Environmental Science & Policy 66 (2016) 366–374

Contents lists available at ScienceDirect

Environmental Science & Policy
journal homepage: www.elsevier.com/locate/envsci

The social and spatial distribution of temperature-related health
impacts from urban heat island reduction policies
Jason Vargo, Ph.Da,b,* , Brian Stone, Ph.Dc, Dana Habeebc , Peng Liu, Ph.Dd ,
Armistead Russell, Ph.Dd
a

University of Wisconsin-Madison, Global Health Institute, 1070 Medical Sciences Center, 1300 University Avenue, Madison, WI, 53706, United States
University of Wisconsin-Madison, Nelson Institute for Environmental Studies, Center for Sustainability and the Global Environment, United States
Georgia Institute of Technology, School of City and Regional Planning, Atlanta, GA, United States
d
Georgia Institute of Technology, School of Civil and Environmental Engineering, Atlanta, GA, United States
b
c

A R T I C L E I N F O

Article history:
Received 1 February 2016
Received in revised form 19 August 2016
Accepted 24 August 2016
Available online 7 September 2016
Keywords:
Urban heat island
Climate change
Urban adaptation
Green area ratio
Heat-related mortality
Climate justice

A B S T R A C T

Cities are developing innovative strategies to combat climate change but there remains little knowledge
of the winners and losers from climate-adaptive land use planning and design. We examine the
distribution of health benefits associated with land use policies designed to increase vegetation and
surface reflectivity in three US metropolitan areas: Atlanta, GA, Philadelphia, PA, and Phoenix, AZ.
Projections of population and land cover at the census tract scale were combined with climate models for
the year 2050 at 4 km  4 km resolution to produce future summer temperatures which were input into a
comparative risk assessment framework for the temperature-mortality relationship. The findings
suggest disparities in the effectiveness of urban heat management strategies by age, income, and race. We
conclude that, to be most protective of human health, urban heat management must prioritize areas of
greatest population vulnerability.
ã 2016 Elsevier Ltd. All rights reserved.

1. Introduction
Cities, given their concentrated populations, outsized contributions to carbon emissions, and impacts on surface energy
balances, are crucial sites for addressing climate change and
avoiding associated health impacts. Urbanized regions produce the
majority of greenhouse gas emissions, and are the places most
vulnerable to human health impacts resulting from climate change
due to concentrated poverty and inequality (Bulkeley and Betsill,
2005; Revi et al., 2014). Urban environments further elevate the
rate of warming in cities through the urban heat island effect, a
phenomenon where the impervious materials of urban construction absorb, store, and release heat energy. Urban warming has
been shown in large US cities to be as great, or greater than, the
impact of global climate change on local temperature trends
(Georgescu et al., 2014; Stone et al., 2012).
While municipal governments are taking actions on climate
change, there remains scarce evidence for how the potential health

* Corresponding author at: University of Wisconsin-Madison, Global Health
Institute, 1070 Medical Sciences Center, 1300 University Avenue, Madison, WI,
53706, United States.
E-mail address: javargo@wisc.edu (J. Vargo).
http://dx.doi.org/10.1016/j.envsci.2016.08.012
1462-9011/ã 2016 Elsevier Ltd. All rights reserved.

benefits of climate-adaptive land use planning and design (or
urban heat management) — additional urban vegetation, reduced
impervious surface, and increased reflectivity of built surfaces —
are distributed. In this work we assess the distribution of
environmental health benefits from alternative climate adaptation
scenarios in Atlanta, GA, Philadelphia, PA, and Phoenix, AZ. These
planning scenarios involve urban land cover changes designed to
increase the spatial extent of vegetation and cool building and
paving materials across these metropolitan areas by 2050. Using
the combination of high resolution output from a regional climate
model the Weather Research and Forecast (WRF) model and a
health modeling software
USEPA’s Benefits Mapping and
Analysis Program (BenMAP)
temperature-related changes in
mortality resulting from alternative development scenarios were
modeled. Here, we evaluate the distribution of resulting health
benefits. Specifically, we test the effectiveness of planning
strategies to lower summer ambient temperatures and thereby
reduce mortality for different races, ages, and income levels in the
three metropolitan areas.

