https://github.com/civic-interconnect/decision_explorer_data_centers
Structured framework for exploring data center siting and governance tradeoffs under explicit, configurable constraints.
https://github.com/civic-interconnect/decision_explorer_data_centers
civic-technology data-centers decision-support infrastructure-governance python reproducible-research ruff siting-analysis uv zensical
Last synced: 2 months ago
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Structured framework for exploring data center siting and governance tradeoffs under explicit, configurable constraints.
- Host: GitHub
- URL: https://github.com/civic-interconnect/decision_explorer_data_centers
- Owner: civic-interconnect
- License: mit
- Created: 2026-03-26T13:39:12.000Z (3 months ago)
- Default Branch: main
- Last Pushed: 2026-03-27T01:11:15.000Z (3 months ago)
- Last Synced: 2026-03-27T07:22:36.284Z (3 months ago)
- Topics: civic-technology, data-centers, decision-support, infrastructure-governance, python, reproducible-research, ruff, siting-analysis, uv, zensical
- Language: Python
- Homepage: https://civic-interconnect.github.io/decision_explorer_data_centers/explorer/
- Size: 1.5 MB
- Stars: 1
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
- Citation: CITATION.cff
- Agents: .github/AGENTS.md
Awesome Lists containing this project
README
# Decision Explorer: Data Centers
[](https://civic-interconnect.github.io/decision_explorer_data_centers/explorer/)
[](https://civic-interconnect.github.io/decision_explorer_data_centers/)
[](https://github.com/civic-interconnect/decision_explorer_data_centers/actions/workflows/ci-python-zensical.yml)
[](https://github.com/civic-interconnect/decision_explorer_data_centers/actions/workflows/links.yml)
[](#)
[](./LICENSE)
> Putting Data Centers in Context: Promise, Burden, and the Case for Sound Governance
**[Try the Interactive Explorer](https://civic-interconnect.github.io/decision_explorer_data_centers/explorer/)**
To accept the example data and see the results, click the green "Evaluate" button in the lower right-hand corner of either:
- The Example Policies data (TOML)
- The Example Sites data (CSV)
## This Project
This project provides a structured framework for exploring data center siting
and governance tradeoffs under explicit assumptions and constraints.
It:
- frames the issue as one of infrastructure governance
- recognizes the distribution of costs vs benefits
- emphasizes integration with broader systems rather than isolated analysis
The goal is to make tradeoffs visible and inspectable across multiple dimensions.
## Contribution
The contribution of this project is the framework for structured exploration,
not the specific values used in any given evaluation.
- Constraints, thresholds, and weights are configurable
- Assumptions are explicit and inspectable
- Results are comparative and assumption-dependent
This project does not determine outcomes or recommend decisions.
It provides a way to examine how different assumptions and constraints shape outcomes.
## Working Files
Working files are found in these areas:
- **data/** - source inputs and scenario configuration
- **docs/** - narrative, assumptions, and analysis
- **src/** - implementation
## Current Capabilities
- Loads candidate sites from CSV and policy constraints from TOML
- Evaluates hard-constraint admissibility for each site (PASS / FAIL)
- Exports results as JSON for the web Explorer
- Interactive web Explorer for non-technical users
## Command Reference
Show command reference
### In a machine terminal (open in your `Repos` folder)
After you get a copy of this repo in your own GitHub account,
open a machine terminal in your `Repos` folder:
```shell
# Replace username with YOUR GitHub username.
git clone https://github.com/username/decision_explorer_data_centers
cd decision_explorer_data_centers
code .
```
### In a VS Code terminal
```shell
# Set Up the Environment
uv self update
uv python pin 3.14
uv sync --extra dev --extra docs --upgrade
uvx pre-commit install
# Local format + lint
uv run ruff format --check .
uv run ruff check .
# Pre-commit (enforce repo rules)
git add -A
uvx pre-commit run --all-files
# repeat if changes were made
git add -A
uvx pre-commit run --all-files
# Static + security + dependency checks
uv run validate-pyproject pyproject.toml
uv run deptry .
uv run bandit -c pyproject.toml -r src
# Tests (after static checks pass)
uv run pytest --cov=src --cov-report=term-missing
uv 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
# Docs build (after everything passes)
uv run zensical build
# Commit and push
git add -A
git commit -m "update"
git push -u origin main
```
## Potential Benefits
- **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
- **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/)).
- **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/)).
- **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/)).
¹ 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/)
## Resources
### Energy demand - global and U.S.
- International Energy Agency (IEA) Energy and AI (April 2025): https://www.iea.org/reports/energy-and-ai/energy-demand-from-ai
- Global: ~415 TWh in 2024 (1.5% of global electricity); projected to double to ~945 TWh by 2030 (~3%)
- U.S.: ~45% of global total in 2024; ~4% of all U.S. electricity
- Nearly half of U.S. capacity concentrated in five regional clusters (Northern Virginia, Dallas, Silicon Valley, Phoenix, Chicago area)
- U.S. data centers projected to consume more electricity by 2030 than all energy-intensive manufacturing combined
- AI is primary driver; accelerated servers growing ~30% annually
- U.S. Energy Information Administration (EIA): https://www.eia.gov/
- U.S. Department of Energy (DOE): https://www.energy.gov/
### Data center energy and infrastructure
- Lawrence Berkeley National Laboratory: https://datacenters.lbl.gov/
- Electric Power Research Institute (EPRI): https://www.epri.com/
### Water and environmental context
- U.S. Geological Survey (USGS): https://www.usgs.gov/
- USGS Water Use in the United States: https://www.usgs.gov/mission-areas/water-resources/science/water-use-united-states
- World Resources Institute (WRI): https://www.wri.org/
### Policy and governance
- National Conference of State Legislatures (NCSL): https://www.ncsl.org/
- Federal Energy Regulatory Commission (FERC): https://www.ferc.gov/
### Industry practices and metrics
- Uptime Institute: https://uptimeinstitute.com/
- Green Grid: https://www.thegreengrid.org/