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.
U.S. Workers
15,880
Median Salary
$115,440
10-Year Growth
+1.2%
Annual Openings
900
Typical entry: Master's degree
19 of 19 tasks have some AI capability
Exposure Trend
This score reflects estimated AI technical capability for tasks in this occupation. It does not predict employment changes, and it does not account for company-specific constraints, regulation, or adoption barriers.
Write technical documents or academic articles to communicate study results or economic forecasts.
AI: Fully automatable - By 2025 generative models can produce high‑quality technical documents and academic‑style articles to communicate results and forecasts given data and guidance, requiring only human review for accuracy and citation checking.
Prepare and deliver presentations to communicate economic and environmental study results, to present policy recommendations, or to raise awareness of environmental consequences.
AI: Fully automatable - AI in 2025 can generate polished slide decks, visuals, speaker notes and even synthesized deliveries, enabling end‑to‑end preparation and delivery of presentations with minimal human input for tailoring.
Write research proposals and grant applications to obtain private or public funding for environmental and economic studies.
AI: Fully automatable - By 2025 AI can draft competitive research proposals and grant narratives, tailor applications to calls, and format required sections, though human input is typically needed for precise budgets and final signoffs.
Monitor or analyze market and environmental trends.
AI: Fully automatable - AI can largely automate ingestion of diverse data streams, monitoring pipelines, and statistical/ML analyses to detect and forecast market and environmental trends at scale.
Identify and recommend environmentally friendly business practices.
AI: Fully automatable - AI can identify and recommend evidence-based, context-tailored environmentally friendly business practices and produce actionable guidance that organizations can implement with routine oversight.
Conduct research on economic and environmental topics, such as alternative fuel use, public and private land use, soil conservation, air and water pollution control, and endangered species protection.
AI: Partial - AI can perform literature review, synthesize findings, and analyze existing datasets for environmental economic research, but cannot fully replace on‑the‑ground data collection, field experiments, and nuanced expert judgment.
Assess the costs and benefits of various activities, policies, or regulations that affect the environment or natural resource stocks.
AI: Partial - AI can run cost‑benefit calculations and model scenarios, but valuations of nonmarket goods, ethical tradeoffs, and final policy judgments still require human oversight and contextual knowledge.
Collect and analyze data to compare the environmental implications of economic policy or practice alternatives.
AI: Partial - Automated tools and AI can scrape, harmonize, and analyze many datasets to compare policy implications, yet field data collection, data‑quality validation, and some contextual interpretation remain human‑dependent.
Develop programs or policy recommendations to achieve environmental goals in cost-effective ways.
AI: Partial - AI can design cost‑effective program alternatives and model expected outcomes, but crafting practicable policy programs requires stakeholder engagement, political judgment, and implementation planning that humans must lead.
Perform complex, dynamic, and integrated mathematical modeling of ecological, environmental, or economic systems.
AI: Partial - AI and computational tools can construct and solve complex dynamic models, yet building validated, integrated ecological‑economic models and interpreting results for policy remains a specialist discipline requiring human expertise.
Conduct research to study the relationships among environmental problems and patterns of economic production and consumption.
AI: Partial - AI can support research into links between environmental problems and production/consumption patterns through synthesis and analysis, but cannot fully replace fieldwork, causal identification, and expert interpretation.
Write social, legal, or economic impact statements to inform decision makers for natural resource policies, standards, or programs.
AI: Partial - AI can synthesize evidence and draft social, legal, and economic impact statements but lacks reliable legal judgment and the stakeholder validation needed for final decision-ready reports.
Develop environmental research project plans, including information on budgets, goals, deliverables, timelines, and resource requirements.
AI: Partial - AI can produce detailed research plans with budgets, goals, deliverables, timelines, and resource estimates, but feasibility, institutional constraints, and final approvals require human project management and domain oversight.
Develop economic models, forecasts, or scenarios to predict future economic and environmental outcomes.
AI: Partial - AI can build, calibrate, and run economic-environmental models and generate scenarios and forecasts, but model specification, strong causal inference, and high-stakes interpretation still need expert judgment.
Develop programs or policy recommendations to promote sustainability and sustainable development.
AI: Partial - AI can draft programs and evidence-based policy recommendations for sustainability, but cannot fully handle political trade-offs, stakeholder negotiation, and implementation realities without human leadership.
Demonstrate or promote the economic benefits of sound environmental regulations.
AI: Partial - AI can quantify and communicate the economic benefits of environmental regulations using data and models, but credibility in policymaking requires expert review and contextualization.
Develop systems for collecting, analyzing, and interpreting environmental and economic data.
AI: Partial - AI can design and prototype systems for data collection, analysis, and interpretation and generate code, but building robust, secure, production-grade systems requires human engineers and governance.
Examine the exhaustibility of natural resources or the long-term costs of environmental rehabilitation.
AI: Partial - AI can model exhaustibility and estimate long-term rehabilitation costs using available data and scenarios, but deep uncertainty, valuation choices, and ground-truthing demand human expertise.
Interpret indicators to ascertain the overall health of an environment.
AI: Partial - AI can interpret many environmental indicators and flag potential health issues, but comprehensive ecosystem health assessments require field measurements, local knowledge, and expert synthesis.