| Energy Category | Projects | Included Technologies |
|---|---|---|
| Decarbonization | 20,725 | Wind, solar, transmission, geothermal, hydropower, biomass, energy storage, CCS, nuclear, and other clean-energy labels |
| Fossil Fuel | 10,783 | Land-based oil and gas, offshore oil and gas, coal, pipelines, rural energy, and other fossil-energy labels |
NEPA Decarbonization Technology Analysis: Deliverable 3
NEPA Review Patterns: Fossil Fuel vs. Decarbonization Projects
Executive Summary
Key Findings
- Decarbonization projects are more often resolved through Categorical Exclusions than fossil fuel projects. Across 20,725 decarbonization projects, 93.6% are CEs, 2.8% are EAs, and 3.6% are EISs. Across 10,783 fossil fuel projects, 85.2% are CEs, 9.0% are EAs, and 5.8% are EISs.
- The association is statistically significant but moderate in effect size. The clean/fossil difference clears standard significance thresholds (chi-square = 690.4, p < 0.001; Cramer’s V = 0.148), but technology group, agency, and geographic context explain much of the remaining variation in review type.
- Technology group is a stronger predictor of review type than the clean/fossil category alone. The highest EIS-share technologies — Hydropower, Offshore Oil & Gas, Coal, Pipeline, and Wind — span both fossil and clean categories, illustrating that project type and federal nexus drive review intensity as much as the clean/fossil designation. The most CE-heavy technologies are CCS, Rural Energy, Energy Storage, Other Clean, and Biomass.
- Categorical Exclusion authority differs by energy category. Fossil fuel CEs are heavily concentrated in oil-and-gas-specific pathways — particularly EPAct 2005 Section 390, and 516 DM 11.9 (BLM Handbook H-1790-1 codes covering routine oil and gas operations, and where applicable the Energy Policy Act 2005 Section 390 statutory CE for federal oil and gas leases). Decarbonization CEs are more diffuse, with B3.6, B1.3, 516 DM 11.9, and B5.1 prominent.
- Agency controls narrow the headline clean/fossil difference. When BLM or DOE is held constant, the clean/fossil review-type gap changes materially, pointing to agency practice and project mix as important explanations alongside energy category.
- Geothermal is CE-heavy and tracks the clean-energy average more closely than oil and gas. Geothermal projects have a 93.8% CE share, compared with 93.6% for the clean-energy average and 86.1% for oil and gas. Because 12.5% of geothermal projects are BLM-led, claims about BLM geothermal pathways specifically should be tested with a separate BLM-controlled analysis.
- Visual impact analysis is promising but should be treated as exploratory. The current visual-impact extractor runs on EA/EIS text only, not CE forms, and coverage is uneven by technology. The results are useful for triage and example selection, but should not yet be treated as a portfolio-wide visual-impact rate.
This report delivers:
A comparison of how fossil fuel and decarbonization projects move through NEPA, including CE/EA/EIS rates by technology, categorical exclusion citation patterns, geographic distributions, visual-impact discussion patterns, and an all-agency geothermal vs. oil-and-gas comparison.
Methodology
Analysis Universe
The Deliverable 3 build script scopes the analysis to 31,508 energy projects:
Projects classified as Other are excluded before analysis. This keeps the denominator focused on energy projects and avoids diluting clean/fossil comparisons with unrelated federal actions.
Data Pipeline
The analysis is built in two scripts:
| Script | Role |
|---|---|
| phase2/code/deliverable03/01_build_nepa_reviews.py | Builds project-level review data, normalized CE citations, visual-impact extraction outputs, and the all-agency geothermal/oil-and-gas subset |
| phase2/code/deliverable03/02_analyze_nepa_reviews.R | Produces figures and CSV tables for review rates, CE citations, geography, visual impacts, and geothermal comparisons |
The main project-level output is phase2/data/analysis/deliverable03/projects_nepa_reviews.parquet. It contains one row per project with energy category, technology group, review process, lead agency, geography, and clean-energy trigger information where available.
