# Research Ideas & Future Work

This document outlines potential research directions, data improvements, and analyses that could extend the current US Data Centers geospatial research infrastructure.

## Priority Next Steps

### 1. Backfill Power Capacity Data
**Status**: Only 5.9% of facilities have `power_mw` values (108/1,833)

**Approach**:
- Scrape Baxtel data center database (requires paid subscription)
- Use Data Center Map API/scraping
- Cross-reference with utility interconnection queue filings
- FOIA requests to state utility commissions for large loads

**Research Impact**: 
- Enable capacity-weighted concentration metrics (current analyses are facility-count only)
- Correlate power capacity with demographic/environmental variables
- Identify "hyperscale" facilities (>100 MW) vs. edge/enterprise (<10 MW)

**Implementation**:
```python
# Add capacity-weighted HHI calculation to analyze_dc_tract_concentration.py
capacity_weighted_hhi = sum((mw_i / total_mw) ** 2 for mw_i in tract_capacities)
```

---

### 2. Operator Name Deduplication
**Status**: String fragmentation inflates operator counts ("Meta" vs. "Meta, Inc.", AWS variants)

**Approach**:
- Create `operator_mapping` table with canonical names
- Use fuzzy matching (e.g., `fuzzywuzzy` library) to standardize
- Add `operator_canonical` column to `master_data_centers`

**Research Impact**:
- Accurate hyperscaler market share analysis
- Study operator-specific siting strategies (AWS hydro, Microsoft nuclear, Meta solar)
- Enable "operator power" political economy analyses

**Script**:
```python
# operators_dedupe.py
import pandas as pd
from fuzzywuzzy import process

# Load unique operators
operators = pd.read_sql("SELECT DISTINCT operator FROM master_data_centers", conn)

# Manual + fuzzy matching to canonical names
canonical_map = {
    "Meta": ["Meta", "Meta, Inc.", "Meta Platforms", "Facebook"],
    "Amazon Web Services": ["AWS", "Amazon", "Amazon Web Services"],
    # ... etc.
}
```

---

### 3. Water Stress Overlay
**Status**: 257 HUC8 watersheds contain data centers; 15 watersheds hold 50% of facilities

**Priority**: HIGH - Critical for environmental impact analysis

**Approach**:
- Join to USGS WaterWatch streamflow data
- Add USGS Drought Watch indicators by HUC8
- Correlate data center density with:
  - Groundwater depletion rates
  - Surface water withdrawal permits
  - Drought frequency/severity (USDM historical data)

**Key Watersheds for Focus**:
- **Middle Potomac-Catoctin** (HUC8 02070008): 235 DCs (12.8% of US total) - Loudoun/Ashburn
- **Middle Potomac-Anacostia-Occoquan** (02070010): 111 DCs - Fairfax/inner Loudoun
- **Coyote** (18050003): 88 DCs - Silicon Valley
- **Upper Scioto** (05060001): 73 DCs - Columbus OH
- **Umatilla** (17070103): 29 DCs - AWS-exclusive watershed

**Research Questions**:
- Are data centers sited in water-stressed watersheds?
- Do high-density clusters (Loudoun County, Columbus OH) face water constraints?
- Compare water stress in hyperscaler non-metro sites (Columbia River corridor) vs. metro clusters
- Does single-operator watershed capture (Umatilla = AWS only) correlate with water availability?

**Tables to Create**:
- `watershed_water_stress` - HUC8-level water stress indicators
- `data_center_water_risk` - Per-facility water-stress exposure

**Notebook**: `water_stress_analysis.ipynb`

---

### 4. Opposition Cases Overlay
**Status**: 18 geocoded opposition cases in `opposition_cases_geocoded` table (loaded but unused)

**Approach**:
- Expand dataset: Compile additional cases of rejected/delayed data center proposals from news archives
- Geocode all opposition cases, join to demographics/hazards
- Test hypotheses:
  - Do wealthier/more educated communities successfully block projects?
  - Are opposition cases more common in water-stressed or drought-prone areas?
  - Do smaller non-metro communities have less bargaining power?
  - Does clustered vs. isolated location predict opposition likelihood?