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367

1.1. Planning actions to cool cities

1.2. Vulnerability to heat

Some cities are experiencing a higher rate of warming than
proximate rural areas, with recent work finding the frequency,
intensity, and duration of heat waves to be increasing rapidly in
many large cities (Habeeb et al., 2015). Results of studies
comparing urban and rural warming trends demonstrate that
many large cities are warming at a rate more than double that of
the planet as a whole (Fujibe, 2009; Ren et al., 2007; Stone, 2007).
With the incidence of extreme heat constituting a leading climaterelated threat to human health (Confalonieri et al., 2007), the
higher rates of warming in cities poses a substantial challenge to
municipalities and public officials at all levels of government
(Stone et al., 2012). Four specific changes in urban environments
drive the urban heat island effect: 1) the loss of natural vegetation,
2) the introduction of urban construction materials that are more
efficient at absorbing and storing thermal energy, 3) the creation of
“urban canyons” which trap solar radiation and reduce air flow,
and, 4) the emission of waste heat from buildings and vehicles
(Arnfield, 2003; Oke, 1982; Rizwan et al., 2008). As this elevated
rate of warming in large cities is directly influenced by their own
patterns of land use, climate adaptive land use planning provides a
potential strategy for local action to mitigate extreme heat and its
impacts.
The benefits of increased urban vegetation, through tree
planting, increased park space, and the construction of green
roofs, has been demonstrated in a large number of modeling
studies (Akbari, 2005; Dimoudi and Nikolopoulou, 2003; Taha
et al., 1997). In one such study, tree planting alone reduced summer
afternoon temperatures by as much as 1.5 C, diminishing the
region’s average summer heat island by more than half (Rosenfeld
et al., 1998). Similarly, studies modeling the large-scale conversions of urban surfaces to higher albedo materials show reductions
in afternoon temperatures. Numerous studies on the Los Angeles
basin find that extensive albedo enhancement of building rooftops
and paved surfaces could result in a reduction in afternoon
summer temperatures of between 1.5  C and 2  C (Rosenfeld et al.,
1998; Sailor, 1995; Taha et al., 1997). In a study of Athens, Greece,
climate models parameterizing citywide roofing with baseline
(0.18), moderate (0.63), and extreme (0.85) albedos demonstrated
a reduction in ambient temperatures of between 1 and 2  C, with
results varying by neighborhood (Synnefa et al., 2008).
Previous analysis of the simulations informing this work
showed additional tree canopy and green roofs in Atlanta,
Philadelphia, and Phoenix lowered summer temperatures (Stone
et al., 2014). Such greening strategies alone in Atlanta, for example,
offset projected mid-century warming by about 30% across the
metropolitan region as a whole, with greater reductions resulting
in specific areas. Albedo enhancement in Atlanta, Philadelphia, and
Phoenix offset mid-century projections of the increases in warm
season temperatures by about 11–20% at the metropolitan level
(Stone et al., 2014).
In this work we assess the distribution of health benefits
resulting from policies aimed at lowering urban summer temperatures by increasing vegetative cover or increasing surface albedo.
Underpinning our analysis are simulated changes in vegetative
cover over time; the result of a “green area ratio” (GAR) policy in
each metropolitan region. A longstanding zoning tool in Germany,
and more recently adopted in Seattle and Washington, DC, GARs
specify minimum vegetative cover requirements for privately
owned property, but provide wide flexibility in meeting these
cover standards. GARs identify a menu of greening options from
which property owners can choose, including tree planting and
green roofs, among other greening strategies and allow property
owners to combine multiple strategies to meet the minimum
requirements.

