152 lines
6.2 KiB
Markdown
152 lines
6.2 KiB
Markdown
# Chapter 4 Variable Reference
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## The Geography of Transition: Distribution and Consequences of Orphaned Wells
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---
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## Research Questions
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1. How are orphaned wells spatially distributed and do they concentrate in historically fossil-dependent communities?
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2. Do states with strong fossil industry dependence have formal energy transition governance mechanisms?
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3. Do states frame orphaned well remediation as an engineering problem or a justice problem?
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4. How does spatial distribution relate to political tensions over responsibility and funding?
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---
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## Core Analytical Variables
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### Well Distribution
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| Variable | Source | DB Location | Notes |
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|---|---|---|---|
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| Well count by state | USGS DOW | `v_wells_by_state.well_count` | 27 states |
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| Well count by county | USGS DOW | `v_wells_by_county.well_count` | 5-digit GEOID |
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| Well count by tract | USGS DOW | `v_wells_by_tract.well_count` | 11-digit GEOID |
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| Well density (wells/km²) | Calculated | `v_wells_by_tract.wells_per_km2` | Land area only |
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| Well type (normalized) | USGS DOW | `wells.well_type_normalized` | 12 categories |
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| Well status | USGS DOW | `wells.status` | State-specific terminology |
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### State Governance Framework (RA-coded)
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| Variable | Source | DB Location | Values |
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|---|---|---|---|
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| Transition office count | Climate Policy Dashboard | `v_state_governance.transition_office_count` | 0, 1, 2+ |
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| Office fossil language | Climate Policy Dashboard | `state_transition_offices.code_fossil` | 0/1 |
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| Office equity language | Climate Policy Dashboard | `state_transition_offices.code_equity` | 0/1 |
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| Prioritization system type | IOGCC 2023 | `state_prioritization.system_type` | Text description |
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| Technical factors used | IOGCC 2023 | `state_prioritization.tech_factors` | Semicolon list |
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| Rural/urban in scoring | IOGCC 2023 | `state_prioritization.code_rural_urban` | 0/1 |
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| Vulnerability/EJ in scoring | IOGCC 2023 | `state_prioritization.code_vuln` | 0/1 |
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| Surface land use in scoring | IOGCC 2023 | `state_prioritization.code_surface` | 0/1 |
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### Derived Classification
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| Variable | DB Location | Logic |
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|---|---|---|
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| `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 |
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| `office_language_type` | `v_state_governance` | Fossil + Equity / Fossil only / Equity only / Office exists no language / No transition office |
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### Environmental Justice Indicators (requires ACS join)
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Join on `wells.tract_geoid` = ACS `geoid`:
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| Variable | ACS Table | Description |
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|---|---|---|
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| Median household income | B19013 | Tract-level; proxy for economic vulnerability |
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| % Non-white | B03002 | Calculated from race/ethnicity totals |
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| % Below poverty line | B17001 | Federal poverty threshold |
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| Median housing age | B25035 | Proxy for legacy industrial neighborhood |
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| % Unemployed | B23025 | Labor market conditions |
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---
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## Key Queries
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### State governance summary (activate after RA data loaded)
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```sql
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SELECT state, state_name, well_count_dow,
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framework_type, office_language_type,
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code_vuln, code_rural_urban, code_fossil, code_equity,
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est_liability_mid_usd
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FROM v_ch4_state_analysis
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ORDER BY well_count_dow DESC;
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```
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### Engineering vs. justice states, well count comparison
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```sql
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SELECT framework_type,
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count(DISTINCT state) AS state_count,
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sum(well_count_dow) AS total_wells,
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round(avg(well_count_dow)) AS avg_wells_per_state
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FROM v_ch4_state_analysis
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GROUP BY framework_type
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ORDER BY total_wells DESC;
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```
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### Highest-density tracts (for mapping)
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```sql
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SELECT tract_geoid, tract_name, county_name, state_usps,
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well_count, wells_per_km2, tract_land_km2
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FROM v_highest_density_tracts
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LIMIT 50;
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```
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### Wells in tracts below median income (EJ analysis — requires ACS)
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```sql
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SELECT w.state, count(*) AS wells_in_low_income_tracts
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FROM wells w
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JOIN acs_b19013 a ON w.tract_geoid = a.geoid
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WHERE a.median_hh_income < 50000
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GROUP BY w.state
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ORDER BY wells_in_low_income_tracts DESC;
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```
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### State transition office presence vs. well burden
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```sql
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SELECT sg.framework_type,
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sg.office_language_type,
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count(DISTINCT sg.state) AS states,
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sum(sg.well_count_dow) AS total_wells,
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avg(sg.well_count_dow) AS avg_wells
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FROM v_state_governance sg
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GROUP BY sg.framework_type, sg.office_language_type
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ORDER BY total_wells DESC;
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```
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---
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## Analytical Strategy (Chapter 4)
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### Section 1: Mapping the Distribution
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- National map: well locations by `well_type_normalized`
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- State-level choropleth: well count and well density
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- County-level choropleth: `v_wells_by_county` joined to TIGER county boundaries
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- Key finding to highlight: OH + PA + OK = 47% of all documented orphaned wells
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### Section 2: Fossil Dependence and Governance
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- Crosstab: states by framework_type × well count
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- Test: Do high-burden states have transition offices? (office_count > 0 vs. well_count)
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- Key contrast: PA (no transition office, engineering frame) vs. CO (Just Transition Office, equity language)
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### Section 3: The Justice Dimension
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- Map: well density by tract overlaid with % non-white or % below poverty
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- Identify tracts where both are elevated — the "double burden"
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- Use `v_highest_density_tracts` for case study selection
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### Section 4: Political Tensions
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- Connect framework_type to state political context (add `state_politics` table if needed)
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- Argument: justice framing is not randomly distributed — correlates with state political economy
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---
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## Data Limitations for Chapter 4
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1. **DOW dataset is documented wells only.** True orphaned well count is almost certainly higher. API estimates 2 million+ undocumented orphaned wells nationally.
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2. **Definitional inconsistency.** California "idle" wells differ legally from other states' "orphaned" definition. Flagged in `data_file_notes`.
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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.
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4. **Snapshot data.** Data collected 2019–2022; plugging programs have been active since, so current counts are lower.
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5. **Spatial precision.** No formal accuracy tests. Some coordinates converted from PLSS — precision is lower for KS and MT wells.
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