**Research Questions**:
- What predicts opposition success?
- Are opposition cases spatially clustered?
- Do demographics differ between accepted vs. rejected sites?
- Correlation with FEMA hazard exposure scores?

**Analysis Plan**:
```sql
-- Join opposition cases to demographics
SELECT o.*, ct.median_household_income, ct.bachelors_or_higher_pct
FROM opposition_cases_geocoded o
JOIN _dc_census_tract_acs_2024 ct 
  ON ST_Contains(ct.geom, o.geom);
```

**Output**: `opposition_cases_analysis.md`

---

### 5. IM3 Forward Projection Integration
**Status**: IM3 model data loaded in `im3_state_projected_moderate_50` (328 rows) and `im3_projected_state_demand_summary` (31 rows)

**Approach**:
- Load IM3 projected demand scenarios (2030, 2040, 2050)
- Overlay projected growth with:
  - Current grid saturation (% of generation within 50 km)
  - Water stress indicators
  - Land availability (zoned industrial parcels)
- Identify regions where projected demand may exceed infrastructure capacity

**Grid Saturation Context** (from current analysis):
- **New Jersey**: 83% of grid within 50 km of DC
- **Nevada**: 75%
- **Tennessee**: 70%
- **Oregon**: 68%
- **Arizona**: 56%
- **Virginia**: 50%

**Research Questions**:
- Which states face grid saturation from data center growth?
- Are projected sites in water-stressed watersheds?
- Does IM3 assume spatial distribution patterns consistent with current siting?
- Can states with >50% grid saturation accommodate projected demand?

**Implementation**:
```sql
-- Compare current saturation to IM3 projected demand
SELECT 
  current.state,
  current.dc_count,
  current.pct_grid_saturated,
  proj.facility_count AS projected_new_facilities,
  proj.total_it_mw AS projected_new_mw
FROM state_grid_saturation current
JOIN im3_projected_state_demand_summary proj ON current.state = proj.state
WHERE current.pct_grid_saturated > 50
ORDER BY current.pct_grid_saturated DESC;
```

**Notebook**: `im3_projection_overlay.ipynb`

---

## Ready-to-Run Analyses Enabled by New Context Tables

**Status**: Four one-row-per-facility context tables are now loaded and documented:
- `data_center_historical_climate`
- `data_center_usdm_drought_exposure`
- `data_center_hms_smoke_exposure`
- `data_center_election_context`

These make several publishable descriptive analyses possible without another major ingestion step.

### Climate Exposure and Cooling Burden
**Core idea**: Data centers are energy-intensive cooling loads. The historical climate table lets us ask whether facilities are already sited in hotter, wetter-bulb, or more cooling-intensive climates.

**Research Questions**:
- Are clustered facilities in hotter or more humid climate regimes than isolated facilities?
- Do hyperscalers choose cooler/non-metro climates more often than colocation providers?
- Are facilities with high `cooling_degree_days_c` or `extreme_wet_bulb_days` also near constrained grids?
- Do hotter sites overlap with lower-income or politically less powerful communities?

**Suggested Output**: `output/data_center_climate_exposure_summary.md`

**Starter Query**:
```sql
SELECT
    dc.state,
    COUNT(*) AS facilities,
    AVG(c.mean_annual_temperature_c) AS mean_temp_c,
    AVG(c.annual_cooling_degree_days_c_mean) AS annual_cdd_c,
    AVG(c.extreme_wet_bulb_days) AS extreme_wet_bulb_days
FROM master_data_centers dc
JOIN data_center_historical_climate c USING (master_id)
GROUP BY dc.state
HAVING COUNT(*) >= 10
ORDER BY annual_cdd_c DESC;
```

### Drought Exposure and Water-Use Politics
**Core idea**: The USDM summary table makes drought exposure measurable at each facility, and it can be joined to HUC8 watersheds, opposition cases, and climate metrics.