Lowered summer temperatures are expected to result in lower
overall mortality. In general, the temperature-mortality relationship for a given location follows a u-shaped curve with steep
increases in the relative risk of mortality at severe high temperatures (Curriero et al., 2002; Gasparrini et al., 2015). Several factors
determine the effectiveness of planning strategies to protect
human health from heat. Implementing municipal policies
designed to increase vegetation and/or the use of cool materials
have implications for potential cooling benefits which may be
unequally distributed. An important factor in determining a
policy’s effectiveness to reduce heat mortality depends on
characteristics of the population affected by the land cover, and
resulting temperature, changes.
Population vulnerability to heat-related mortality has been
found to vary greatly by metropolitan region in the United States,
with several explanations including differences in the biophysical
ability of populations (Bonner et al., 1976; Senay et al., 1976),
cultural practices, and the character of physical and technical
infrastructures such as air conditioning (Greenberg et al., 1983). Air
conditioning is among the most effective adaptation strategies but
remains one of the most costly, particularly when individuals are
directly responsible for costs through utility expenses (The Royal
Society Policy Centre 2014). Architects and planners have begun to
factor heat relief into designs (Davis et al., 2003; Santamouris and
Kolokotsa, 2013), but air conditioning remains a heavily reliedupon and immediate means of protecting human health from
thermal hazards (Bouchama et al., 2007; Davis et al., 2002;
Semenza et al., 1996).
Individual factors such as age and pre-existing health conditions also affect physiological ability to adapt to high temperatures. Advanced age reduces the function of the body’s
thermoregulatory system (Flynn et al., 2005; Grundy, 2006). Older
individuals, particularly those over the age of 65, are recognized to
have a higher risk and in some instances elevated mortality is
observed among populations less than 1 year of age following heat
waves (Basu and Samet, 2002).
Income also is an important correlate of adverse health
outcomes during periods of extreme heat (Madrigano et al.,
2015). Poorer populations experience more ill effects of heat waves
(Jones, 1982; Martinez, 1989), have less neighborhood vegetative
resources (Harlan et al., 2006), and reside in more urban inner
cities with elevated temperatures (Applegate et al., 1981; Martinez,
1989).
Race had been shown to be associated with increased heat
mortality as it was correlated with urban living and poverty
(Applegate et al., 1981; Jones 1982). More recent studies have not
found an association between health impacts of heat and race, and
the findings on race and heat-related health effects have been
inconsistent (Basu and Samet, 2002; Kovats and Hajat, 2008),
perhaps suggesting that findings manifest as the product of spatial
arrangement in certain urban areas (Martinez, 1989; Smoyer,
1998). Cultural adaptations related to racially-defined groups,
however, have been suggested to be important for influencing
health outcomes during extreme heat events (Whitman et al.,
1997). Race and income are common proxies for other factors
including social isolation (Semenza, 1999), poor housing quality
(Uejio et al., 2011), and lack of air conditioning (Bouchama et al.,
2007) known to increase risks of extreme heat.
While the potential for vegetation and albedo enhancement to
lower ambient summer temperatures is well supported by the
technical literature, only a handful of studies have examined the
direct health-related implications of these approaches to climate
management, and none to our knowledge has addressed the likely
demographic distribution of potential benefits.

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J. Vargo et al. / Environmental Science & Policy 66 (2016) 366–374

2. Methods

2.4. Simulated future climate

To describe the distribution of benefits from climate adaptive land
use planning and design strategies we rely on modeled estimates of
mortality in the year 2050. Much of the work was performed as part
of the Climate, Urban Land use and Excess Mortality (CULE) study
sponsored by the US Centers for Disease Control and Prevention.
Here, we briefly review the methods employed through the CULE
project to associate land cover change with future climate and heatrelated morality in Atlanta, Philadelphia, and Phoenix by 2050.
Earlier analyses and published methodologies discuss the specifics of
projecting future land cover (Vargo et al., 2013), refining climate
models (Liu et al., 2012), and estimating health impacts of
temperature changes (Stone et al., 2014).

The underlying weather for the year 2050 (May-September)
was simulated using the Weather Research and Forecasting (WRF)
model. Additional model details can be found in previously
published CULE documentation (Liu et al., 2012; Stone et al., 2014;
Trail et al., 2013). Simulations for BAU, ALBEDO, GREEN, and
COMBINED scenarios produced hourly temperature and humidity
metrics at a 4 km spatial resolution. Daily minimum temperatures
for each scenario were averaged for the entire summer and
compared against BAU (Medina-Ramón and Schwartz, 2007;
Voorhees et al., 2011). Daily average temperatures were used to
identify heat waves
2 or more consecutive days with values
above the 95th percentile of 1987–2005 summer average in each
scenario (Brooke Anderson and Bell, 2011).

2.1. Population
2.5. Health impacts modeling
Underlying population estimates come from the economic
forecasting firm Woods & Poole and were extended to 2050 using
linear regression of the county-level trends between 1990 and
2040, with the census tract’s percentage of the total county
population in the year 2000 held constant to arrive at 2050
estimates.
2.2. Land cover
Per capita rates of land conversion were empirically estimated
previously by combining US Census population numbers and
temporally proximate land cover change details available from the
National Land Cover Dataset (NLCD) 1992/2001 Retrofit Land Cover
Change Product (Fry et al., 2009). Population projections were
considered with land cover conversion rates to produce per capita
land conversion rates and estimates of seven (7) land cover classes
within each tract for the year 2050. For each metropolitan area in
the study, this estimate provided the ‘Business As Usual’ (BAU)
land cover, to which we applied policies to manage urban heat
(Stone et al., 2014; Vargo et al., 2013).
2.3. Mitigation policies
As part of previous work, 2050 BAU land cover at the tract scale
was manipulated to 1) increase reflectivity of impervious surfaces,
2) increase the amount of vegetation, or 3) do both in combination.
In the ‘ALBEDO’ scenario, we simulated the effects of policies
designed to enhance the albedo of all surface paving from 0.15 to
0.45–and roofing from 0.15 to 0.9–throughout each metropolitan
area. Modified albedo values were obtained from those associated
with commercial cool roofing and paving products available today
(Wan and Hien, 2012).
Simulated new vegetation was then added to each census tract
under the ‘GREEN’ scenario according to policies requiring private
non-residential, private residential, and public parcel areas to meet
minimum green area standards by zoning class. Private, nonresidential parcels were first required to convert roof areas to grass,
and then to add additional green material until achieving a
minimum green area of 50%. Residential parcels and public lands
were required to meet an 80% green area standard, which was
achieved through variable combinations of street tree planting,
parking lot conversions to tree canopy, and residential roof
conversions to green roofs.
In a third scenario the ALBEDO and GREEN strategies were
modeled in combination by applying the green area standard first
and then increasing the albedo of any remaining impervious
surfaces. This final climate simulation is referred to as the
‘COMBINED’ scenario. Additional details can be found in Table 1
of Stone et al., 2014.