Current Data Availability
| Component | Status | Notes |
|---|---|---|
| Review process type | Available | CE, EA, and EIS populated for clean and fossil projects |
| Technology group | Available | Derived from NEPATEC project-type labels |
| CE citations | Available | Parsed and normalized from document-level CE categories |
| Geography | Available | State and county fields are exploded from project metadata; multi-state projects can count in multiple states |
| Visual impacts | Available, exploratory | Extracted from EA/EIS page text only using lexical filtering plus sentence-transformer similarity |
| Geothermal vs. oil and gas | Available | Dedicated comparison output includes review-rate bars, all-state stacked shares, and a state geothermal-share map |
| NEPA trigger | Partial | Deliverable 1 trigger classifications cover clean-energy projects; fossil projects currently have NULL trigger values |
| Linear vs. non-linear geometry | Not yet available | is_linear is still empty, so geometry analysis is omitted |
| Timelines | Not yet available | Duration analysis is omitted unless phase2/data/analysis/timeline.parquet exists |
Review Type Patterns
Decarbonization vs. Fossil Fuel
Decarbonization projects are more CE-heavy than fossil fuel projects. Fossil fuel projects are 2.3 times as likely to require either an EA or EIS (14.8%) as decarbonization projects (6.4%).
The distinction is especially pronounced for EAs. Fossil fuel projects have a 9.0% EA share, compared with 2.8% for decarbonization projects. EIS rates are closer but still higher for fossil fuel projects: 5.8% vs. 3.6%.
Review Type by Technology
The technology view shows why the clean/fossil headline should not be overinterpreted. Some clean technologies have materially higher EIS shares than the clean-energy average. Some fossil categories remain highly CE-heavy because many oil and gas actions are processed through repeatable BLM or statutory CE pathways.
Highest EIS shares by technology:
| Technology | Projects | CE Share | EA Share | EIS Share |
|---|---|---|---|---|
| Hydropower | 376 | 74.7% | 3.5% | 21.8% |
| Offshore Oil & Gas | 211 | 77.7% | 1.4% | 20.9% |
| Coal | 656 | 80.5% | 4.0% | 15.5% |
| Pipeline | 1,105 | 79.2% | 8.2% | 12.6% |
| Wind | 867 | 79.9% | 8.5% | 11.5% |
| Nuclear | 1,308 | 89.1% | 2.9% | 8.0% |
Most CE-heavy technologies:
| Technology | Projects | CE Share |
|---|---|---|
| CCS | 1,181 | 99.3% |
| Rural Energy | 147 | 98.6% |
| Energy Storage | 1,045 | 97.6% |
| Other Clean | 4,942 | 96.4% |
| Biomass | 1,058 | 96.4% |
| Geothermal | 873 | 93.8% |
| Transmission | 6,830 | 93.7% |
| Solar | 2,245 | 91.8% |
The figure below plots CE, EA, and EIS shares for all technology groups, sorted by categorical exclusion share from highest to lowest. The spread — from near-universal CE use (CCS, nuclear) to double-digit EIS rates (hydropower, wind) — illustrates that a project’s technology type predicts its review pathway as reliably as its clean/fossil designation.
Linear vs. Non-linear Geometry
A planned breakdown by project geometry (linear infrastructure such as transmission lines and pipelines vs. non-linear point or area projects) is omitted from this version. The is_linear field is not yet populated. See the Known Gaps table.
Within-Agency Comparisons
Agency controls help separate technology effects from agency practice. Within-agency review profiles are generated from the latest review_rates_within_blm.csv and review_rates_within_doe.csv outputs.
| Agency Subset | Energy Category | CE | EA | EIS |
|---|---|---|---|---|
| BLM | Clean | 89.8% | 5.6% | 4.6% |
| BLM | Fossil | 86.2% | 11.0% | 2.8% |
| DOE | Clean | 98.0% | 1.2% | 0.9% |
| DOE | Fossil | 90.2% | 2.9% | 6.9% |
The BLM comparison is the more informative of the two: within a single land-management agency, the clean/fossil CE gap narrows relative to the headline rate, indicating that federal land exposure and repeatable permitting pathways — not energy category alone — drive a meaningful share of the headline difference. The DOE comparison points in a different direction: clean-energy DOE reviews are overwhelmingly CEs, likely because the DOE clean portfolio contains many financial-assistance and low-disturbance actions, while DOE fossil projects include a smaller and more heterogeneous set.
Categorical Exclusion Citations
Most-Cited CE Authorities
The CE citation distribution is concentrated in a relatively small set of authorities. The top normalized citations in the current output are:
| CE Citation | Documents |
|---|---|
| 516 DM 11.9 | 12,711 |
| B3.6 | 8,466 |
| B1.3 | 6,182 |
| EPAct 2005 Section 390 | 3,682 |
| B5.1 | 2,403 |
These codes should be interpreted as document-level citation counts, not unique project counts. A project can have multiple CE citations, and some agency manuals encode several closely related categorical exclusions separately.
Clean vs. Fossil CE Profiles
The clean and fossil CE portfolios rely on different legal and administrative pathways.
