Biomass Electric Power Generation
National industry · NAICS 221117
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Biomass Electric Power Generation is a U.S. industry in the NAICS classification. The Bureau of Labor Statistics estimates about 1,840 workers across 14 detailed occupations in it. A typical worker earns around $83,945 a year (Singulariki estimate, see below).
This U.S. industry comprises establishments primarily engaged in operating biomass electric power generation facilities. These facilities use biomass (e.g., wood, waste, alcohol fuels) to produce electric energy. The electric energy produced in these establishments is provided to electric power transmission systems or to electric power distribution systems. Cross-References.
Employment is national May 2024 OEWS. "Typical pay" is Singulariki's own figure — the employment-weighted average of each occupation's national median wage — a rough center of the industry, not an official BLS number.
How exposed this industry is to AI
Weighting every occupation in this industry by its employment and its unified AI-exposure index (the OpenAI "GPTs are GPTs" human-rated task overlap folded with the Felten/Raj/Seamans AIOE index), this industry sits in the Low band — 20th percentile across all industries.
Exposure measures how much of the work overlaps with what today's AI can do, not a prediction of automation; high-exposure industries are where AI is most likely to reshape tasks. Employment-weighted across 14 occupations that carry an exposure score. Compare every industry on the AI exposure hub.
How AI is actually used in this industry
Among measured Claude.ai (Free and Pro) conversations mapped to O*NET task statements (Anthropic Economic Index, 2026-01-15), these patterns are most associated with the occupations in this industry, weighted by its employment mix. They are shares of observed AI conversations — not of worker time, revenue, or what could be automated — and reflect one AI assistant's consumer sample, not all AI.
| Signal coverage | 85.3% of employment · 10/14 occupations have AEI task data |
| Augmentation vs. automation | 13.8% working with AI · 50.5% handed to AI |
| Most common pattern | Directive · AI does it; you give the instruction |
| Typical AI autonomy | 3.3 / 5 · higher = AI acts more independently |
Tasks driving the signal
The task families that account for the most AI activity across this industry's occupations (employment × observed usage), each attributed to the occupation it comes from.
| Task | Occupation | How | Share of signal |
|---|---|---|---|
| Use computers for various applications, such as database management or word processing. | Secretaries and Administrative Assistants, Except Legal, Medical, and Executive | Directive | 10.4% |
| Conduct searches to find needed information, using such sources as the Internet. | Secretaries and Administrative Assistants, Except Legal, Medical, and Executive | Directive | 9.6% |
| Present investment information, such as product risks, fees, or fund performance statistics. | Managers, All Other | Learning | 7.9% |
| Develop or maintain internal or external company Web sites. | Secretaries and Administrative Assistants, Except Legal, Medical, and Executive | Directive | 7.2% |
| Enter codes and instructions to program computer-controlled machinery. | Industrial Machinery Mechanics | Directive | 7.0% |
| Identify, investigate, or resolve security breaches. | Managers, All Other | Feedback loop | 6.3% |
| Record and compile operational data by completing and maintaining forms, logs, or reports. | Power Plant Operators | Directive | 5.4% |
| Review financial statements, sales or activity reports, or other performance data to measure productivity or goal achievement or to identify areas needing cost reduction or program improvement. | General and Operations Managers | Directive | 3.7% |
| Develop or implement product-marketing strategies, including advertising campaigns or sales promotions. | General and Operations Managers | Iteration | 2.7% |
| Classify, record, and summarize numerical and financial data to compile and keep financial records, using journals and ledgers or computers. | Bookkeeping, Accounting, and Auditing Clerks | Directive | 2.3% |
| Analyze business operations, trends, costs, revenues, financial commitments, and obligations to project future revenues and expenses or to provide advice. | Accountants and Auditors | Iteration | 2.2% |
| Perform financial calculations, such as amounts due, interest charges, balances, discounts, equity, and principal. | Bookkeeping, Accounting, and Auditing Clerks | Directive | 1.9% |
Occupations behind the signal
The occupations whose AI-touched tasks contribute most to this industry's signal, by employment here.
| Occupation | Workers | Share | How they use AI |
|---|---|---|---|
| Power Plant Operators | 950 | 51.6% | Directive |
| General and Operations Managers | 150 | 8.2% | Iteration |
| Industrial Machinery Mechanics | 130 | 7.1% | Directive |
| Industrial Production Managers | 90 | 4.9% | Directive |
| First-Line Supervisors of Mechanics, Installers, and Repairers | 60 | 3.3% | Directive |
| Managers, All Other | 40 | 2.2% | Directive |
| Accountants and Auditors | 40 | 2.2% | Directive |
| Electrical Engineers | 40 | 2.2% | Iteration |
| Bookkeeping, Accounting, and Auditing Clerks | 40 | 2.2% | Directive |
| Secretaries and Administrative Assistants, Except Legal, Medical, and Executive | 30 | 1.6% | Directive |
This rollup is only as complete as the occupation-task matches available for the industry; the coverage figure above is shown so sparse industries do not look falsely precise. AI exposure is not the same as replacement.
