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Environmental science professors

Official title Environmental Science Teachers, Postsecondary (SOC 25-1053)

Also called Environmental sciences professor, Env sci postsec instructor, Environmental studies professor, Environmental science faculty, Environmental science lecturer

In brief

In May 2025, BLS counted 6,690 US environmental science professors, with median pay of $94,980 a year. Of the BLS counts since 2003, the highest was 7,130, in May 2024. The earliest figure here is an estimate of 1,800 from 1975, not measured the same way as today's count. The last full technology era for environmental science professors: Cloud GIS + real-time sensor networks, 2010 to 2020.

What the sources say

Official outlook

BLS projects employment to grow 2.7% from 2025 to 2035.

BLS Employment Projections 2025-35

AI exposure studies

Data as of BLS OEWS May 2025; BLS Employment Projections 2025-35. BLS publishes these counts as yearly survey snapshots and cautions against reading them as a precise trend. In May 2021 BLS changed how it estimates these counts (a model-based method using three years of survey data) and moved fully to the 2018 occupation codes, so part of a change across 2021 can come from the method rather than the work.

Last updated .

Cite this page
APA

Future History. (2026, October 11). Environmental science professors: jobs and pay since 1975 [Data set; data as of BLS OEWS May 2025; BLS Employment Projections 2025-35]. https://futurehistory.earth/timeline/25-1053

Chicago

Future History. "Environmental science professors: jobs and pay since 1975." Data as of BLS OEWS May 2025; BLS Employment Projections 2025-35. Last modified October 11, 2026. https://futurehistory.earth/timeline/25-1053.

BibTeX

@misc{futurehistory_25_1053, author = {{Future History}}, title = {Environmental science professors: jobs and pay since 1975}, year = {2026}, month = oct, howpublished = {\url{https://futurehistory.earth/timeline/25-1053}}, note = {Last updated 2026-10-11. Data as of BLS OEWS May 2025; BLS Employment Projections 2025-35}, }

The access date is today's date on your device.

drag to travel through time
19752000now
2026
Known then as Environmental Studies / Ecology Professor (program-formation era)
Known then as Environmental Science / Natural Resources Professor (regulatory-science era)
Known then as Environmental Science Teachers, Postsecondary (pre-SOC-2000 BLS classification)
Known then as Environmental Science Teachers, Postsecondary (BLS SOC 25-1053)
Known today as Environmental Science Teachers, Postsecondary (BLS SOC 25-1053)
Projected to still be called Environmental Science Teachers, Postsecondary (BLS SOC 25-1053)
Data from the May 2025 survey
US Employment
7K
Est.

Stated in source: BLS OEWS

Official statistic
Report an error
Median Annual Wage
$94,980
Est.

≈ $92,545 in 2024 prices (derived by us, CPI-adjusted)

Official statistic
Report an error
Beat · 1962

Rachel Carson's Silent Spring is published on September 27, 1962. The book documents pesticide harms to bird populations and ecosystems, reaches millions of general readers, and catalyzes the environmental movement that will produce Earth Day, NEPA, and the EPA eight years later. Without Silent Spring, the legislative wave that created the need for environmental science faculty would likely have been delayed by a decade.

Beat · 1970

Earth Day draws 20 million participants on April 22, 1970, the largest civic demonstration in US history to that date. NEPA takes effect January 1. The EPA opens December 2. The Clean Air Act passes December 31. Colleges and universities immediately begin launching environmental science and environmental studies programs; UC Santa Barbara launches one of the first in 1970. The modern occupation is born in a single legislative year.

Beat · 1980

The early-1980s recession triggered by Federal Reserve Chair Paul Volcker's interest rate increases transforms the environmental science and ecology faculty job market almost overnight. Faculty positions that had been filled directly from PhD programs in the 1960s and 1970s now require 2-4 years of postdoctoral experience and 5-10 publications. The postdoctoral holding pattern that defines the modern academic science career path solidifies during this period.

Beat · 1992

The UN Earth Summit in Rio de Janeiro (June 1992) and the Exxon Valdez oil spill (March 1989) drive a wave of public and political attention to environmental issues. Enrollment in environmental science programs at US universities increases noticeably in the early 1990s, supporting new faculty hiring after a decade of stagnation following the 1980s recession.

Beat · 2003

NIH budget growth that had expanded research funding through the 1990s levels off around 2003, creating a mismatch between the large cohort of environmental science PhD graduates and the stagnant number of available faculty lines. Postdoctoral appointments lengthen; the typical path to a tenure-track environmental science position now involves two or more postdoctoral appointments and a decade or more of training after the PhD.