**Research Questions**:
- Which major clusters have the highest share of weeks in D2+ drought?
- Are water-sensitive regions still attracting new or projected facilities?
- Are opposition cases more common where `pct_weeks_in_d2_or_worse` or `longest_d2_streak_weeks` is high?
- Do non-metro hyperscaler sites trade cheaper land/power for higher drought exposure?

**Suggested Output**: `output/data_center_drought_water_risk_summary.md`

**Starter Query**:
```sql
SELECT
    w.huc8,
    w.huc8_name,
    COUNT(*) AS facilities,
    AVG(d.pct_weeks_in_d2_or_worse) AS avg_pct_d2_or_worse,
    MAX(d.longest_d2_streak_weeks) AS max_d2_streak_weeks
FROM data_center_watershed_huc8 w
JOIN data_center_usdm_drought_exposure d USING (master_id)
GROUP BY w.huc8, w.huc8_name
HAVING COUNT(*) >= 5
ORDER BY avg_pct_d2_or_worse DESC;
```

### Wildfire Smoke, Operational Resilience, and Worker Exposure
**Core idea**: Smoke exposure is a climate-adaptation issue for facility operations and for workers who build, maintain, and secure these sites.

**Research Questions**:
- Are facilities in the West and Mountain West systematically more smoke-exposed?
- Do major clusters create regional redundancy risk because many facilities share the same smoke exposure profile?
- Are smoke-exposed data centers in communities already facing higher FEMA NRI risk or lower resilience scores?
- Do smoke exposure patterns differ by operator strategy?

**Suggested Output**: `output/data_center_smoke_resilience_summary.md`

**Starter Query**:
```sql
SELECT
    dc.state,
    COUNT(*) AS facilities,
    AVG(s.pct_days_with_any_smoke) AS avg_any_smoke_days,
    AVG(s.pct_days_with_heavy_smoke) AS avg_heavy_smoke_days,
    MAX(s.longest_heavy_smoke_streak_days) AS max_heavy_smoke_streak
FROM master_data_centers dc
JOIN data_center_hms_smoke_exposure s USING (master_id)
GROUP BY dc.state
HAVING COUNT(*) >= 10
ORDER BY avg_heavy_smoke_days DESC;
```

### Political Geography of Host Communities
**Core idea**: `data_center_election_context` provides a rough but reusable local political context for each facility. It is not a causal measure of support/opposition, but it can help frame siting politics and legislative outcomes.

**Research Questions**:
- Are data centers more common in precincts with stronger Democratic or Republican vote shares?
- Do clustered and isolated facilities sit in different local political environments?
- Are opposition cases associated with precinct partisanship, turnout, or close elections?
- Do state-level data center bills emerge from states where host precincts differ from statewide political averages?

**Suggested Output**: `output/data_center_political_geography_summary.md`

**Starter Query**:
```sql
SELECT
    dc.state,
    COUNT(*) AS facilities,
    AVG(ec.democratic_votes / NULLIF(ec.total_votes, 0)) AS avg_dem_vote_share,
    AVG(ec.republican_votes / NULLIF(ec.total_votes, 0)) AS avg_rep_vote_share
FROM master_data_centers dc
JOIN data_center_election_context ec USING (master_id)
WHERE ec.total_votes > 0
GROUP BY dc.state
HAVING COUNT(*) >= 10
ORDER BY facilities DESC;
```

### Compound Exposure Index
**Core idea**: Combine NRI, historical climate, drought, smoke, watershed concentration, and demographics into a transparent screening index for cumulative exposure.

**Research Questions**:
- Which facilities or clusters have high climate, drought, smoke, and FEMA risk simultaneously?
- Are compound-exposure sites demographically different from lower-exposure sites?
- Do projected IM3 facilities fall into lower- or higher-risk exposure profiles than current facilities?

**Implementation Notes**:
- Standardize each indicator as a percentile rank before combining.
- Keep the index descriptive and auditable; avoid black-box weighting.
- Report sensitivity using equal weights, environment-only weights, and infrastructure-weighted variants.

