Double exposure and the climate gap: changing demographics and extreme heat in Ciudad Juárez, Mexico

Abstract

Scholars have recognized a climate gap, wherein poor communities face disproportionate impacts of climate change. Others have noted that climate change and economic globalization may mutually affect a region or social group, leading to double exposure. This paper investigates how current and changing patterns of neighborhood demographics are associated with extreme heat in the border city of Juárez, Mexico. Many Juárez neighborhoods are at-risk to triple exposures, in which residents suffer due to the conjoined effects of the global recession, drug war violence, and extreme heat. Due to impacts of the recession on maquiladora employment and the explosion of drug violence (since 2008), over 75% of neighborhoods experienced decreasing population density between 2000 and 2010 and the average neighborhood saw a 40% increase in the proportion of older adults. Neighborhoods with greater drops in population density and increases in the proportion of older residents over the decade are at significantly higher risk to extreme heat, as are neighborhoods with lower population density and lower levels of education. In this context, triple exposures are associated with a climate gap that most endangers lower socioeconomic status and increasingly older aged populations remaining in neighborhoods from which high proportions of residents have departed.

Urban Heat and Climate Justice: A Landscape of Thermal Inequity in Pinellas County, Florida

“…As this important subfield of environmental justice research continues to develop, a rigorous empirical methodology is required to examine the interconnection among the built urban environment, urban heat, and socio‐demographic characteristics of urban residents. Following recent studies on social inequities in exposure to extreme heat (Grineski and others , ), our article contributes to climate justice research by: focusing on the use of high‐resolution thermal remote sensing of land surface temperature (LST); and implementing geostatistical techniques that explicitly account for spatial dependence in the data.…”

“…Land surface temperature (LST) was chosen as the dependent variable in this study because of its status as a key parameter in urban climate studies, and its positive statistical association with rates of heat‐related morbidity and mortality (Johnson and Wilson ; Johnson and others ; Hondula and others ), and utilization in EJ studies that have considered disparities in the exposure to environmental hazards, including urban heat (Grineski and others , ). LST from two types of remote sensing data were first examined in order to determine whether a UHI pattern was present in the study area.…”

“…Christopher Uejio and his colleagues (2011) analyzed the health impacts of urban heat in both Phoenix and Philadelphia by utilizing LST and impervious‐surface data in conjunction with a generalized linear mixed‐model approach to correct for temporal autocorrelation in the data. Additionally, recent studies in the U.S.–Mexico border cities of El Paso, Texas, and Juárez, Mexico, have examined the spatial and social distribution of urban heat exposure at the neighborhood level (Grineski and others , ; Collins and others ). These studies used LST measures derived from Landsat imagery and spatial regression modeling to correct for spatial autocorrelation, in their analysis of exposure and vulnerability to extreme heat and other climate change related risks.…”

“…Specifically, much of the previous research has not comprehensively assessed the spatial pattern of urban heat within study areas. Additionally, only a few recent studies have used geostatistical techniques to account for spatial dependence in the data (Grineski and others , ; Collins and others ). Our study addresses the methodological limitations of previous work and extends climate justice research through a case study that examines social and spatial inequities in the distribution of urban heat in Pinellas County, Florida.…”

A scalable climate health justice assessment model

“…The environmental justice (EJ) literature has documented that poor and racial/ethnic minority groups often face disproportionate exposure to environmental hazards (Brown, 1995; Brulle & Pellow, 2006; Chakraborty, Maantay, & Brender, 2011; Mohai, Pellow, & Roberts 2009). Within that literature, a growing body of work on the “climate gap” is demonstrating that socially marginalized groups face unequal and disproportionate exposure to climate change-related hazards (Grineski et al, 2014; Grineski et al, 2012; Kersten, Morello-Frosch, Pastor, & Ramos, 2012; Morello-Frosch, Pastor, Sadd, Shonkoff, 2009; Pastor, Morello-Frosch, Sadd, & Scoggins, 2010; Shonkoff, Morello-Frosch, Pastor, & Sadd, 2009; Shonkoff, Morello-Frosch, Pastor, & Sadd, 2011). However, few if any scholars have merged normative environmental justice concerns with quantification of health impacts of climate change.…”