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Who Wins and Loses from “No Net Loss”?: Results from Analysis of Socioeconomic and Participatory Differences in Compensatory Mitigation in New Hampshire

Authors: Simone Chapman*, , Catherine M Ashcraft, University of New Hampshire
Topics: Water Resources and Hydrology, Geographic Information Science and Systems, Natural Resources
Keywords: Compensatory mitigation; environmental justice; GIS; Clean Water Act
Session Type: Paper
Presentation File: No File Uploaded

African American, other minority, and low-income communities are disproportionately exposed and vulnerable to the cumulative, negative impacts of environmental threats, including pollution and degradation of water resources. Executive Order 12898 directs federal agencies to integrate environmental justice (EJ) considerations into policy implementation to promote nondiscrimination. Environmental justice refers to “the fair treatment and meaningful involvement of all people regardless of race, color, sex, national origin, or income with respect to the development, implementation and enforcement of environmental laws, regulations, and policies” (US EPA, 2019). However, very few studies analyze the environmental justice impacts of how the Clean Water Act’s compensatory mitigation policy is implemented. When a development project, such as road widening or building expansion, involves unavoidable activities that impair wetlands regulated under the Clean Water Act and their functions, such as wildlife habitat, and flood storage, the authorizing permit requires mitigation to compensate for the loss of wetland functions and values such that there is “no net loss” of wetland functions and values.

Existing studies have found evidence of systemic resource relocation, in which wetland resources are shifted from more urban, whiter, more highly educated impact sites areas to more rural, less populated, and poorer compensation sites with a higher percentage of minorities.

I will compare the transaction level spatial data for compensatory wetland mitigation with census-tract level socioeconomic data (non-white percentage, population density, educational attainment and household income) to analyze population characteristics. Then seek to evaluate community access to participate in the ARM Fund program.

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