Environmental Scientists and Specialists, Including Health
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Conduct research or perform investigation for the purpose of identifying, abating, or eliminating sources of pollutants or hazards that affect either the environment or public health. Using knowledge of various scientific disciplines, may collect, synthesize, study, report, and recommend action based on data derived from measurements or observations of air, food, soil, water, and other sources.
The occupation "Environmental Scientists and Specialists, Including Health" has an automation risk of 51.4%, which closely aligns with its base risk of 52.3%. This moderate level of automation risk can be attributed to the significant portion of the job that involves routine or data-driven tasks, which are increasingly feasible for AI and automation technologies. Many aspects of data management, monitoring, and routine communication are now within reach of advanced software and machine learning systems, leading to this middling risk ranking. However, the occupation is not considered highly automatable due to the specialized nature of key responsibilities that require human judgment, creativity, and nuanced understanding of complex environmental interactions. The top three most automatable tasks within this field clarify why the automation risk is relatively high. First, "Communicate scientific or technical information to the public, organizations, or internal audiences" can be partially automated through advanced natural language generation and dissemination tools. Automated reporting and digital communication platforms can handle much of the recurring documentation and presentation work. Second, "Monitor effects of pollution or land degradation and recommend means of prevention or control" is becoming more automatable with remote sensing, IoT, and AI-based analytics. Third, "Collect, synthesize, analyze, manage, and report environmental data" is already being transformed by automation tools that rapidly process large datasets and generate reports, reducing the need for manual data handling. On the other hand, the most resistant tasks underscore the ongoing need for human expertise. Developing novel methods to minimize environmental impacts, or designing research models using advanced mathematical and statistical knowledge, demands higher-order thinking and innovative capacities—areas where automation currently falls short. Similarly, creating programs to ensure sustainable land use draws not only on scientific knowledge but also on an understanding of social, legislative, and ecological considerations that are difficult for AI to fully comprehend and integrate. This is reflected in the identified bottleneck skill, Originality, with relatively low levels (3.1% and 3.6%)—indicating that the creative aspect of developing new solutions remains difficult to automate, and continues to safeguard portions of this occupation from full automation.