Occupation · SOC 19-3011.01
Conduct economic analysis related to environmental protection and use of the natural environment, such as water, air, land, and renewable energy resources. Evaluate and quantify benefits, costs, incentives, and impacts of alternative options using economic principles and statistical techniques.
Also called: Environmental Economist · Natural Resource Economist · Research Economist · Resource Economist · Agricultural Economist · Ecological Economist · Energy Economist · Environment and Natural Resources Economics Researcher · Environmental Protection Economist · Marine Resource Economist · Natural Resource Specialist
Job family: Life, Physical, and Social Science Occupations
A source-stamped Markdown brief of this occupation — paste it into an agent, or fetch
/roles/role-19-3011-01/context.md directly.
A fast read on where AI already shows up in this occupation, where it stays a copilot, where humans remain in the loop, and what the labor market is doing. Built from observed Claude.ai conversations mapped to O*NET tasks and from published research — measures of usage and exposure, not advice or predictions that the job is going away.
Task areas where people work with AI — iterating, learning, or checking — staying in the loop rather than handing the task off.
Task areas where a human was still judged necessary in a large share of observed conversations — not a safety ruling, an observed-need signal.
The capabilities O*NET rates most important for this occupation — the human ground the work is built on.
See all skills →Independent published positions, read together — not a forecast.
99th-percentile task overlap — yet about 900 openings a year (+1.2% projected, BLS), and observed AI use leans 5428% copilot, not hand-off (AEI) . What exposure means →
What today's research says about this occupation's exposure to AI, how AI is actually being used in it, and where employment is headed. These are positions within published studies — measures of exposure and usage, not predictions that this job will disappear.
Each study uses its own scale, so the raw scores are not comparable across rows — the percentile (this job's rank among all U.S. occupations with data) is the comparable figure, and sizes the bars.
| Measure | Rank vs all occupations | Percentile | Score |
|---|---|---|---|
| Overall AI exposure (Felten et al.) High | 96th | 1.4 | |
| LLM task exposure, γ (OpenAI / Eloundou) High | 95th | 1.0 | |
| AI assistant applicability (Microsoft) High | 94th | 0.3 |
OpenAI's exposure study scores tasks three ways: with a language model alone (α 0.2), with simple added tooling (β 0.6), and including AI-powered software (γ 1.0). Higher means more of the job's tasks could be done at least twice as fast — not that they will be automated away.
Most of this job's tasks can be done remotely (Dingel–Neiman), which tends to track with higher digital and AI exposure.
A pre-LLM (2013) estimate of how automatable this job is by computerization and robotics. Shown for historical context only — it is not part of any current AI ranking.
Frey–Osborne probability 0.4 · 45th percentile among occupations · Moderate
Among measured AI assistant conversations mapped to this occupation (Anthropic Economic Index, 2026-01-15), these task types came up most. These are shares of observed AI conversations — not shares of the job, of worker time, or of what could be automated.
| Write technical documents or academic articles to communicate study results or economic forecasts. | 3.6% | |
| Prepare and deliver presentations to communicate economic and environmental study results, to present policy recommendations, or to raise awareness of environmental consequences. | 1.0% | |
| Develop economic models, forecasts, or scenarios to predict future economic and environmental outcomes. | 0.8% | |
| Interpret indicators to ascertain the overall health of an environment. | 0.5% | |
| Develop programs or policy recommendations to achieve environmental goals in cost-effective ways. | 0.4% | |
| Collect and analyze data to compare the environmental implications of economic policy or practice alternatives. | 0.2% |
Independent U.S. Bureau of Labor Statistics employment projection for 2024–2034 — a labor-market forecast, not an AI-impact forecast.
| Outlook | About average · +1.2% by 2034 |
| Projected annual openings | 900 |
| Employment 2024 → 2034 | 17,600 → 17,800 |
“Annual openings” counts new jobs plus replacements for workers who leave the occupation, so it can be large even when growth is modest.
The ILO's 2025 global study scores generative-AI exposure on the international ISCO-08 occupation system, not US SOC. Bridged through the published (and approximate, many-to-many) IBS O*NET-SOC ↔ ISCO-08 crosswalk, this US occupation corresponds to the international occupation below. Exposure here means how much of the work's tasks today's AI can attempt — task overlap, not automation, adoption, or jobs lost.
| International occupation (ISCO-08) | Task exposure (2025) | Most tasks fall in |
|---|---|---|
| Economists · 2631 | 55% | Gradient 3 |
Read the whole six-band gradient on the GenAI exposure gradient page. The crosswalk is approximate: a US occupation can map to several international ones, and the ILO scores describe the international occupation, not this exact US role.
