University of Michigan at Ann Arbor
Climate change and health: residential energy-efficiency for comfort and equity
Marie O’Neill, Ph.D.
As the number and severity of extreme weather events increase with the advancement of global climate change, understanding the public health implications and prioritizing adaptation strategies for retrofitting urban infrastructure assumes greater urgency. Architects and urban planners in cold and temperate climates have long recognized the value of weatherizing residential structures to improve wintertime interior comfort and reduce utility costs. However, less understood is the contribution of weatherization efforts to additionally reduce the negative health effects of hot weather extremes and how these efforts might be targeted to lessen health disparities in urban environments. Our broad objective is to reduce temperature-related health disparities in U.S. communities. Using a trans-disciplinary approach, we propose to new knowledge that will help guide health-enhancing residential energy conservation measures. This model will use data from three case-study communities– Detroit, Michigan, Cleveland, Ohio, and Phoenix, Arizona-where important social disparities in temperature-related health outcomes have been documented. Specifically, we propose 1) to update and expand existing maps of vulnerability to extreme temperatures, based on a finer-scale neighborhood level analysis using structural, environmental, socio-economic, and demographic data, and evaluate potential for home weatherization to reduce these vulnerabilities; 2) to model how future climate scenarios and changes in neighborhood conditions might impact the distribution of vulnerability and the interior comfort conditions that residents will experience in these communities; and 3) to share and discuss research results with key stakeholders (e.g. installers, public health officials, community leaders, city officials) to inform urban planning, public health preparedness, community improvement and weatherization programming decisions relevant to vulnerable populations.
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