Beat · 2023

NSF introduces mandatory AI use disclosure requirements for grant proposals. Environmental science faculty now routinely use AI tools in proposal writing, literature synthesis, and course preparation, and are expected to explicitly document and justify that use. The disclosure requirement signals a formal recognition that AI tools are embedded in the research and pedagogical workflow rather than exceptional or experimental.

How to read this number

OEWS is a point-in-time survey snapshot, not a continuous time series; BLS advises against using it for year-over-year trend comparison.

Each dot is a cited figure over time; the dotted line only links them (values between aren't measured). Hollow dots are estimates.

Official statistic: Copied by a script from a statistics agency table or a published study's table, not retyped by hand. BLS figures and the forecast and exposure tables are also re-checked against that table on every data run. Researched context: Researched from the cited source rather than copied from it by a script, so it is not machine-checked yet. Spot a mistake? Each figure has a “Report an error” link.

Cited forecasts · present → 2035

Will AI replace environmental science professors? What the research says

Each figure is stated by the named source; we publish no forecasts of our own. Where sources disagree, we show the disagreement rather than an average.

Each figure is stated by the named source; we publish no forecasts of our own. Where sources disagree, the spread is the honest signal.

What published forecasts say
Forecast change in the number of people doing this work, as published by each named source. Bars share one scale on every role, centred on zero and reaching the end at ±30%. Grey marks a small change, 3% or less either way.
BLS Employment Projections 2025-35
→ 2035
+2.7%
BLS National Employment Matrix, 2025-35 cycle, line for SOC 25-1053 (Environmental science teachers, postsecondary): 8.4 thousand jobs in 2025, projected 8.7 thousand in 2035, a change of +0.2 thousand (+2.7%). Matrix employment counts all jobs in the occupation, including self-employed workers. The average for all occupations in this cycle is +3.5%.
Official statisticReport an error
How exposed is this work to AI (research measures)
Scores as published by each study. Each row says what it measures. These measure what AI could technically do, not forecasts of jobs lost.
Eloundou et al. (2023), "GPTs are GPTs"
2023 study · Share of tasks exposed to LLMs (β, human-rated)
42.4%
of tasks
What this measures

Published occupation-level exposure from the paper's occ_level.csv for O*NET-SOC 25-1053.00 Environmental Science Teachers, Postsecondary. Shown: human-rated β = 0.424, the share of the occupation's tasks an LLM could do in at least half the time at equal quality, counting tasks that need LLM-powered software at half weight (β = E1 + 0.5 × E2). Other published measures for this occupation: human-rated α (LLM alone) 0.196 and γ (all LLM-assisted) 0.652; GPT-4-rated β 0.522. This measures how much of the work an LLM could touch, not how many jobs disappear.

Official statisticReport an error
Today, in this role

What's changing day to day for environmental science professors

Three parts of your work where AI is already doing real lifting, and what stays yours.

AI is sitting alongside you hereGrade and provide feedback on student coursework (environmental impact assessment reports, field lab notebooks, policy analysis papers, and examinations) using Gradescope AI-assisted answer grouping for structured submissions, while applying expert scientific and regulatory judgment to evaluate whether a student's environmental risk assessment, pollutant fate-and-transport analysis, or environmental policy argument reflects sound scientific reasoning and compliance with EPA or IPCC methodology standards.

Grade and provide feedback on student coursework (environmental impact assessment reports, field lab notebooks, policy analysis papers, and examinations) using Gradescope AI-assisted answer grouping for structured submissions, while applying expert scientific and regulatory judgment to evaluate whether a student's environmental risk assessment, pollutant fate-and-transport analysis, or environmental policy argument reflects sound scientific reasoning and compliance with EPA or IPCC methodology standards.[12],[1]

Tools picking this up
Where your edge is

Deploy Gradescope for environmental lab reports and structured exam questions: AI-assisted answer grouping clusters similar reasoning strategies (same flawed fate-and-transport calculation, same misapplication of the CERCLA liability standard) so you apply a rubric once per pattern rather than once per submission, yielding the documented 30–50% time savings. Run Turnitin AI detection on environmental impact assessment and policy papers: these are the assignment types most vulnerable to AI generation. Reserve manual evaluation for the scientifically substantive judgment: does this student's risk characterization correctly apply EPA risk assessment guidelines? Does this water-budget calculation account for evapotranspiration correctly? These are the questions that distinguish scientific environmental understanding from AI-generated plausibility.