**Suggested Output**: `output/data_center_compound_exposure_index.csv`

**Starter Query**:
```sql
WITH joined AS (
    SELECT
        dc.master_id,
        dc.name,
        dc.state,
        c.annual_cooling_degree_days_c_mean,
        d.pct_weeks_in_d2_or_worse,
        s.pct_days_with_heavy_smoke,
        n."RISK_SCORE"
    FROM master_data_centers dc
    LEFT JOIN data_center_historical_climate c USING (master_id)
    LEFT JOIN data_center_usdm_drought_exposure d USING (master_id)
    LEFT JOIN data_center_hms_smoke_exposure s USING (master_id)
    LEFT JOIN data_center_nri_exposure n USING (master_id)
)
SELECT *
FROM joined
ORDER BY
    annual_cooling_degree_days_c_mean DESC NULLS LAST,
    pct_weeks_in_d2_or_worse DESC NULLS LAST,
    pct_days_with_heavy_smoke DESC NULLS LAST
LIMIT 50;
```

---

## Methodological Extensions

### 6. Time-Series Analysis of Cluster Growth
**Approach**:
- Use `rfs_year` (ready for service) from cable data and EIA generator vintage
- Reconstruct data center siting over time (requires RFS dates for facilities)
- Animate cluster formation in interactive map

**Research Questions**:
- Did Ashburn VA become dominant before or after major cable landings?
- Do clusters grow via agglomeration (new facilities near existing) or simultaneous build-out?
- Correlation between energy infrastructure build-out and data center growth

**Data Needed**:
- Facility RFS dates (scrape from press releases, Baxtel historical data)
- Historical tract demographics (decennial Census + ACS back to 2000)

---

### 7. Network Effects: Fiber Route Proximity
**Status**: Current analysis tests submarine cable proximity (negative result)

**Approach**:
- Obtain fiber optic backbone route GIS data (from FCC, carriers, or Infrapedia)
- Test proximity to long-haul fiber routes (not just submarine cables)
- Hypothesis: Data centers cluster near fiber, not cables

**Data Sources**:
- FCC Form 477 fiber deployment data
- Infrapedia fiber route database
- State-level fiber maps (e.g., Virginia Broadband Map)

**Expected Result**: Positive correlation (unlike submarine cables)

---

### 8. Land Use & Zoning Analysis
**Approach**:
- Join data centers to local zoning classifications (industrial, commercial, etc.)
- Analyze land prices in data center tracts before/after facility construction
- Correlate with property tax revenues

**Research Questions**:
- Do data centers drive local property value increases?
- Are they preferentially sited in already-zoned industrial areas?
- Do host communities capture tax base growth?

**Data Sources**:
- Zillow Home Value Index (ZHVI) by ZIP
- ATTOM property tax assessments
- Municipal zoning GIS layers (city-specific, requires scraping/FOIA)

---

### 9. Environmental Justice Scoring
**Approach**:
- Compare data center host tracts to EPA's EJScreen indices
- Add CalEnviroScreen-style burden/benefit framework
- Test if data centers increase cumulative environmental burdens

**Metrics**:
- Air quality (PM2.5, ozone)
- Hazardous waste proximity
- Superfund site proximity
- Heat island effect (LST from Landsat)
- Noise pollution (traffic, cooling systems)

**Expected Challenge**: Data centers may improve local metrics (compared to heavy industry) but increase water/energy consumption

---

## Policy & Political Economy Research

### 10. Tax Incentive Analysis
**Approach**:
- Compile state/local tax incentives for data center siting (property tax abatements, sales tax exemptions)
- Create `data_center_incentives` table with per-facility incentive details
- Correlate incentive generosity with:
  - State fiscal health
  - Local government bargaining power
  - Facility size/operator

**Research Questions**:
- Do weaker fiscal states offer larger incentives?
- Are incentives regressive (larger for hyperscalers)?
- Do incentives predict siting decisions (natural experiment approach)?

