Share and intensity of work current AI systems can materially affect.
SOC 19-2041
Environmental Scientists and Specialists AI displacement risk
Environmental data synthesis, monitoring analysis, and report drafting are strongly AI-augmentable. Field sampling, regulatory interpretation, site inspection, and scientific guidance to agencies and industry keep the role grounded.
Likely potential for exposed tasks to move to software after workflow integration.
Climate reporting and environmental monitoring generate enormous data flows that AI helps process — which raises demand for scientists who can collect valid field data and defend conclusions in regulatory proceedings.
Distribution
Where Environmental Scientists and Specialists sits across 620 tracked roles
Displacement pressure 34 — higher than 56% of the 620 occupations tracked on displacement.ai.
Score version
This page uses Seed model v0.4 (seed-v0.4-2026-05), last reviewed 2026-08-08. Directional occupation-level planning model using hand-reviewed public research, task exposure estimates, wage context, and transition-pathway assumptions.
30 O*NET task statements matched to SOC 19-2041. The displayed task profile combines these official task statements with the current public score model.
Median wage context: $82,220 (May 2025, US national). The latest BLS row matched SOC 19-2041.
Scores are planning signals, not forecasts. Local hiring demand, employer-specific workflows, licensing, and credentials must be validated before making career decisions.
2030 economic stress test
How Anthropic's scenarios classify Environmental Scientists and Specialists
SOC 19-2041 places this role in the paper's cognitive occupation group. These group-level outcomes do not change the 34/100 role score and are not an occupation forecast.
+0.4% group wage
-0.5% cognitive employment since mid-2026; 2.9% cognitive unemployment.
Economy-wide: +1.6% GDP and 3.9% unemployment.
-0.3% group wage
-3.9% cognitive employment since mid-2026; 4.5% cognitive unemployment.
Economy-wide: +8.3% GDP and 4.6% unemployment.
-11.5% group wage
-21.5% cognitive employment since mid-2026; 17.9% cognitive unemployment.
Economy-wide: +32.4% GDP and 11.9% unemployment.
Compare the assumptions and limitations across all three scenarios. Source: The Anthropic Institute Working Paper No. 2026-02.
Official task evidence
O*NET task matches for Environmental Scientists and Specialists
The current evidence import matched 30 task statements from Task Statements 31.0 (August 2026). These rows are used as a grounding layer for judging which parts of the occupation are repeatable, language-heavy, analytical, social, physical, or compliance-sensitive.
Dataset31.0 (August 2026)
Matched tasks30
SOC19-2041
- Core task / ID 15219
Communicate scientific or technical information to the public, organizations, or internal audiences through oral briefings, written documents, workshops, conferences, training sessions, or public hearings.
- Core task / ID 1530
Monitor effects of pollution or land degradation and recommend means of prevention or control.
- Core task / ID 15218
Collect, synthesize, analyze, manage, and report environmental data, such as pollution emission measurements, atmospheric monitoring measurements, meteorological or mineralogical information, or soil or water samples.
- Core task / ID 1517
Review and implement environmental technical standards, guidelines, policies, and formal regulations that meet all appropriate requirements.
- Core task / ID 15220
Provide scientific or technical guidance, support, coordination, or oversight to governmental agencies, environmental programs, industry, or the public.
- Core task / ID 15221
Process and review environmental permits, licenses, or related materials.
Source: O*NET Resource Center, Task Statements. Raw import target: data/raw/onet/task-statements-31-0.txt.
Task profile
Where AI changes the work
Collect and analyze environmental data
Exposure 60, automation 33%, augmentation 70%.
O*NET evidence: Collect, synthesize, analyze, manage, and report environmental data, such as pollution ... (ID 15218)
Conduct field sampling and inspections
Exposure 26, automation 10%, augmentation 46%.
O*NET evidence: Conduct environmental audits or inspections or investigations of violations. (ID 1514)
Prepare technical reports and briefings
Exposure 68, automation 38%, augmentation 72%.
O*NET evidence: Communicate scientific or technical information to the public, organizations, or intern... (ID 15219)
Advise on standards and regulations
Exposure 36, automation 14%, augmentation 58%.
O*NET evidence: Review and implement environmental technical standards, guidelines, policies, and forma... (ID 1517)
TaskExposureAutomationAugmentation
Collect and analyze environmental data6033%70%
Conduct field sampling and inspections2610%46%
Prepare technical reports and briefings6838%72%
Advise on standards and regulations3614%58%
Transition pathways
Adjacent moves that preserve existing skills
adjacent role
ModerateClimate Policy Analyst
Training horizon: 4-9 months. Skill overlap 64. Wage preservation signal 108.
- Study climate policy frameworks
- Analyze emissions data
- Brief decision-makers
role redesign
ModerateEnvironmental Data Scientist
Training horizon: 3-8 months. Skill overlap 66. Wage preservation signal 116.
