Heat vulnerability of urban populations is becoming a major issue of concern with climate change, particularly in the cities of the Southwest United States. In this article we discuss the importance of understanding coupled social and technical systems, how they constitute one another, and how they form the conditions and circumstances in which people experience heat. We discuss the particular situation of Los Angeles and Maricopa Counties, their urban form and the electric grid. We show how vulnerable populations are created by virtue of the age and construction of buildings, the morphology of roads and distribution of buildings on the landscape. Further, the regulatory infrastructure of electricity generation and distribution also contributes to creating differential vulnerability. We contribute to a better understanding of the importance of sociotechnical systems. Social infrastructure includes codes, conventions, rules and regulations; technical systems are the hard systems of pipes, wires, buildings, roads, and power plants. These interact to create lock-in that is an obstacle to addressing issues such as urban heat stress in a novel and equitable manner.

This study examines the distributional equity of urban tree canopy (UTC) cover for Baltimore, MD, Los Angeles, CA, New York, NY, Philadelphia, PA, Raleigh, NC, Sacramento, CA, and Washington, D.C. using high spatial resolution land cover data and census data. Data are analyzed at the Census Block Group levels using Spearman’s correlation, ordinary least squares regression (OLS), and a spatial autoregressive model (SAR). Across all cities there is a strong positive correlation between UTC cover and median household income. Negative correlations between race and UTC cover exist in bivariate models for some cities, but they are generally not observed using multivariate regressions that include additional variables on income, education, and housing age. SAR models result in higher r-square values compared to the OLS models across all cities, suggesting that spatial autocorrelation is an important feature of our data. Similarities among cities can be found based on shared characteristics of climate, race/ethnicity, and size. Our findings suggest that a suite of variables, including income, contribute to the distribution of UTC cover. These findings can help target simultaneous strategies for UTC goals and environmental justice concerns.