# Chapter 4 Variable Reference
## The Geography of Transition: Distribution and Consequences of Orphaned Wells

---

## Research Questions

1. How are orphaned wells spatially distributed and do they concentrate in historically fossil-dependent communities?
2. Do states with strong fossil industry dependence have formal energy transition governance mechanisms?
3. Do states frame orphaned well remediation as an engineering problem or a justice problem?
4. How does spatial distribution relate to political tensions over responsibility and funding?

---

## Core Analytical Variables

### Well Distribution

| Variable | Source | DB Location | Notes |
|---|---|---|---|
| Well count by state | USGS DOW | `v_wells_by_state.well_count` | 27 states |
| Well count by county | USGS DOW | `v_wells_by_county.well_count` | 5-digit GEOID |
| Well count by tract | USGS DOW | `v_wells_by_tract.well_count` | 11-digit GEOID |
| Well density (wells/km²) | Calculated | `v_wells_by_tract.wells_per_km2` | Land area only |
| Well type (normalized) | USGS DOW | `wells.well_type_normalized` | 12 categories |
| Well status | USGS DOW | `wells.status` | State-specific terminology |

### State Governance Framework (RA-coded)

| Variable | Source | DB Location | Values |
|---|---|---|---|
| Transition office count | Climate Policy Dashboard | `v_state_governance.transition_office_count` | 0, 1, 2+ |
| Office fossil language | Climate Policy Dashboard | `state_transition_offices.code_fossil` | 0/1 |
| Office equity language | Climate Policy Dashboard | `state_transition_offices.code_equity` | 0/1 |
| Prioritization system type | IOGCC 2023 | `state_prioritization.system_type` | Text description |
| Technical factors used | IOGCC 2023 | `state_prioritization.tech_factors` | Semicolon list |
| Rural/urban in scoring | IOGCC 2023 | `state_prioritization.code_rural_urban` | 0/1 |
| Vulnerability/EJ in scoring | IOGCC 2023 | `state_prioritization.code_vuln` | 0/1 |
| Surface land use in scoring | IOGCC 2023 | `state_prioritization.code_surface` | 0/1 |

### Derived Classification

| Variable | DB Location | Logic |
|---|---|---|
| `framework_type` | `v_state_governance` | Justice if `code_vuln=1`; Mixed if `code_rural_urban=1`; Engineering if system documented but no EJ/density; Unclassified otherwise |
| `office_language_type` | `v_state_governance` | Fossil + Equity / Fossil only / Equity only / Office exists no language / No transition office |

### Environmental Justice Indicators (requires ACS join)

Join on `wells.tract_geoid` = ACS `geoid`:

| Variable | ACS Table | Description |
|---|---|---|
| Median household income | B19013 | Tract-level; proxy for economic vulnerability |
| % Non-white | B03002 | Calculated from race/ethnicity totals |
| % Below poverty line | B17001 | Federal poverty threshold |
| Median housing age | B25035 | Proxy for legacy industrial neighborhood |
| % Unemployed | B23025 | Labor market conditions |

---

## Key Queries

### State governance summary (activate after RA data loaded)
```sql
SELECT state, state_name, well_count_dow,
       framework_type, office_language_type,
       code_vuln, code_rural_urban, code_fossil, code_equity,
       est_liability_mid_usd
FROM v_ch4_state_analysis
ORDER BY well_count_dow DESC;
```

### Engineering vs. justice states, well count comparison
```sql
SELECT framework_type,
       count(DISTINCT state)              AS state_count,
       sum(well_count_dow)                AS total_wells,
       round(avg(well_count_dow))         AS avg_wells_per_state
FROM v_ch4_state_analysis
GROUP BY framework_type
ORDER BY total_wells DESC;
```

### Highest-density tracts (for mapping)
```sql
SELECT tract_geoid, tract_name, county_name, state_usps,
       well_count, wells_per_km2, tract_land_km2
FROM v_highest_density_tracts
LIMIT 50;
```

### Wells in tracts below median income (EJ analysis — requires ACS)
```sql
SELECT w.state, count(*) AS wells_in_low_income_tracts
FROM wells w
JOIN acs_b19013 a ON w.tract_geoid = a.geoid
WHERE a.median_hh_income < 50000
GROUP BY w.state
ORDER BY wells_in_low_income_tracts DESC;
```

### State transition office presence vs. well burden
```sql
SELECT sg.framework_type,
       sg.office_language_type,
       count(DISTINCT sg.state) AS states,
       sum(sg.well_count_dow) AS total_wells,
       avg(sg.well_count_dow) AS avg_wells
FROM v_state_governance sg
GROUP BY sg.framework_type, sg.office_language_type
ORDER BY total_wells DESC;
```

---

## Analytical Strategy (Chapter 4)

### Section 1: Mapping the Distribution
- National map: well locations by `well_type_normalized`
- State-level choropleth: well count and well density
- County-level choropleth: `v_wells_by_county` joined to TIGER county boundaries
- Key finding to highlight: OH + PA + OK = 47% of all documented orphaned wells

### Section 2: Fossil Dependence and Governance
- Crosstab: states by framework_type × well count
- Test: Do high-burden states have transition offices? (office_count > 0 vs. well_count)
- Key contrast: PA (no transition office, engineering frame) vs. CO (Just Transition Office, equity language)

### Section 3: The Justice Dimension
- Map: well density by tract overlaid with % non-white or % below poverty
- Identify tracts where both are elevated — the "double burden"
- Use `v_highest_density_tracts` for case study selection

### Section 4: Political Tensions
- Connect framework_type to state political context (add `state_politics` table if needed)
- Argument: justice framing is not randomly distributed — correlates with state political economy

---

## Data Limitations for Chapter 4

1. **DOW dataset is documented wells only.** True orphaned well count is almost certainly higher. API estimates 2 million+ undocumented orphaned wells nationally.

2. **Definitional inconsistency.** California "idle" wells differ legally from other states' "orphaned" definition. Flagged in `data_file_notes`.

3. **Type field missingness (59.3% Unknown).** Major states (OH, PA, KY) did not classify type. Limit type-based analysis to states with complete type data or use normalized categories cautiously.

4. **Snapshot data.** Data collected 2019–2022; plugging programs have been active since, so current counts are lower.

5. **Spatial precision.** No formal accuracy tests. Some coordinates converted from PLSS — precision is lower for KS and MT wells.