AI is sitting alongside you herePrepare course materials (syllabi, lecture slides, lab procedures, environmental impact assessment templates, and policy analysis frameworks on topics including environmental regulations [CERCLA, RCRA, CWA, CAA], climate science, toxicology, and ecological risk assessment) using AI tools (ChatGPT Edu, NotebookLM) to generate first drafts and synthesize background literature, then editing with domain expertise to ensure scientific and regulatory accuracy and alignment with current EPA guidance and IPCC findings.

Prepare course materials (syllabi, lecture slides, lab procedures, environmental impact assessment templates, and policy analysis frameworks on topics including environmental regulations [CERCLA, RCRA, CWA, CAA], climate science, toxicology, and ecological risk assessment) using AI tools (ChatGPT Edu, NotebookLM) to generate first drafts and synthesize background literature, then editing with domain expertise to ensure scientific and regulatory accuracy and alignment with current EPA guidance and IPCC findings.[11],[1]

Where your edge is

Use ChatGPT Edu to generate a first-draft lecture on remediation technologies for contaminated sites or a problem set on fate-and-transport of persistent organic pollutants: it produces reasonable structure and standard content. Invest expert effort in verifying regulatory accuracy (AI frequently misattributes cleanup standards, confuses CERCLA and RCRA liability frameworks, or cites outdated EPA risk assessment factors), adding current case studies, and incorporating AI-tool exercises. Use NotebookLM to upload the latest IPCC AR6 Working Group reports or EPA risk assessment guidelines and synthesize them into course-specific background sections, particularly effective for rapidly evolving topics like AI-derived environmental monitoring regulations.

AI is sitting alongside you hereKeep current with rapid advances in AI-augmented environmental science (monitoring new releases of CMIP6 ML emulators [ClimaX updates, NeuralGCM climate scenario releases], AI-assisted eDNA monitoring tools, Google Earth Engine new ML capabilities, EPA guidance on AI in environmental compliance, and fast-moving literature on AI for environmental justice and chemical exposure modeling) using AI research synthesis tools (Elicit, Consensus) to monitor high-velocity literature across ecology, atmospheric science, and environmental engineering simultaneously.

Keep current with rapid advances in AI-augmented environmental science (monitoring new releases of CMIP6 ML emulators [ClimaX updates, NeuralGCM climate scenario releases], AI-assisted eDNA monitoring tools, Google Earth Engine new ML capabilities, EPA guidance on AI in environmental compliance, and fast-moving literature on AI for environmental justice and chemical exposure modeling) using AI research synthesis tools (Elicit, Consensus) to monitor high-velocity literature across ecology, atmospheric science, and environmental engineering simultaneously.[13],[6]

Where your edge is

Use Elicit to set up standing literature monitoring queries across Environmental Science & Technology, Global Change Biology, Nature Climate Change, and the EPA Science Inventory: the AI-environmental-science literature is advancing faster than any individual can track. Use Consensus to quickly evaluate whether a new preprint's AI-derived deforestation alert claim is consistent with ground-truth validation studies before updating course materials. Use NotebookLM to synthesize a semester's worth of new IPCC or EPA guidance into course revision input before each term. The key expert judgment is deciding which new AI environmental tools represent genuine capability advances worth incorporating into research workflows or curriculum versus incremental benchmark papers: a discrimination that requires your domain expertise.

Where environmental science professors go next

Natural next steps for someone with your foundation: not exits, evolutions.

What each move takes

A direction you could grow

Education Administrators, Postsecondary

Environmental science faculty who have demonstrated leadership in curriculum redesign, interdisciplinary sustainability program development, field program management, or environmental accreditation review are strong candidates for department chair, associate dean of science, or director of sustainability research center roles. These programs are under significant pressure in 2025–2026 to integrate AI geospatial tools, climate ML, and eDNA metagenomics into degree curricula while responding to growing demand from EPA, consulting, and NGO employers for graduates with these competencies, and to the curriculum integrity challenge that AI-generated environmental impact assessment papers pose. Administrators with both environmental science credibility and governance experience are disproportionately valuable navigating these transitions. The CRI increase reflects that postsecondary education administration is growing (+7% BLS 2024–2034) and that sustainability and environmental science programs are seeing increased enrollment as climate risk drives employer and student demand.

