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Community Sustainability, Resilience, and Preparedness

Sustainability scholarship involves creating, integrating and harnessing new knowledge to protect and improve social and natural systems and their interactions. Communities that depend on these systems can attain more sustainable futures through many paths. Creating and maintaining its economic and environmental health, promoting social equity, and fostering broad-based citizen participation in planning and implementation is crucial to the sustainability of any community.

Combining the approaches of epidemiologists and clinical trial health scientists, we seek to identify the causal links between the actions we take to improve our world and the impacts of those actions. In the Department of Environmental Health and Engineering, our research focuses on building a credible evidence base about the environmental and social impacts of public and private programs.

Research Highlights

Quantifying Causal Mechanisms to Determine How Protected Areas Affect Poverty Through Changes in Ecosystem Services and Infrastructure

To develop effective environmental policies, we must understand the mechanisms through which the policies affect social and environmental outcomes. Unfortunately, empirical evidence about these mechanisms is limited, and little guidance for quantifying them exists. We develop an approach to quantifying the mechanisms through which legal protection of ecosystems affects areas a poverty. We focus on three mechanisms: changes in tourism and recreational services; changes in infrastructure in the form of road networks, health clinics, and schools; and changes in regulating and provisioning ecosystem services and foregone production activities that arise from land-use restrictions.

COPEWELL: A Conceptual Framework and System Dynamics Model for Predicting Community Functioning and Resilience After Disasters

Policy-makers and practitioners have a need to assess community resilience in disasters. Prior efforts conflated resilience with community functioning, combined resistance and recovery (the components of resilience), and relied on a static model for what is inherently a dynamic process. We sought to develop linked conceptual and computational models of community functioning and resilience after a disaster. We developed a system dynamics computational model that predicts community functioning after a disaster. The computational model outputted the time course of community functioning before, during, and after a disaster, which was used to calculate resistance, recovery, and resilience for all US counties.

Associated Faculty

 *Denotes faculty who are accepting PhD students.