**Data Sources**:
- Good Jobs First Subsidy Tracker
- State economic development agency press releases
- Local news archives

---

### 11. Employment & Labor Market Effects
**Approach**:
- Join to BLS Quarterly Census of Employment and Wages (QCEW) by ZIP/county
- Identify "data center construction boom" periods (before/after major facility openings)
- Analyze employment effects in:
  - Construction (NAICS 23)
  - Transportation/warehousing (NAICS 48-49)
  - Professional services (NAICS 54)

**Research Questions**:
- Do data centers create durable local employment?
- Are jobs filled by local residents or commuters?
- Wage effects in host tracts?

**Data Sources**:
- BLS QCEW
- Census LEHD Origin-Destination Employment Statistics (LODES)

---

### 12. Energy Cost Pass-Through
**Approach**:
- Join to state-level electricity rate data (EIA, utility rate tracker)
- Test if data center density correlates with residential rate increases
- Natural experiment: Compare rate trajectories in high-DC vs. low-DC states

**Research Questions**:
- Do data centers drive residential rate increases (capacity cost allocation)?
- Are rate increases concentrated in utility service territories with large data center loads?
- Do states with retail choice (deregulated markets) see different effects?

**Data Sources**:
- EIA Form 861 (retail rates by state/utility)
- Utility rate case filings (state public utility commissions)

---

## Data Quality & Infrastructure Improvements

### 13. Address Validation & Geocoding Refinement
**Approach**:
- Re-geocode the 45 facilities using city-precision fallback
- Use USPS address validation API
- Cross-reference with Google Maps satellite imagery (manual review)

**Implementation**:
```python
# Re-run geocoding with stricter thresholds
python3 load_postgis_data_centers.py --revalidate-addresses
```

---

### 14. OSM Continuous Monitoring
**Approach**:
- Set up automated Overpass API queries (daily/weekly)
- Detect new OSM data center tags
- Alert for review + merge into `master_data_centers`

**Implementation**:
- Cron job running `load_postgis_osm_data_centers.py --update-only`
- Slack/email notification on new facilities

---

### 15. Broadband Speed Validation
**Approach**:
- Cross-reference FCC BDC provider data with Ookla Speedtest results
- Test if data center host tracts have faster actual speeds (not just availability)

**Hypothesis**: Data center presence correlates with infrastructure investment → higher speeds

**Data Sources**:
- Ookla Open Data (aggregated Speedtest results by tile)
- FCC Measuring Broadband America

---

## Visualization & Communication

### 16. Interactive Story Map
**Approach**:
- Build Scrollama.js narrative map
- Sections:
  1. National overview (cluster map)
  2. Ashburn VA zoom (dominance of single region)
  3. Demographics comparison (host vs. national)
  4. Water stress hot spots
  5. Energy infrastructure saturation

**Output**: `story_map.html` (standalone web page)

---

### 17. Policy Brief Generation
**Approach**:
- Auto-generate policy briefs from analysis outputs
- Targeted audiences:
  - State legislators (energy/water policy)
  - Local governments (tax incentive negotiation)
  - Environmental justice advocates

**Template**:
```markdown
# Data Center Siting in [STATE]: Key Facts for Policymakers

- **[STATE] hosts X% of US data centers** (rank: #Y)
- **Host communities are Z% wealthier** than state average
- **A% of state generation is within 50 km of a data center**
- **Top watershed holds B facilities** (water stress: [HIGH/MEDIUM/LOW])
```

---

### 18. Comparative International Analysis
**Approach**:
- Extend methodology to EU, Canada, Australia
- Compare siting patterns (e.g., Nordic countries = renewable energy, cold climate)
- Test if "concentrated costs / dispersed benefits" holds internationally

**Data Sources**:
- OpenStreetMap (global coverage)
- Eurostat demographics
- IEA energy data
- TeleGeography global cable data (already available)

**Research Questions**:
- Are US patterns unique (tax-driven siting) vs. EU (regulatory constraints)?
- Do Nordic countries see more equitable distribution?