What you'd add
  • · Higher education budget management: faculty line planning, field equipment capital requests (sampling equipment, GIS workstations, field vehicles), NSF and EPA center grant indirect-cost administration, external advisory board management
  • · STEM accreditation processes: ABET accreditation for environmental engineering programs, regional accreditation self-study coordination, NAAEE environmental education standards, program review for interdisciplinary sustainability degrees
  • · Faculty performance review: promotion/tenure facilitation in field-intensive and interdisciplinary environmental programs, hiring committee leadership for computational vs. field-science balance in the faculty portfolio
  • · Industry and agency partnership development: relationships with EPA regional offices, state environmental agencies, NOAA, USGS, and environmental consulting firms for student internship pipelines and sponsored research; corporate sustainability advisory boards
  • · Institutional AI governance for environmental science: developing department policies on AI use in environmental impact assessment coursework, research data management, and graduate thesis writing; evaluating geospatial AI platforms for departmental licensing
What it takesSome new skills to pick up
Tools of the era

How has the work of environmental science professors changed since 1970?

The tools that defined the work, one line per era. Select an era to see how it reshaped the work.

  • Field notebooks, glass chemistry, and slide rules (pre-digital environmental analysis)

    The first environmental science faculty operated with the same instruments ecologists and chemists had used for decades: field notebooks, hand-drawn topographic overlays, wet-chemistry analysis in a lab, and mechanical calculations. Teaching a water quality course meant students titrated water samples by hand, calculated dissolved oxygen from Winkler method tables, and read spectrometer dial gauges. Environmental sampling protocols were manual: grab samples in glass bottles, chain of custody on paper forms, gas chromatography results read from paper strip charts. The pedagogical challenge was translating the emerging regulatory language of NEPA and the Clean Air Act into scientific practice using instruments designed for other questions.

  • Personal computer + early GIS (Arc/INFO 1982, ArcView 1991, EPA-approved analytical software)

    ESRI shipped Arc/INFO for workstations in 1982 and ArcView for desktop PCs in 1991, giving environmental science faculty their first accessible geographic information system. For the first time, a faculty member could show students a spatially explicit map of a pollution plume, model watershed boundaries, or overlay land-use change against habitat data in a classroom setting. EPA and state agencies began requiring GIS-formatted deliverables for Environmental Impact Statements and remediation plans during this period, which pulled GIS into environmental science curricula as a near-required competency. Simultaneously, EPA-certified analytical chemistry software (for gas chromatography data reduction, toxicity modeling via MINTEQ and WASP) replaced paper calculation entirely. This era is when the computer screen replaced the slide rule as the central instrument in environmental science education.

    Effect on the work

    GIS adoption increased the analytical scope of what a single faculty member could teach: courses that previously required large institutional computing resources became teachable on desktop workstations, lowering the barrier for regional universities to offer competitive environmental science programs.

  • Remote sensing satellites + internet data access (Landsat, MODIS; EPA ECHO, EPA STORET, USGS StreamStats)

    Free access to Landsat imagery expanded radically after the USGS opened the archive in 2008. MODIS (operational from 2000) and Landsat gave environmental science courses continuous global satellite coverage at no cost. Simultaneously, EPA's ECHO compliance database, STORET water quality portal, and USGS StreamStats opened decades of environmental monitoring data to students with a web browser. A land-cover change analysis that previously required a federal grant and a mainframe became an undergraduate homework assignment. Remote sensing replaced the purely local orientation of earlier environmental science courses: students could now analyze deforestation in the Amazon or bleaching on the Great Barrier Reef from a desktop in Ohio. Faculty who mastered remote sensing analysis became more competitive for NSF and EPA grants that required landscape-scale analysis.

    Effect on the work

    Open satellite archives and online regulatory databases substantially raised the expected analytical sophistication of environmental science coursework, increasing the premium on faculty with remote sensing and geospatial skills and creating a widening competency gap between research-active and teaching-only faculty.

  • Cloud GIS + real-time sensor networks (Google Earth Engine 2010, ArcGIS Online, EPA AQS API, USGS NWIS)

    Google Earth Engine (launched 2010 for research and education) moved environmental data analysis from installed software on departmental servers to a browser-accessible cloud platform hosting petabytes of satellite imagery and pre-built machine learning classifiers. A faculty member in a classroom could now run a random forest land-cover classifier over ten years of Landsat imagery in minutes, live, with students watching. ArcGIS Online extended desktop GIS to collaborative cloud workflows. Real-time sensor network APIs (EPA AQS for air quality, USGS NWIS for streamflow and water quality) made live environmental data a routine course input rather than a special project. Environmental science pedagogy shifted from analyzing historical datasets toward teaching students to work with continuously streaming environmental monitoring data and to evaluate the uncertainty in automated sensor outputs.

