Environmental Restoration Planners

Collaborate with field and biology staff to oversee the implementation of restoration projects and to develop new products. Process and synthesize complex scientific data into practical strategies for restoration, monitoring or management.

The occupation "Environmental Restoration Planners" has an automation risk estimated at 49.1%, which is only slightly lower than the base risk of 50.0% for comparable roles. This risk stems largely from the fact that many tasks within this field are highly structured and can be systematized through software and AI-driven solutions. For example, the tasks of developing environmental restoration project schedules and budgets are increasingly being managed by advanced project management software that can optimize timelines and costs with minimal human input. Similarly, providing technical direction on environmental planning can be partially automated by centralized knowledge bases and collaboration platforms that consolidate expert recommendations and best practices. Additionally, creating habitat management or restoration plans, such as native tree restoration or weed control, often involves data-intensive work that can be efficiently handled by algorithms analyzing environmental data sets. However, not all aspects of this occupation are equally susceptible to automation, which helps keep the overall risk below the base rate. Some of the most resistant tasks require a higher degree of specialized knowledge and adaptability. For instance, developing environmental management or restoration plans for complex sites—like those with power transmission lines or renewable energy infrastructure—demands a nuanced understanding of both engineering and environmental science, as well as the unique characteristics of each location. Creating diagrams to effectively communicate remediation planning using GIS or CAD tools involves not just technical proficiency but also creative judgment to convey information clearly and persuasively. Furthermore, generating environmental models or simulations using GIS data and deep ecosystem knowledge relies heavily on systems thinking and the ability to interpret and synthesize data within ever-changing natural contexts. The bottleneck skills preventing full automation of this occupation are primarily tied to originality, which remains a mostly human trait; the occupation’s assessed levels for originality are 3.1% and 3.9%. These low percentages underscore the importance of creative problem-solving and the generation of novel strategies, especially in unstandardized or unpredictable situations. While AI can process and propose solutions based on existing data and patterns, it still struggles with generating innovative ideas and adjusting plans in response to unforeseen ecological changes or regulatory shifts. As environmental restoration often involves unique challenges for each project, the value of professionals who can think creatively, synthesize interdisciplinary knowledge, and adapt dynamically ensures that at least half of the role’s core functions stay resistant to automation for the foreseeable future.