Skill & tool metabolism
What this industry's work actually runs on. Each figure is the share of the industry's workers in occupations that significantly rely on a skill, knowledge area, or ability (O*NET importance ≥ 3 of 5), or that use a tool category — its employment reach. This is a measure of how widespread a requirement is across the workforce, not how intensively any one worker uses it. Shares are independent and need not add to 100%.
Based on 100.0% of this industry's employment that maps to a detailed occupation with an O*NET skill profile.
Skills
| Skill | Employment reach | Workers |
|---|---|---|
| Active Listening | 96.7% | 1,780 |
| Monitoring | 96.7% | 1,780 |
| Critical Thinking | 94.6% | 1,740 |
| Speaking | 94.6% | 1,740 |
| Coordination | 92.9% | 1,710 |
| Judgment and Decision Making | 92.4% | 1,700 |
| Complex Problem Solving | 90.8% | 1,670 |
| Reading Comprehension | 87.5% | 1,610 |
| Writing | 84.8% | 1,560 |
| Operations Monitoring | 81.5% | 1,500 |
| Quality Control Analysis | 78.3% | 1,440 |
| Troubleshooting | 70.1% | 1,290 |
Knowledge areas
| Knowledge area | Employment reach | Workers |
|---|---|---|
| English Language | 96.7% | 1,780 |
| Mathematics | 96.2% | 1,770 |
| Computers and Electronics | 83.2% | 1,530 |
| Administration and Management | 80.4% | 1,480 |
| Mechanical | 80.4% | 1,480 |
| Production and Processing | 80.4% | 1,480 |
| Engineering and Technology | 75.0% | 1,380 |
| Design | 68.5% | 1,260 |
| Education and Training | 65.8% | 1,210 |
| Public Safety and Security | 65.8% | 1,210 |
| Chemistry | 56.5% | 1,040 |
| Physics | 56.5% | 1,040 |
Abilities
| Abilitie | Employment reach | Workers |
|---|---|---|
| Deductive Reasoning | 100.0% | 1,840 |
| Information Ordering | 100.0% | 1,840 |
| Near Vision | 100.0% | 1,840 |
| Oral Comprehension | 100.0% | 1,840 |
| Oral Expression | 100.0% | 1,840 |
| Problem Sensitivity | 100.0% | 1,840 |
| Inductive Reasoning | 96.7% | 1,780 |
| Written Comprehension | 94.6% | 1,740 |
| Speech Recognition | 94.0% | 1,730 |
| Selective Attention | 87.5% | 1,610 |
| Written Expression | 87.5% | 1,610 |
| Speech Clarity | 87.0% | 1,600 |
Tool categories
| Tool category | Employment reach | Workers |
|---|---|---|
| Electronic mail software | 100.0% | 1,840 |
| Office suite software | 100.0% | 1,840 |
| Spreadsheet software | 100.0% | 1,840 |
| Word processing software | 97.8% | 1,800 |
| Enterprise resource planning ERP software | 95.1% | 1,750 |
| Data base user interface and query software | 91.8% | 1,690 |
| Presentation software | 91.8% | 1,690 |
| Internet browser software | 90.2% | 1,660 |
| Industrial control software | 88.6% | 1,630 |
| Analytical or scientific software | 82.1% | 1,510 |
| Facilities management software | 79.3% | 1,460 |
| Inventory management software | 74.5% | 1,370 |
| Development environment software | 71.2% | 1,310 |
| Computer aided design CAD software | 33.2% | 610 |
| Project management software | 33.2% | 610 |
Reach = share of industry employment in occupations where the requirement is significant; it is not a per-worker usage or proficiency measure. Skill, knowledge, and ability importance is from O*NET; tool use is reported presence of a technology category.
Largest occupations
The occupations that employ the most people in this industry, with their share of the industry's workforce and national median pay for the occupation (not industry-specific pay).
| Occupation | Workers | Share | National median pay |
|---|---|---|---|
| Power Plant Operators | 950 | 51.6% | $63,470 |
| General and Operations Managers | 150 | 8.2% | $170,180 |
| Industrial Machinery Mechanics | 130 | 7.1% | $73,630 |
| First-Line Supervisors of Production and Operating Workers | 120 | 6.5% | $85,060 |
| Industrial Production Managers | 90 | 4.9% | $151,640 |
| First-Line Supervisors of Mechanics, Installers, and Repairers | 60 | 3.3% | $111,270 |
| Industrial Truck and Tractor Operators | 60 | 3.3% | $51,280 |
| Electrical and Electronics Repairers, Powerhouse, Substation, and Relay | 50 | 2.7% | $77,380 |
| Managers, All Other | 40 | 2.2% | $137,540 |
| Accountants and Auditors | 40 | 2.2% | $118,160 |
| Electrical Engineers | 40 | 2.2% | $131,710 |
| Bookkeeping, Accounting, and Auditing Clerks | 40 | 2.2% | $55,530 |
| Operating Engineers and Other Construction Equipment Operators | 40 | 2.2% | $52,070 |
| Secretaries and Administrative Assistants, Except Legal, Medical, and Executive | 30 | 1.6% | $59,530 |
Most distinctive occupations
The occupations most unusually concentrated in this industry compared with the economy as a whole. The location quotient is how many times more common an occupation is here versus its economy-wide share (a value of 5 means five times as concentrated).