    Effect on the work

    Cloud GIS dramatically reduced the infrastructure cost of running a competitive environmental analysis course, enabling smaller regional universities to offer curriculum that matched research-intensive institutions. Faculty who had not transitioned from desktop GIS to cloud-based workflows began to fall behind in both teaching effectiveness and grant competitiveness.

  • AI environmental foundation models (ClimaX, NeuralGCM, Microsoft Aurora) + AI-assisted research and teaching (Elicit, Gradescope, Google Earth Engine ML classifiers)

    From 2020 onward, a cluster of AI environmental tools shifted the methodological baseline for environmental science research and education. Google Earth Engine added deep learning classifiers (semantic segmentation, change detection) directly into its cloud platform, making AI-derived land-cover and water-quality analysis routine in undergraduate courses. Microsoft Research published ClimaX in 2023 (ICML), the first foundation model for weather and climate pre-trained on the CMIP6 multi-model ensemble: for the first time students could run climate scenario comparisons without HPC access. Google DeepMind published NeuralGCM in Nature (2024), a hybrid neural-physics climate model capable of both weather forecasts and multidecadal projections. Microsoft Aurora (arXiv:2405.13063, May 2024) became a fine-tuneable atmospheric foundation model for air quality forecasts. These tools transformed the environmental science faculty role: the new core competency is not just running the tools but teaching students when AI-derived environmental outputs are physically plausible, properly uncertainty-quantified, and appropriate for regulatory or policy decisions. Simultaneously, Elicit (138M+ papers) compressed literature review by up to 80%, Gradescope reduced lab-report grading time by 30-50%, and ChatGPT Edu (deployed system-wide at CSU in February 2025, hundreds of thousands of users) handled first-draft lecture scaffolding on topics from biogeochemical cycling to CERCLA liability frameworks.

    Effect on the work

    The Federal Reserve (February 2026) placed life, physical, and social science teachers in the above-average AI-exposure band for postsecondary education, reflecting real erosion of lecture-preparation, report-grading, and literature-monitoring tasks. Faculty who integrate the AI tool stack can redirect several hours per week toward field research, mentorship, and the high-value work of teaching students to critically evaluate AI environmental outputs.

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Environmental Science Teachers, Postsecondary employment and pay by year

On record since1970
Latest US employment6,690 (May 2025)Source: BLS OEWS May 2025Official statisticReport an error
Latest US median pay$94,980 (May 2025)Source: BLS OEWS May 2025Official statisticReport an error
Outlook+2.7% by 2035Source: BLS Employment Projections 2025-35Official statisticReport an error
View all 28 years of cited US data, each linked to its source
YearUS employmentAnnual pay (median unless marked)SourceReport
19751,800n/aEst., Encyclopedia.com Researched contextReport
1980n/a$20,400Est., AAUP Researched contextReport
19853,500n/aEst., NCES Researched contextReport
19935,000$42,000Est., NCES (employment) Researched contextEst., NCES (pay) Researched contextReport
20006,200n/aEst., O*NET 25-1053.00 Researched contextReport
20033,620$57,830BLS OEWS May 2003 Official statisticReport
20043,860$62,330BLS OEWS May 2004 Official statisticReport
20054,340$60,880BLS OEWS May 2005 Official statisticReport
20064,310$64,780BLS OEWS May 2006 Official statisticReport
20074,470$64,850BLS OEWS May 2007 Official statisticReport
20084,870$65,130BLS OEWS May 2008 Official statisticReport
20094,820$65,540BLS OEWS May 2009 Official statisticReport
20105,090$71,020BLS OEWS May 2010 Official statisticReport
20114,990$75,050BLS OEWS May 2011 Official statisticReport
20124,990$77,320BLS OEWS May 2012 Official statisticReport
20135,130$78,490BLS OEWS May 2013 Official statisticReport
20145,300$77,470BLS OEWS May 2014 Official statisticReport
20155,540$78,770BLS OEWS May 2015 Official statisticReport
20165,520$78,340BLS OEWS May 2016 Official statisticReport
20175,990$76,360BLS OEWS May 2017 Official statisticReport
20186,040$79,910BLS OEWS May 2018 Official statisticReport
20196,060$82,430BLS OEWS May 2019 Official statisticReport
20205,860$84,740BLS OEWS May 2020 Official statisticReport
20215,440$81,980BLS OEWS May 2021 Official statisticReport
20226,240$83,040BLS OEWS May 2022 Official statisticReport
20237,120$88,410BLS OEWS May 2023 Official statisticReport
20247,130$87,710BLS OEWS May 2024 Official statisticReport
20256,690$94,980BLS OEWS May 2025 Official statisticReport
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