Extreme heat poses a growing threat to human populations, with numerous implications for public health, economic stability, and quality of life [1
]. Past heat waves have had devastating, deadly outcomes worldwide [4
], and such events are expected to increase in intensity, frequency, and duration as climate change progresses [7
]. Although human settlements of any type may experience the negative effects of extreme heat, these are and will continue to be most pronounced in urban areas, the development practices of which are highly correlated with rising temperatures [9
]. Currently, more than 50% of the world’s population is located in urban areas, and that figure is expected to reach over 66% by 2050 [12
]; with so many people potentially at risk of exposure, it is imperative that local governments and planning practitioners recognize varying degrees of vulnerability among urban residents.
Urban heat events—defined as those above the 90th percentile of historic temperatures [13
]—are an environmental stressor, placing economic, infrastructure, and human health burdens on society [14
]. As a stressor, urban heat can create vulnerabilities, which may be understood as a combination of three factors [17
]: exposure, sensitivity, and adaptive capacity. Exposure
refers to an individual’s contact with a stressor, either from living, working, or spending time in an affected location. Sensitivity
is the point at which exposure becomes dangerous to an individual’s health [18
]. Finally, adaptive capacity
refers to one’s ability to change exposure or sensitivity, or to cope with an extreme event. Regarding urban heat, indicators believed to enhance adaptive capacity include high income, social cohesion, and knowledge of hazardous environments [19
]. Given these conditions, it may be reasonable to categorize extreme heat exposure as an environmental justice issue.
The phenomenon central to this study is the urban heat island (UHI) effect, which has been known to researchers since the mid-19th century, and indicates a strong correlation between urban environments and high temperatures [21
]. Impervious surfaces and anthropogenic activity within cities portend rising temperatures, as does the relative scarcity of heat-ameliorating elements such as trees and grasses [23
]. A higher frequency of regional extreme heat events, as such, will amplify temperatures [8
], and generate UHI in areas that have greater amounts of heat absorbing surfaces. While early studies focused on the comparative temperatures between urban and non-urban regions, the emergence of mobile sensors, highly accurate global positioning systems, and computational software allows for the comparison of intra-urban spaces, measuring variation in temperature distribution across a single city [26
]. Past research has utilized infrared satellite data for this purpose [10
], though vehicle-based traverse measurements (used in this study) can provide a detailed representation of heat exposure at a smaller scale [30
From an environmental justice point of view, the existing research emphasizes point source pollution [34
], though it has become clear that climate change affects communities differentially and creates novel impacts never before witnessed in traditional environmental research. As such, we argue that climate change is catalyst for injustice. While some of the effects can be easily observed at the national level, particularly in the world’s poorest countries [37
], there is relatively little understanding of the impact at a more granular scale. Needed are approaches—methodological, conceptual, and pragmatic—that help us to identify those communities disproportionately affected by UHI, and strategies that can help to reduce vulnerabilities. This study provides new evidence of disproportionate exposure to climate change at a local level, as well as access to refuge, an understudied facet of adaptive capacity.
Previous studies indicate a relationship between socio-demographic factors and heat-related morbidity/mortality [39
]. Income is quite predictive of vulnerability (inverse relationship) [41
], though it is possible that other indicators also play a role. If so, urban heat exposure may be framed as an environmental justice or ‘climate justice’ [44
] issue, disproportionately affecting marginalized socio-demographic groups with limited adaptive capacity. This study aims to identify such populations in Portland, Oregon by assessing (1) disproportionate heat exposure
among socio-demographic groups; and (2) disproportionate access to refuge (either public refuge facilities or residential central air conditioning), resulting in heightened or lowered adaptive capacity
. An in-depth statistical and spatial analysis will reveal significant, inequitable relationships, validating the application of an environmental justice lens in addressing urban heat resilience.
This study examined socio-demographic factors in relation to the distribution of urban heat in an attempt to better understand vulnerability based on (1) disproportionate heat exposure among socio-demographic groups; and (2) disproportionate access to refuge (either public cooling facilities or residential central air conditioning), resulting in heightened or lowered adaptive capacity. Overall, results indicate that populations with low adaptive capacity characteristics also experience high exposure, and that access to refuge is significantly influenced by socio-demographic status.
A series of Student’s t-tests were performed to test the hypotheses that the difference between “high” and “low” adaptive capacity groups were significantly greater than 0. The results of the study, with the exception of the variable isolated elderly, allow rejection of the null hypothesis, and indicate that populations with characteristics of low adaptive capacity do experience higher temperatures than those with high adaptive capacity within the study area. Additionally, the analysis showed significantly higher temperatures in the area directly surrounding affordable housing when compared to a random sample of non-affordable (i.e., regular) housing from similar block groups.