| Occupation | Concentration | Workers |
|---|---|---|
| Power Plant Operators | 2591.38× | 950 |
| Industrial Machinery Mechanics | 25.82× | 130 |
| First-Line Supervisors of Production and Operating Workers | 14.68× | 120 |
| General and Operations Managers | 3.51× | 150 |
Write a report on thisheadline · factoids · citation
The Biomass Electric Power Generation workforce sits at the 20th percentile of AI task overlap — 1,840 U.S. workers
- Weighting every occupation by its real share of Biomass Electric Power Generation employment, the industry's workforce ranks in the 20th percentile (Low band) for AI task overlap — overlap with what AI can attempt, not a measure of jobs at risk.Eloundou et al. + Felten AIOE, weighted by BLS OEWS
- The industry employs about 1,840 U.S. workers across 14 occupations.BLS OEWS (May 2024)
- Employment-weighted typical annual pay is about $83,945.BLS OEWS (May 2024)
- Of AI use observed across this industry's occupations, 14% looks like augmentation rather than automation — from a Claude.ai sample, not a census.Anthropic Economic Index
The Biomass Electric Power Generation workforce sits at the 20th percentile of AI task overlap — 1,840 U.S. workers • Weighting every occupation by its real share of Biomass Electric Power Generation employment, the industry's workforce ranks in the 20th percentile (Low band) for AI task overlap — overlap with what AI can attempt, not a measure of jobs at risk. (Eloundou et al. + Felten AIOE, weighted by BLS OEWS) • The industry employs about 1,840 U.S. workers across 14 occupations. (BLS OEWS (May 2024)) • Employment-weighted typical annual pay is about $83,945. (BLS OEWS (May 2024)) • Of AI use observed across this industry's occupations, 14% looks like augmentation rather than automation — from a Claude.ai sample, not a census. (Anthropic Economic Index) Source: Singulariki — "Biomass Electric Power Generation". https://singulariki.com/industries/221117 Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.
AssetsShare imageMethodology & sourcesPress & newsroomThe newsroom
Every line is built only from figures this page already shows and cites. AI task overlap means what today's AI can attempt — not automation, job loss, or a forecast.
Sources for this page
Every figure above traces to a named public dataset and the exact release below — not hand-written opinion. See the full methodology for what each measure does and does not mean.
- O*NET 30.3 U.S. Department of Labor / National Center for O*NET Development
- BLS Occupational Employment and Wage Statistics (OEWS) May 2024 U.S. Bureau of Labor Statistics
- Census NAICS 2022 U.S. Census Bureau
- Anthropic Economic Index v4 (2026-01-15) + v2 (2025-03-27) Anthropic
- “GPTs are GPTs” (Eloundou et al.) arXiv 2303.10130 OpenAI / academic
- AI Occupational Exposure (AIOE) Felten, Raj & Seamans academic
Data compiled June 3, 2026. Figures are estimates, not advice.
Cite this page
Singulariki. "Biomass Electric Power Generation." Singulariki: a source-backed encyclopedia of work. Built from O*NET 30.3; BLS Occupational Employment and Wage Statistics (OEWS) May 2024; Census NAICS 2022; Anthropic Economic Index v4 (2026-01-15) + v2 (2025-03-27); “GPTs are GPTs” (Eloundou et al.) arXiv 2303.10130; AI Occupational Exposure (AIOE) Felten, Raj & Seamans. Accessed June 7, 2026. https://singulariki.com/industries/221117
Singulariki. (2026). Biomass Electric Power Generation. Singulariki: a source-backed encyclopedia of work. Retrieved June 7, 2026, from https://singulariki.com/industries/221117
@misc{singulariki-221117,
title = {Biomass Electric Power Generation},
author = {{Singulariki}},
year = {2026},
note = {O*NET 30.3; BLS Occupational Employment and Wage Statistics (OEWS) May 2024; Census NAICS 2022; Anthropic Economic Index v4 (2026-01-15) + v2 (2025-03-27); “GPTs are GPTs” (Eloundou et al.) arXiv 2303.10130; AI Occupational Exposure (AIOE) Felten, Raj & Seamans. Accessed June 7, 2026},
url = {https://singulariki.com/industries/221117}
} Citations name the underlying public dataset releases — they reflect what this page is built from, not just the URL.