Authors: Mark Adams*, USDA Forest Service
Topics: Environmental Justice, Natural Resources, Spatial Analysis & Modeling
Keywords: Environmental Justice, Inclusion, Federal land management, Spatial Analysis
Session Type: Virtual Paper
Start / End Time: 1:30 PM / 2:45 PM
Room: Virtual 6
Presentation File: No File Uploaded
U.S. federal government agencies are directed by presidential executive order (EO 12898, 1994) to avoid imposing disproportionately high adverse environmental and health impacts on low income and minority populations and to ensure equitable access to the public goods that their actions create. A major conceptual challenge in meeting this direction for managers of U.S. federal land agencies, such as the USDA Forest Service, is that there is no singular environmental justice (EJ) "population of concern" with which federal land managers can consistently interact. The specification of a population of concern is dependent both on the scale of the management activity and on the geographic connections between a specific form of land use and its public participants. This research demonstrates a viable GIS spatial analysis method for identifying populations of concern in the context of multiple, spatially distinct management activities: stakeholder outreach and engagement, economic impact monitoring, protecting ecosystem services, and mitigating wildland fire risk. Central to the method is a systematic approach to integrating agency activity data and best available demographic data that maintains fidelity to the significant underlying estimate errors in the demographic data. Specification of an activity's expected scale of impact is critically important to any environmental justice assessment effort because each unique combination of activity type and impact scale yields distinct communities of concern. Though developed in a federal land management context, this method offers a viable geographic approach to researching environmental justice issues affecting U.S. rural populations in general, for which suitable methods are still lacking.