Differences between BAU and other scenarios, in terms of both
average summer temperatures and number of heat waves, will
produce differences in mortality relative to the BAU 2050 summer.
Earlier studies using the US EPA’s Benefits Mapping and Analysis
Program (BenMAP) combined information on weather, population,
and baseline mortality to estimate changes in health outcomes
between scenarios. The resultant number of avoided (or increased)
deaths in each tract was estimated for three scenarios depicting
urban heat management policies. Consistent with underlying
health studies, BenMAP scales annual mortality to approximate
daily changes to health outcomes. These results are then
aggregated for the period of interest, in this case summer months.
This measure of “avoided mortality” is the difference in deaths
expected to occur in 2050 under BAU and the alternative (ALBEDO,
GREEN, and COMBINED) scenario due to changing summer
temperatures. The result of this computation is an estimate of
the health benefit, or heat-related deaths that can be avoided, due
to the implementation of vegetation and/or albedo enhancement
strategies region-wide.
These methodologies used epidemiological studies to define
associations between summer temperatures and changes in
baseline mortality (the number of deaths from all causes, as
defined by the pertinent epidemiological studies), which were age
and county specific. Based on those studies, mortality was
expected to increase by 3.74% for each additional summer heat
wave day (Anderson and Bell, 2011), and by 0.43% for each 1  C
increase in average summer minimum daily temperature (MedinaRamón and Schwartz, 2007).
Results from previous CULE work including population
projections and avoided mortality estimates, were compiled and
processed using R Statistical Software version 3.0.3 (R Foundation
for Statistical Computing, 2014).
2.6. Vulnerability factors
In this work we assess the distribution of health benefits from
urban heat management policies using the population distribution
data stratified by age, race, and income. Tracts are classified based
on median income, median age, and majority race (white or nonwhite). Three classes of income and age are selected and
breakpoints identified such that each grouping contains more
than one tract in all metro areas.
2.7. Health outcomes
BenMAP provides modeled estimates for changes in avoided
mortality resulting from our land cover-based urban heat
management strategies. BenMAP estimates a total for the change

J. Vargo et al. / Environmental Science & Policy 66 (2016) 366–374

in mortality from daily estimates for summer months. In each tract,
we combined BenMAP’s estimates of total tract population and the
number of avoided deaths to calculate avoided mortality rates
(avoided deaths/100,000 persons). We reduced the number of
outliers by removing tracts with fewer than 500 people and those
with avoided mortality rates more than five standard deviations
from the mean avoided mortality rate for all tracts and scenarios.
Rates for avoided mortality represent a standardized effectiveness of each heat management strategy holding population
constant. Using population-normalized rates allows us to make
comparisons between different locations within a metropolitan
area, as well as between different metropolitan areas. Avoided
mortality rates were inspected through visual inspection of Q–Q
plots. Average rates were computed for each category with 95%
confidence intervals, which are used to compare group means
within and across metros.
Local climate affects avoided mortality rates through its
influence on regional humidity, air movements, and dominant
vegetation types. Historic land use also affects avoided mortality
rates by determining the amount of existing vegetation and thus
the application of land cover change decision rules. Finally,
population characteristics influence avoided mortality rates.
Modeled avoided mortality is responsive to some of these
characteristics through variability in baseline mortality and
population age captured in the underlying data.