Top decarbonization CE citations:
| CE Citation | Documents |
|---|---|
| B3.6 | 4,137 |
| B1.3 | 2,751 |
| 516 DM 11.9 | 2,202 |
| B5.1 | 1,922 |
| B3.1 | 714 |
| A9 | 503 |
| A9, A11 | 498 |
| B2.5 | 477 |
Top fossil fuel CE citations:
| CE Citation | Documents |
|---|---|
| EPAct 2005 Section 390 | 3,661 |
| 516 DM 11.9 | 2,627 |
| B3.6 | 705 |
| 516 DM 6 | 439 |
| B3.1 | 295 |
| 516 DM 11 | 253 |
| B1.3 | 228 |
| B5.1 | 185 |
The fossil distribution is dominated by oil-and-gas-specific pathways, especially Section 390. The decarbonization distribution is more mixed, reflecting a broader set of technologies and agencies.
CE Citations by Agency
Agency-specific CE citation patterns are central to the story. The same high-level review type, “Categorical Exclusion”, can reflect very different institutional routines depending on whether the action is BLM oil and gas, BLM right-of-way work, DOE funding, or another agency’s infrastructure program.
CE Citations by Trigger
The CE-by-trigger table is currently limited to clean-energy projects because Deliverable 1 trigger classification has not yet been extended to fossil projects. The table below is generated from the latest ce_by_trigger.csv output.
| Clean-Energy Trigger | Leading CE Citation Pattern |
|---|---|
| Direct Action | B1.3, B3.6, B4.6, B1.15, B4.11 |
| Funding | B3.6, B5.1, B3.1, A9, A9, A11 |
| Land | 516 DM 11.9, 516 DM 2, 516 DM 11.9., 43 CFR 46.210, 516 DM 11 |
| Permit | B4.2, B1.24, B3.1, B5.25, B5.1 |
| Program | B3.6, B1.4, B1.7, B1.3, B3.1 |
| Property Transaction | B1.24, B1.2, B1.3, B4.13, B4.9 |
| Unknown | B4.2, B3.6, B1.3, B5.1, B1.15 |
This is a high-value area for expansion once fossil triggers are classified. It would allow the client to distinguish “fossil projects use more CEs” from “specific fossil authorities create repeatable CE pathways.”
Geographic Distribution
State-Level Patterns
The maps and tables count project-state records, so projects spanning multiple states can contribute to multiple state totals. This is appropriate for geographic footprint analysis, but it should not be interpreted as a unique-project denominator.
Top decarbonization states by project-state count:
| State | Project-State Records |
|---|---|
| South Carolina | 2,024 |
| Washington | 1,872 |
| California | 1,734 |
| Oregon | 1,303 |
| Colorado | 1,220 |
| Idaho | 962 |
| Arizona | 944 |
| Nevada | 909 |
| Wyoming | 688 |
| Texas | 602 |
Top fossil fuel states by project-state count:
| State | Project-State Records |
|---|---|
| Wyoming | 2,577 |
| New Mexico | 2,391 |
| California | 906 |
| Colorado | 764 |
| Texas | 428 |
| Utah | 406 |
| North Dakota | 373 |
| Alaska | 281 |
| Louisiana | 247 |
| Montana | 197 |
The fossil map is more visibly concentrated in Interior West oil-and-gas states. The decarbonization map is broader, with strong concentrations in the West, Pacific Northwest, California, and selected Southeastern and Atlantic states.
County-Level Patterns
County maps provide a more granular view of project geography. They show fossil fuel activity clustering in oil-and-gas regions and decarbonization activity spreading across renewable, transmission, and federal-power geographies.
Process Type by State
The state-by-process map helps identify where the same energy category receives different review intensity. This can support targeted client questions, for example whether high-EIS states reflect project size, federal land exposure, agency practice, technology mix, or state-specific project portfolios.
Visual Impact Analysis
The visual-impact module is designed to identify substantive discussion of visual resources, viewsheds, aesthetics, scenic quality, glare, landscape character, and related topics. It uses a lexical prefilter followed by sentence-transformer similarity scoring against a visual-impact query.
The current results should be treated as exploratory for three reasons:
- The extractor runs on EA/EIS page text only; CE forms are not included.
- Current coverage is small and uneven across technologies.
- The 0.4 similarity threshold is plausible but not yet calibrated against a hand-reviewed validation set.
Even with those caveats, the results are useful for finding examples and identifying technologies where visual resources are likely to matter.
Recommended Interpretation
Use this module first as an evidence-finding tool rather than as a final statistical outcome. The strongest near-term deliverable would be a curated set of example passages for technologies where visual impacts are expected to be salient: solar, wind, transmission, geothermal, offshore oil and gas, and coal.