- Build monitoring data pipelines
- Model environmental scenarios
- Validate AI-generated analyses
Comparison guides
Compare the next move before you commit
4-9 months
Environmental Scientists and Specialists to Climate Policy Analyst
Compare AI displacement pressure, wage preservation, skill overlap, training time, and first proof project for moving from Environmental Scientists and Specialists into Climate Policy Analyst.
3-8 months
Environmental Scientists and Specialists to Environmental Data Scientist
Compare AI displacement pressure, wage preservation, skill overlap, training time, and first proof project for moving from Environmental Scientists and Specialists into Environmental Data Scientist.
What the AI risk score means for Environmental Scientists and Specialists
The displacement pressure score for Environmental Scientists and Specialists is 34. That score blends task exposure, automation pressure, augmentation potential, wage vulnerability, transition feasibility, and source confidence. It is designed to help workers and workforce teams decide where to act first, not to claim a specific date when a job will disappear.
For this role, the clearest risk pattern is visible at the task level. Prepare technical reports and briefings carries 38% automation pressure, while Prepare technical reports and briefings carries 72% augmentation potential. That means the best response is usually a targeted redesign of work: move away from repeatable production tasks and toward judgment, exception handling, coordination, stakeholder context, and accountable use of AI tools.
Labor-market context and wage risk
Median wage: $82,220 (May 2025, US national). Employment context: Environmental analysis role with climate-driven demand. Typical education: Bachelor's degree common.
Wage vulnerability is 30, while transition feasibility is 68. A high wage-vulnerability score means workers should pay close attention to salary preservation before making a move. A high transition-feasibility score means there are adjacent paths that can reuse existing skills without requiring a complete career reset.
- Moderate displacement pressure
- Climate data volumes grow the field
- Fieldwork anchors the science
Upskilling priorities
Skills that make this role more resilient
The safest upskilling plan starts with skills already close to the work. For Environmental Scientists and Specialists, the strongest near-term skill priorities are listed below. These are useful whether the goal is to stay in the role, move to a redesigned version of the role, or transition into an adjacent occupation.
Field methods
Build proof of this skill through a work sample, checklist, dashboard, case note, workflow map, or portfolio artifact tied to the transition paths on this page.
Data analysis
Build proof of this skill through a work sample, checklist, dashboard, case note, workflow map, or portfolio artifact tied to the transition paths on this page.
Regulatory interpretation
Build proof of this skill through a work sample, checklist, dashboard, case note, workflow map, or portfolio artifact tied to the transition paths on this page.
Scientific communication
Build proof of this skill through a work sample, checklist, dashboard, case note, workflow map, or portfolio artifact tied to the transition paths on this page.
90-day transition plan
The most practical next step is not to wait for a layoff or a full role redesign. Use the next 90 days to create evidence that you can operate in a safer, more AI-augmented version of the work.
- In the first 30 days, document the repetitive tasks in your current work and identify where AI can reduce drafting, lookup, classification, or reporting time.
- By 60 days, complete one small project connected to Climate Policy Analyst, such as study climate policy frameworks.
- By 90 days, compare internal openings and external postings for Climate Policy Analyst or Environmental Data Scientist and update your resume around measurable workflow outcomes.
FAQ
Questions about AI and Environmental Scientists and Specialists
Will AI replace Environmental Scientists and Specialists?
Environmental data synthesis, monitoring analysis, and report drafting are strongly AI-augmentable. Field sampling, regulatory interpretation, site inspection, and scientific guidance to agencies and industry keep the role grounded. The better planning signal is not full replacement, but which tasks become automated, which tasks become AI-assisted, and which responsibilities still need human judgment.
Which parts of Environmental Scientists and Specialists work are most exposed to AI?
Prepare technical reports and briefings and Collect and analyze environmental data show the strongest automation pressure in this model. Prepare technical reports and briefings and Collect and analyze environmental data are better treated as AI-augmented work.
What should Environmental Scientists and Specialists learn next?
Start with Field methods, Data analysis, Regulatory interpretation. The most practical adjacent paths in this model are Climate Policy Analyst and Environmental Data Scientist.
How should this score be used?
Use it as a planning signal, not a prediction. Confirm local hiring demand, wages, licensing, credentials, and employer adoption before making a career move.
Sources
Evidence trail
International Labour Organization / 20 May 2025Generative AI and Jobs: A Refined Global Index of Occupational ExposureOECD / 11 July 2023OECD Employment Outlook 2023: Artificial Intelligence and the Labour MarketO*NET Resource Center / Database 31.0 (August 2026)Occupation, Task, Skill, and Work-Activity DataUS Bureau of Labor Statistics / May 2025 estimates (released 15 May 2026)Occupational Employment and Wage Statistics