How people actually apply AI to this occupation's tasks, from Claude.ai (Free and Pro) conversations in the Anthropic Economic Index, 2026-01-15. This is one AI assistant's consumer sample — not all AI, not the whole workforce. Autonomy and the collaboration mix are model-rated estimates; figures below the sample floor are hidden.
| Augmentation vs. automation | 54.3% working with AI · 35.9% handed to AI |
| Most common way people use AI here | Iteration · you and AI go back and forth |
| Typical AI autonomy | 4.0 / 5 · higher = AI acts more independently |
| Used for work (vs. personal / coursework) | 28.2% |
The role's most common tasks in AI conversations, each tagged with how people work with the AI on it. “Usage” is the share of observed conversations, not of the job.
| Task | How | Usage |
|---|---|---|
| Write technical documents or academic articles to communicate study results or economic forecasts. | Iteration | 2.3% |
Tasks where the model most often judged that a person remained necessary — a useful read on the current boundary, not a guarantee.
| Write technical documents or academic articles to communicate study results or economic forecasts. | 83.8% |
Example prompts phrased from the tasks people most often delegate to AI in this occupation (Anthropic Economic Index). Each shows the underlying measured task and its share of observed AI use. They are suggested phrasings of real tasks — starting points, not endorsed instructions.
Help me write technical documents or academic articles to communicate study results or economic forecasts. From: Write technical documents or academic articles to communicate study results or economic forecasts. · 2.3% of measured AI use · task iteration
All 20 tasks O*NET lists for this occupation, ordered by importance. Each links to its own page with AI-exposure and observed-use detail.
O*NET importance rating, from 1 (not important) to 5 (extremely important).
| Mathematics | 4.7 | |
| Economics and Accounting | 4.4 | |
| English Language | 4.0 | |
| Computers and Electronics | 3.7 | |
| Education and Training | 3.2 | |
| Law and Government | 3.1 |
| Written Comprehension | 4.3 | |
| Mathematical Reasoning | 4.1 | |
| Oral Comprehension | 4.0 | |
| Oral Expression | 4.0 | |
| Written Expression | 4.0 | |
| Problem Sensitivity | 3.9 | |
| Deductive Reasoning | 3.9 | |
| Inductive Reasoning | 3.9 | |
| Number Facility | 3.9 | |
| Speech Clarity | 3.9 | |
| Near Vision | 3.8 | |
| Speech Recognition | 3.6 | |
| Fluency of Ideas | 3.3 | |
| Originality | 3.3 | |
| Information Ordering | 3.3 | |
| Category Flexibility | 3.3 | |
| Selective Attention | 3.1 |
| Writing | 4.1 | |
| Reading Comprehension | 4.0 | |
| Active Listening | 4.0 | |
| Mathematics | 4.0 | |
| Critical Thinking | 4.0 | |
| Active Learning | 3.9 | |
| Speaking | 3.5 | |
| Monitoring | 3.5 | |
| Learning Strategies | 3.3 |
Skills employers ask for in job postings for this occupation (Lightcast), with whether each is a common or specialized skill.
How characteristic each condition is of the job, on O*NET's 1–5 context scale (higher = more present in day-to-day work). Each condition links to how it varies across all occupations.
What to study: Agriculture, Agriculture Operations, and Related Sciences , Business, Management, Marketing, and Related Support Services , Health Professions and Related Programs , Mathematics and Statistics , Multi/Interdisciplinary Studies , Natural Resources and Conservation , Social Sciences . Fields of study crosswalked to this occupation (NCES CIP–SOC), not a requirement.
Share of people in this occupation at each level of education.
| Doctoral Degree | 44.4% | |
| Master's Degree | 40.7% | |
| Bachelor's Degree | 11.1% | |
| Post-Doctoral Training | 3.7% |
The interests and personal qualities O*NET associates with people who do this work.
| Investigative | 7.0 | |
| Enterprising | 4.1 | |
| Conventional | 4.1 | |
| Realistic | 2.7 | |
| Artistic | 2.5 | |
| Social | 2.3 |
U.S. · annual wages (BLS OEWS)
| 10th percentile | $62,340 |
| 25th percentile | $82,260 |
| Median (50th) | $115,440 |
| 75th percentile | $166,030 |
| 90th percentile | $212,710 |
| People employed | 15,880 |
Wages and employment are reported by BLS for the broader occupation group this specialty belongs to (SOC 19-3011), not for the specialty alone.
Where these workers are employed, by number of jobs (national, BLS OEWS). Pay shown is the occupation's national median, not industry-specific.
| Industry | Workers | National median pay |
|---|---|---|
| Professional, Scientific, and Technical Services · Sector | 3,970 | $113,300 |
| Finance and Insurance · Sector | 1,500 | $165,960 |
| Research and Development in the Social Sciences and Humanities · National industry | 1,200 | $129,280 |
| Other Services (except Public Administration) · Sector | 530 | $100,900 |
| Educational Services · Sector | 360 | $87,020 |
| Management of Companies and Enterprises · Sector | 310 | $135,100 |
| Engineering Services · National industry | 270 | $92,270 |
| Direct Health and Medical Insurance Carriers · National industry | 250 | $101,290 |
| Health Care and Social Assistance · Sector | 160 | $107,780 |
| Utilities · Sector | 140 | $130,400 |
| Administrative and Support and Waste Management and Remediation Services · Sector | 110 | $90,460 |
| Mining, Quarrying, and Oil and Gas Extraction · Sector | 50 | $222,980 |
Industries where this occupation is far more common than in the economy as a whole. The location quotient is how many times more concentrated it is here (a value of 5 means five times its economy-wide share).