Geothermal vs. Oil and Gas
This comparison covers all lead agencies — BLM, USFS, DOE, and others. The Python builder classifies geothermal only within clean-energy projects and subsets the project table to clean geothermal, land-based oil and gas, and offshore oil and gas. The R analysis collapses land-based and offshore oil and gas into a single Oil & Gas comparison group.
Across all agencies, geothermal tracks closely with the clean-energy average. Geothermal has a 93.8% CE share, compared with 93.6% for the full clean-energy portfolio. Oil and gas is more review-intensive, with a 86.1% CE share and a higher EA share.
| Comparison Group | Projects | CE Share | EA Share | EIS Share |
|---|---|---|---|---|
| Geothermal | 873 | 93.8% | 2.7% | 3.4% |
| Oil & Gas | 8,875 | 86.1% | 9.6% | 4.3% |
This figure answers a broad technology-comparison question: how geothermal projects compare with oil-and-gas projects across all lead agencies. It does not isolate public-land or BLM permitting pathways.
| Comparison Group | Projects | CE Share | BLM Share | Federal-Land Trigger Share |
|---|---|---|---|---|
| Clean Energy Average | 20,725 | 93.6% | 17.2% | 17.7% |
| Fossil Fuel Average | 10,783 | 85.2% | 72.3% | Not yet classified |
| Geothermal (All Agencies) | 873 | 93.8% | 12.5% | 13.2% |
This is potentially one of the most client-relevant findings in the deliverable, but it should be framed carefully. If the policy question is whether geothermal generally moves through NEPA more like clean energy or oil and gas, the all-agency comparison suggests geothermal is closer to the clean-energy average. If the policy question is whether BLM geothermal development faces a pathway more similar to oil and gas, that should be tested with a separate BLM-controlled geothermal sensitivity analysis.
The state share figure shows all states with geothermal or oil-and-gas projects, ordered by geothermal share. Vermont is the most geothermal-dominant state in this comparison, with 7 geothermal projects and 0 oil-and-gas projects. Among states with at least 100 geothermal plus oil-and-gas projects, Nevada has the highest geothermal share, with 121 geothermal projects and 52 oil-and-gas projects.
The state map encodes the same geothermal share metric as a diverging color scale: blue states are geothermal-dominant, red states are oil-and-gas-dominant, and purple indicates a roughly equal split between the two. States with no projects in either category are shown in grey.
Timelines
Timeline data — initiation date, decision date, and computed review duration — are not yet available in the analysis database. When phase2/data/analysis/timeline.parquet is built and audited for coverage, this section will include:
- Coverage table: how many projects have both initiation and decision dates, broken out by energy category and process type
- Duration by regulatory period and process type: median review length before and after the 2020 CEQ rule revision and the 2023 Fiscal Responsibility Act
- Clean vs. fossil duration comparison: controlled within process type to isolate the energy-category effect
See the Known Gaps table for current status.
Known Gaps and Cautions
| Gap | Why It Matters | Recommended Treatment |
|---|---|---|
| Fossil trigger classifications are missing | Trigger comparisons are currently clean-energy only | Extend Deliverable 1 trigger logic to fossil projects before making trigger-based clean/fossil claims |
| Timeline data are missing | Cannot yet compare review duration or pre/post reform periods | Omit duration claims until timeline.parquet exists and coverage is audited |
| Linear geometry is missing | Cannot yet compare transmission/pipelines against point or area projects | Add geometry derivation before interpreting infrastructure corridor effects |
| All-agency geothermal blends different pathways | The current geothermal comparison is not a BLM/public-land control | Add a BLM-only geothermal sensitivity analysis if the client wants a public-land development comparison |
| Visual-impact coverage is uneven | Current visual rates are not portfolio-wide estimates | Treat as an exploratory retrieval and QA module |
| CE citation normalization is useful but incomplete | Similar DOI/BLM citations may still appear as separate variants | Build a CE-code crosswalk that maps raw citations to canonical authorities |
| State and county counts can duplicate multi-state projects | Geography maps show footprint, not unique project counts | Label outputs as project-state or project-county records when relevant |
Reproduction
Run from the repository root in the nepa conda environment:
conda run -n nepa python phase2/code/deliverable03/01_build_nepa_reviews.py
Rscript phase2/code/deliverable03/02_analyze_nepa_reviews.ROutputs are written to:
phase2/data/analysis/deliverable03/phase2/output/deliverable03/
Draft generated 2026-05-14 | NEPA Decarbonization Technology Analysis - Phase 2, Deliverable 3