs/images/view-results.png)\n\n![Edit Policy](./docs/images/edit-policy.png)\n\n![Edit Candidate Site](./docs/images/edit-candidate-site.png)\n\n## This Project\n\nThis project provides a structured framework for exploring data center siting\nand governance tradeoffs under explicit assumptions and constraints.\nIt:\n\n- frames the issue as one of infrastructure governance\n- recognizes the distribution of costs vs benefits\n- emphasizes integration with broader systems rather than isolated analysis\n\nThe goal is to make tradeoffs visible and inspectable across multiple dimensions.\n\n## Contribution\n\nThe contribution of this project is the framework for structured exploration,\nnot the specific values used in any given evaluation.\n\n- Constraints, thresholds, and weights are configurable\n- Assumptions are explicit and inspectable\n- Results are comparative and assumption-dependent\n\nThis project does not determine outcomes or recommend decisions.\nIt provides a way to examine how different assumptions and constraints shape outcomes.\n\n## Working Files\n\nWorking files are found in these areas:\n\n- **data/** - source inputs and scenario configuration\n- **docs/** - narrative, assumptions, and analysis\n- **src/** - implementation\n\n## Current Capabilities\n\n- Loads candidate sites from CSV and policy constraints from TOML\n- Evaluates hard-constraint admissibility for each site (PASS / FAIL)\n- Exports results as JSON for the web Explorer\n- Interactive web Explorer for non-technical users\n\n## Command Reference\n\n\u003cdetails\u003e\n\u003csummary\u003eShow command reference\u003c/summary\u003e\n\n### In a machine terminal (open in your `Repos` folder)\n\nAfter you get a copy of this repo in your own GitHub account,\nopen a machine terminal in your `Repos` folder:\n\n```shell\n# Replace username with YOUR GitHub username.\ngit clone https://github.com/username/decision_explorer_data_centers\n\ncd decision_explorer_data_centers\ncode .\n```\n\n### In a VS Code terminal\n\n```shell\n# Set Up the Environment\nuv self update\nuv python pin 3.14\nuv sync --extra dev --extra docs --upgrade\nuvx pre-commit install\n\n# Local format + lint\nuv run ruff format --check .\nuv run ruff check .\n\n# Pre-commit (enforce repo rules)\ngit add -A\nuvx pre-commit run --all-files\n# repeat if changes were made\ngit add -A\nuvx pre-commit run --all-files\n\n# Static + security + dependency checks\nuv run validate-pyproject pyproject.toml\nuv run deptry .\nuv run bandit -c pyproject.toml -r src\n\n# Tests (after static checks pass)\nuv run pytest --cov=src --cov-report=term-missing\n\nuv run python -m decision_explorer_data_centers.cli --candidates data/raw/example_candidates.csv --policy data/raw/example_policy.toml --output-json docs/data/results.json\n\n# Docs build (after everything passes)\nuv run zensical build\n\n# Commit and push\ngit add -A\ngit commit -m \"update\"\ngit push -u origin main\n```\n\n\u003c/details\u003e\n\n## Potential Benefits\n\n- **Tax revenue**: the most reliable benefit when negotiated well. In West Des Moines, Iowa, Microsoft's data centers are projected to generate over $2 billion in tax revenues over the agreement period ([Brookings](https://www.brookings.edu/articles/why-community-benefit-agreements-are-necessary-for-data-centers/)). Loudoun County, Virginia (the largest data center market in the world) now receives an estimated $890 million annually in data center tax revenue, nearly matching its entire operating budget, and has lowered its residential real estate tax rate incrementally as a result.¹\n\n- **Grid investment**: data center demand can accelerate grid upgrades that benefit all ratepayers, not just the facility ([Brookings](https://www.brookings.edu/articles/why-community-benefit-agreements-are-necessary-for-data-centers/)).\n\n- **Broadband**: some operators have built regional fiber networks as part of development agreements, enabling businesses, students, and telemedicine across rural areas that would otherwise lack connectivity ([Brookings](https://www.brookings.edu/articles/turning-the-data-center-boom-into-long-term-local-prosperity/)).\n\n- **University and workforce partnerships**: Microsoft partnered with Gateway Technical College in Wisconsin to launch a Datacenter Academy training more than 1,000 students in five years, and partnered with the University of Wisconsin-Madison on AI-driven research ([Brookings](https://www.brookings.edu/articles/turning-the-data-center-boom-into-long-term-local-prosperity/)).\n\n¹ Loudoun County figures: [Cardinal News](https://cardinalnews.org/2025/04/10/data-centers-can-bring-high-paying-jobs-and-millions-in-tax-revenue-is-that-what-southside-will-get/)\n\n## Resources\n\n### Energy demand - global and U.S.\n\n- International Energy Agency (IEA) Energy and AI (April 2025): https://www.iea.org/reports/energy-and-ai/energy-demand-from-ai\n  - Global: ~415 TWh in 2024 (1.5% of global electricity); projected to double to ~945 TWh by 2030 (~3%)\n  - U.S.: ~45% of global total in 2024; ~4% of all U.S. electricity\n  - Nearly half of U.S. capacity concentrated in five regional clusters (Northern Virginia, Dallas, Silicon Valley, Phoenix, Chicago area)\n  - U.S. data centers projected to consume more electricity by 2030 than all energy-intensive manufacturing combined\n  - AI is primary driver; accelerated servers growing ~30% annually\n- U.S. Energy Information Administration (EIA): https://www.eia.gov/\n- U.S. Department of Energy (DOE): https://www.energy.gov/\n\n### Data center energy and infrastructure\n\n- Lawrence Berkeley National Laboratory: https://datacenters.lbl.gov/\n- Electric Power Research Institute (EPRI): https://www.epri.com/\n\n### Water and environmental context\n\n- U.S. Geological Survey (USGS): https://www.usgs.gov/\n- USGS Water Use in the United States: https://www.usgs.gov/mission-areas/water-resources/science/water-use-united-states\n- World Resources Institute (WRI): https://www.wri.org/\n\n### Policy and governance\n\n- National Conference of State Legislatures (NCSL): https://www.ncsl.org/\n- Federal Energy Regulatory Commission (FERC): https://www.ferc.gov/\n\n### Industry practices and metrics\n\n- Uptime Institute: https://uptimeinstitute.com/\n- Green Grid: https://www.thegreengrid.org/\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcivic-interconnect%2Fdecision_explorer_data_centers","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fcivic-interconnect%2Fdecision_explorer_data_centers","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcivic-interconnect%2Fdecision_explorer_data_centers/lists"}