We focused on isolated elderly specifically because they have historically been disproportionately impacted by heat waves in other parts of the U.S. [39
], though similar patterns are not statistically significant in the City of Portland. In fact, the observed non-significance of the isolated elderly (percent of the population 65+ years old and who live alone) could be related to the spatial nature of the census block group geographies. A test for spatial autocorrelation conducted using Moran’s I [56
] showed that, while census block groups with a high percent of isolated elderly have statistically significant clustering (z-score = 2.921, where 0 is random; p
-value = 0.0035), they are far more random in spatial distribution than the variables for extreme poverty (z-score = 6.411; p
-value ≈ 0), high racial diversity (z-score = 15.475; p
-value ≈ 0), poor English skills (z-score = 15.673; p
-value ≈ 0), and low education (z-score = 17.787; p
-value ≈ 0). This notable difference in spatial autocorrelation shows that block groups with high levels of isolated elderly populations are more randomly distributed than the other socio-demographic variables, thus increasing the chances that they will have a more randomized exposure to extreme heat and a less significant Student’s t
The accessibility analysis revealed that walking speeds, as they relate to the distribution of cooling centers, greatly affect the percentage of areas in the city having access to heat refuge. At the slower walking speed (1.4 km per hour), only 3.4% of residents have access; at the average speed (3.5 km per hour), the percentage increases to 16.9%; and at the fast speed (5.6 km per hour), it increases to 32.7%. This finding reveals that even in the best case scenario (fast speed), less than one third of the population can access a public heat refuge. This may be especially meaningful for individuals with mobility challenges, such as those using wheelchairs, those with pre-existing health conditions, and bedridden patients, though such groups have not been included in this study.
The covariance analysis found racial and age-related disparities in distribution of UHI, CAC, and walkable access to heat refuges. Risk factors concentrate on some socio-demographic groups, especially young children. They are more likely to live in census blocks which are hotter during urban heat events, and with a smaller number of CAC units. In contrast, white populations tend to live in census blocks with less UHI effect, and more CAC units. Black/African American populations tend to have better accessibility to public heat refuges, which may prove helpful if they are concentrated in high-heat census block groups. While analyses focusing on environmental justice have found that non-white communities are disproportionately living near point sources, urban heat and the access to refuge arguably represent novel concern that may further deepen the inequities in society.
This study does not offer a complete exploration of the factors which determine why certain socio-demographic groups cluster in areas experiencing higher temperatures. This is a complex question that would require a complete study of its own, though some of the likely contributing factors are known to researchers. Urban development patterns often feature lower rents in areas near large roads and buildings [57
], both of which can amplify urban heat effects. Assuming individuals with limited financial means seek out lower rent, this increases their likelihood of locating in areas with higher heat stress. A second possible factor relates to socialization and, in some cases, spatial isolation of minority communities. Such groups have a history of building social capital by co-locating in neighborhoods, as well as being coercively isolated in specific locations [58
]; this could result in apparently heightened heat exposure for such groups, simply due to their proximity. In the case of Portland, local development practices have typically placed large trees and other heat-ameliorating features in higher-income neighborhoods [61
], exacerbating heat exposure of low-income and minority communities who have historically been excluded from these areas. These are multifaceted relationships that differ across cities, and are outside the purview of this study. However, it is useful to consider the underlying causes of physical clustering and resulting exposure.
One major drawback to the analyses in this study is the geographic format of the data. The irregular polygon geometry of the census block group data relies on areal aggregation to protect the anonymity of individuals; this aggregation of population and heat data into enumeration units can ‘smooth’ the dataset, eliminating extreme highs and lows in the process of representing the data with a single mean value. This complication is difficult to avoid, as the block group geometries employed in this study are the highest resolution datasets available with the required socio-demographic information. Additionally, the Modifiable Areal Unit Problem (MAUP) may introduce error when using enumeration units such as census block groups [62
]. A potential alternative to this census-based study would be to create an entirely new survey of randomly sampled households in the region. This potential new study could allow for a building-level analysis similar the one performed here for affordable housing, but for all socio-demographic variables. A survey with a sample size high enough for statistically sound inference and analysis would be time consuming and costly, however it could potentially reveal more accurate or meaningful results.
This study is also lacking in a key piece of information which would provide a more complete understanding of vulnerability; though exposure and adaptive capacity have been well explored, sensitivity has not, mainly because reliable data on health, genetics, and lifestyle choices are difficult to obtain. For this reason, the definition of vulnerable populations may not be fully accurate because we do not accurately know whether individuals do not, in fact, have access to other forms of refuge (e.g., ductless heat pump, swimming pool, alternative residences, etc.). At the same time, at the population level, the present study finds significant associations between high exposure and low adaptive capacity, which provide meaningful direction for decision makers to prioritize those areas and groups that are likely to be at high risk.
Next Steps for Practitioners
The results of this study may serve as a guide for practitioners in Portland, Oregon, directing attention to those areas of the city most at risk of extreme heat exposure. However, socio-demographic indicators can only reveal general characteristics of a population; as such, community engagement in these priority areas will be a key strategy moving forward. These results suggest that practitioners will need to meet with community members directly to better understand what they experience during a heat wave, how they adapt, and what they perceive their needs and strengths to be. Rather than offering strictly external monetary or technological support, sustainable solutions may be reached by working with local organizations and individuals to build internal capacity.
Given the diverse nature of marginalized groups exposed to extreme heat, it will be helpful for the City of Portland and Multnomah County to release heat-related materials for such an audience. Information regarding public refuges and heat safety, as well as heat wave warnings should be issued in multiple languages and formats (print, online). Messaging tailored to specific groups may also be helpful. It is further recommended that government agencies work with community organizations to disseminate information and provide refuge, as marginalized populations may be wary of government programs. Although this particular study pertains to Portland, the development of inclusive materials and interventions is a best practice for all cities.