| Industry | Concentration | Workers |
|---|---|---|
| Research and Development in the Social Sciences and Humanities · National industry | 191.67× | 1,200 |
| Direct Health and Medical Insurance Carriers · National industry | 5.41× | 250 |
| Professional, Scientific, and Technical Services · Sector | 3.58× | 3,970 |
| Utilities · Sector | 2.35× | 140 |
| Finance and Insurance · Sector | 2.34× | 1,500 |
| Engineering Services · National industry | 2.27× | 270 |
| Other Services (except Public Administration) · Sector | 1.16× | 530 |
| Management of Companies and Enterprises · Sector | 1.07× | 310 |
Part of the Agriculture , Management & Entrepreneurship and Public Service & Safety career clusters.
Side-by-side comparisons place two occupations’ pay, preparation, skills, and AI exposure on the same page — same data, same scale, no forecast.
Options the data surfaces for Environmental Economists — not advice or a forecast. Each is a real cross-link you can follow into the evidence.
Capabilities this work builds that are used across many other occupations.
Occupations O*NET rates as related — the nearby moves on the map.
How people typically prepare for this work.
On the global GenAI exposure gradient this work sits around the 93rd percentile of 427 international occupations.
Environmental Economists show 99th-percentile AI task overlap — and about 900 annual U.S. openings
Environmental Economists show 99th-percentile AI task overlap — and about 900 annual U.S. openings • Environmental Economists rank in the 99th percentile (High band) for AI task overlap across U.S. occupations — a measure of how much of the work today's AI can attempt, not how much is automated. (Eloundou et al. (GPTs are GPTs) + Felten AIOE) • The occupation is projected to see about 900 U.S. job openings per year (2024–34), counting growth and replacement — a labor-demand projection made independently of AI. (BLS Employment Projections 2024–34) • BLS projects employment to be about average (+1.2%) from 2024 to 2034. (BLS Employment Projections 2024–34) • Median annual pay is $115,440, across about 15,880 U.S. workers. (BLS OEWS (May 2024)) • Of the AI use actually observed for this work, 54% looks like augmentation (drafting, iterating, checking) rather than hands-off automation — from a Claude.ai usage sample, not a census. (2026-01-15-v4-plus-2025-03-27-v2) Source: Singulariki — "Environmental Economists". https://singulariki.com/roles/role-19-3011-01 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.
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.
Data compiled June 2, 2026. Figures are estimates, not advice.
Singulariki. "Environmental Economists." Singulariki: a source-backed encyclopedia of work. Built from O*NET 30.3; BLS Occupational Employment and Wage Statistics (OEWS) May 2024; BLS Employment Projections 2024–2034; Anthropic Economic Index v4 (2026-01-15) + v2 (2025-03-27); Microsoft “Working with AI” working-with-ai; “GPTs are GPTs” (Eloundou et al.) arXiv 2303.10130; AI Occupational Exposure (AIOE) Felten, Raj & Seamans; ILO / Gmyrek et al. GenAI exposure gradient 2025; IBS O*NET-SOC ↔ ISCO-08 occupation crosswalk 2022; Frey & Osborne (2013) frey-osborne-automation; Dingel & Neiman (2020) dingel-neiman-workathome. Accessed June 7, 2026. https://singulariki.com/roles/role-19-3011-01
Singulariki. (2026). Environmental Economists. Singulariki: a source-backed encyclopedia of work. Retrieved June 7, 2026, from https://singulariki.com/roles/role-19-3011-01
@misc{singulariki-role-19-3011-01,
title = {Environmental Economists},
author = {{Singulariki}},
year = {2026},
note = {O*NET 30.3; BLS Occupational Employment and Wage Statistics (OEWS) May 2024; BLS Employment Projections 2024–2034; Anthropic Economic Index v4 (2026-01-15) + v2 (2025-03-27); Microsoft “Working with AI” working-with-ai; “GPTs are GPTs” (Eloundou et al.) arXiv 2303.10130; AI Occupational Exposure (AIOE) Felten, Raj & Seamans; ILO / Gmyrek et al. GenAI exposure gradient 2025; IBS O*NET-SOC ↔ ISCO-08 occupation crosswalk 2022; Frey & Osborne (2013) frey-osborne-automation; Dingel & Neiman (2020) dingel-neiman-workathome. Accessed June 7, 2026},
url = {https://singulariki.com/roles/role-19-3011-01}
} Citations name the underlying public dataset releases — they reflect what this page is built from, not just the URL.