---

## Speculative / Long-Term Ideas

### 19. AI Demand Forecasting
**Approach**:
- Train ML model to predict data center siting
- Features: demographics, energy capacity, fiber proximity, tax rates, water availability
- Test on historical data (train on pre-2015, test on 2015-2025)

**Use Case**: 
- Identify "likely future sites" for proactive policy intervention
- Warn communities of potential incoming projects

---

### 20. Cooling Technology Analysis
**Approach**:
- Classify facilities by cooling type (air, water, hybrid)
- Correlate with:
  - Climate (CDD: cooling degree days)
  - Water availability
  - Facility size

**Data Sources**:
- Manual classification from news/press releases
- FOIA requests to water utilities (cooling water withdrawal permits)

**Research Questions**:
- Are water-cooled facilities concentrated in water-stressed regions (paradox)?
- Do hyperscalers use more efficient cooling (e.g., Meta's Prineville OR evaporative cooling)?

---

### 21. Bitcoin Mining Facilities
**Approach**:
- Overlay cryptocurrency mining facilities (subset of "data centers")
- Compare siting patterns: Bitcoin mines prefer low electricity costs (WA, TX, NY hydro)
- Test if Bitcoin mines face more opposition (negative perception)

**Data Sources**:
- Cambridge Bitcoin Electricity Consumption Index (has facility locations)
- News archives of mining farm proposals/rejections

---

### 22. Disaster Resilience & Redundancy
**Approach**:
- Model simultaneous hazard exposure across data center clusters
- E.g., "What % of US data centers are in wildfire risk zones?"
- Identify single points of failure (e.g., Ashburn VA = 20% of US capacity)

**Research Questions**:
- Is the current spatial distribution resilient to climate change?
- Should policy incentivize geographic diversification?

**Output**: `disaster_resilience_report.md`

---

### 23. Edge Data Center Network
**Approach**:
- Separately analyze edge facilities (<1 MW) vs. hyperscale (>100 MW)
- Test if edge DCs follow different siting logic (population density > energy cost)

**Data Challenge**: Current inventory does not distinguish edge vs. hyperscale (need `power_mw` backfill)

---

### 24. Carbon Intensity of Host Grids
**Approach**:
- Join to EPA eGRID subregion carbon intensity (lb CO₂/MWh)
- Calculate per-facility estimated carbon footprint (if `power_mw` available)
- Compare to corporate renewable energy procurement (RECs, PPAs)

**Research Questions**:
- Are data centers disproportionately in high-carbon grids?
- Do hyperscaler renewable commitments offset grid carbon?

**Data Sources**:
- EPA eGRID
- Corporate sustainability reports (Google, Microsoft, Meta, AWS)

---

## Collaboration Opportunities

### Academic Partnerships
- **Energy researchers**: Joint analysis of grid saturation + IM3 projections
- **Environmental justice scholars**: EJScreen overlay + opposition case studies
- **Political scientists**: Tax incentive analysis + local government bargaining power

### Policy Stakeholders
- **State energy offices**: Share grid saturation maps
- **Water resource agencies**: Watershed analysis for permitting
- **Local governments**: Demographic/tax revenue analysis for negotiation leverage

### Industry Engagement
- **Data center operators**: Validate facility data, discuss siting criteria
- **Colocation providers**: Access to tenant mix data (multi-tenant vs. single-tenant)

---

## Tools & Infrastructure Improvements

### Database Enhancements
- Add `version` column to track data vintage
- Implement `audit_log` table for data lineage
- Set up automated backups to S3/Azure Blob

### Code Quality
- Add unit tests for geocoding functions
- Create `config.yaml` for database credentials (replace hardcoded env vars)
- Dockerize analysis environment for reproducibility

### Documentation
- Add JSDoc-style comments to all Python functions
- Create `CONTRIBUTING.md` for external collaborators
- Record Jupyter notebook walkthroughs (video tutorials)

---

## Unfunded / Ambitious Ideas

### 25. Real-Time Energy Monitoring
- Partner with utility to get live load data from data center substations
- Build dashboard showing real-time energy consumption by facility
- Correlate with AWS/Azure/GCP service outages (reverse-engineer capacity from brownouts)

### 26. Social Media Sentiment Analysis
- Scrape Twitter/Reddit for mentions of local data center projects
- NLP sentiment analysis: support vs. opposition
- Correlate sentiment with facility approval outcomes

### 27. LIDAR Analysis of Cooling Infrastructure
- Use aerial LIDAR to measure rooftop cooling equipment volume
- Proxy for facility size (cooling = f(IT load))
- Build predictive model: cooling equipment → power capacity

---

## Contact & Contributions

If you're interested in collaborating on any of these research directions, please contact the repository owner.

**Priorities for external collaboration**:
1. Power capacity data acquisition
2. Climate, drought, smoke, and compound-exposure analysis
3. Opposition cases database compilation
4. Water stress/drought overlay
5. International comparative analysis

---

## References for Future Work

### Data Sources to Explore
- **Department of Energy**: Grid resilience reports, interconnection queues
- **NREL**: Renewable energy potential by HUC (solar, wind)
- **USDA**: Agricultural water use by county (competition for water)
- **NOAA**: Climate normals + projections by grid cell
- **BLS**: QCEW employment data, wage data
- **EPA**: eGRID, EJScreen, Superfund sites

### Academic Literature Gaps
- Limited peer-reviewed research on data center spatial concentration
- No published studies on water stress exposure of data centers
- Opportunity for "first mover" publication in major geography/planning journals

### Policy Levers to Investigate
- State renewable portfolio standards (RPS) → data center siting
- Federal infrastructure investment (IRA, CHIPS Act) → energy grid capacity
- Local zoning reform (industrial land use restrictions)

---

## Legislative Analysis (LegiScan Data)

**Status**: Data loaded — 1.3M bills across all US states + federal, 2016–2026; ~60K tagged relevant.  
**Tables**: `legiscan_sessions`, `legiscan_bills`  
**Query file**: `query_legiscan_bills.sql`

### Research Questions

**1. Ratepayer Cost Shifting**  
Do states with high data center density show more legislative activity on ratepayer protection?  
- Join `legiscan_bills WHERE 'ratepayer_protection' = ANY(relevance_tags)` to `master_data_centers` counts by state  
- Test correlation between DC concentration and # of ratepayer bills introduced/passed  
- Compare outcomes: do high-DC states pass or fail more ratepayer protections?

**2. Data Center Legislative Wave**  
Is there a measurable increase in DC-specific legislation after 2022 (AI boom)?  
- Trend `data_center` and `large_load` tagged bills by `year_start`  
- Cross-reference with major AI facility announcements (2022–2025)

**3. Tax Incentive Geography**  
Which states enacted tax incentives that may have influenced DC location decisions?  
- `tax_incentive` bills with `status IN (4,8)` (passed/chaptered)  
- Overlay with `master_data_centers` growth by state over the same period  
- Candidate for difference-in-differences analysis

**4. Grid Interconnection Policy**  
Do states with `grid_impact` legislation show different EIA capacity expansion patterns?  
- Join relevant bills to `energy_eia_operating_generator_capacity_flat` by state  
- Look for correlations between grid policy activity and nameplate MW additions

**5. Siting Preemption vs. Local Control**  
Are states passing bills to streamline or restrict local siting authority?  
- Full-text search within `siting_permitting` bills for "preemption" vs. "local control"  
- Map bill outcomes by state political environment (cross-ref RDH vote data)

### Suggested Joins

```sql
-- States with DCs and legislative activity by topic
SELECT
    dc.state,
    COUNT(DISTINCT dc.id)                                              AS data_centers,
    COUNT(DISTINCT lb.bill_id) FILTER (WHERE 'data_center'        = ANY(relevance_tags)) AS dc_bills,
    COUNT(DISTINCT lb.bill_id) FILTER (WHERE 'ratepayer_protection'= ANY(relevance_tags)) AS ratepayer_bills,
    COUNT(DISTINCT lb.bill_id) FILTER (WHERE 'tax_incentive'      = ANY(relevance_tags)
                                         AND lb.status IN (4,8))      AS tax_incentives_passed
FROM master_data_centers dc
LEFT JOIN legiscan_bills lb ON dc.state = lb.state AND lb.is_relevant
GROUP BY dc.state
ORDER BY data_centers DESC;
```

---

**Last Updated**: May 2026