369

3. Results
3.1. Population
The projected 2050 distributions of income, race, and age are
presented for each metro area in Fig. 1. Colors are arranged such
that the category with anticipated higher vulnerability is shown in
darker shades. The direction of this scheme is informed by the
literature on extreme heat risk (Kovats and Hajat, 2008). In 2050
the Atlanta, Philadelphia, and Phoenix metros were projected to be
home to roughly 9.1 M, 6.0 M, and 7.6 M people, respectively. A
majority of the growth in areas is expected to occur beyond the
fringes of the historic Central Business District (CBD) (Stone et al.,
2014). After processing data and applying criteria to remove
outliers, more than 96% of the original tracts remained included in
the analysis.
3.1.1. Age
The metro areas differ with respect to their demographic
compositions and spatial distributions. Tracts with higher median
age are likely to contain more people considered vulnerable to
extreme heat because of their age. The median age represents the
midpoint of all people’s ages for a given tract, thus higher median
age tracts have larger populations of older people. The large
confidence intervals around estimates for older tracts limited our

Fig. 1. Projected 2050 distribution of income, race, and age (with urban core tracts shown) for Atlanta, GA, Philadelphia, PA, and Phoenix, AZ.

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J. Vargo et al. / Environmental Science & Policy 66 (2016) 366–374

Median Age

A
Atlanta

Philadelphia

Phoenix

1.5

1.0

Average Avoided Mortality Rate (deaths/100,000)

0.5

0.0
under 30

30 - 44

45+

under 30

30 - 44

45+

under 30

30 - 44

45+

Median Income

B
Atlanta

Phoenix

Philadelphia

1.25

1.00

0.75

0.50

0.25

0.00
below $30K $30 - $75K

$75K+

below $30K $30 - $75K

$75K+

below $30K $30 - $75K

$75K+

Majority Race

C
Atlanta

Phoenix

Philadelphia

0.75

0.50

0.25

0.0
white

non white

white
ALBEDO

non white
GREEN

white

non white

COMBINED

Fig. 2. Average avoided mortality rates by vulnerability factor ((A) age, (B) income, (C) race) and scenario in Atlanta, GA, Philadelphia, PA, and Phoenix, AZ.

J. Vargo et al. / Environmental Science & Policy 66 (2016) 366–374

ability to draw strong conclusions about differences with these
tracts. In general younger tracts are found closer to central city
urban cores (Fig. 1). Phoenix will be both the oldest and youngest
metro by fraction of its total population, with 8.7% of its population
living in tracts with a median age over 44, and 34.8% living in tracts
with median age under 30. Atlanta will have less than 1% of its total
metro population in 45 and over tracts; 11.5% will live in under 30
tracts. Philadelphia will have nearly 5% of the metro population in
the oldest and youngest categories of tracts while Phoenix’s
projected age distribution is much wider.
3.1.2. Income
Lower income populations and tracts are expected to
experience increased vulnerability to heat because of limited
adaptive capacity to deal with this environmental exposure.
Phoenix is projected to have the largest share of its population
living in tracts with a median annual income below $30,000
(15.5%), and the smallest share in tracts with median income
above $75,000 (11.6%). Only 4.5% of Atlanta’s residents are
expected to live in tracts with median income below $30,000.
Over 10% of Philadelphia’s projected population lives in tracts
with the lowest median income, with a much higher share of the
metro population in more affluent tracts (22.1%); nearly double
that of Phoenix. In all three metros, pockets of low-income tracts
in and around the city center are expected. In Phoenix there are
also large tracts with concentrated poverty in the southern
portions of the metro area.
3.1.3. Race
Race and ethnicity are often discussed in works studying heat
wave and health risk factors due to their ability to capture variation
in poverty, educational attainment, and social isolation (O’Neill
et al., 2005; Uejio et al., 2011; Whitman et al., 1997). All three
metros are expected to have more majority white tracts than
majority non-white; however, Philadelphia will have less of its
population in majority non-white tracts (13.1%) than Atlanta
(22.1%) and Phoenix (26.1%). The patterns of non-white settlement
in the regions also follow noticeable spatial patterns (Fig. 1). In
Atlanta, non-white tracts tend to be to the south of the historic
CBD, clustered in areas that are still relatively central and urban in
character. In Phoenix, non-white populations are dispersed closer
to the fringe of the metro area, away from the city center with
denser more urban character.

3.2. Age and avoided mortality
There are differences in the average avoided mortality rate
between the oldest and youngest tracts in Philadelphia and
Phoenix (Fig. 2, Panel A). In these two metros, for each urban heat
management strategy ALBEDO, GREEN, and COMBINED older
tracts benefited more from the policies in terms of reduced rates of
mortality. A similar trend is observed in Atlanta, but the small
number of tracts with a median age of 45 or older (< 1%) affected
the confidence to distinguish rates in these tracts from others.
In Phoenix’s tracts with median age over 44 there is no
difference in avoided mortality rate between different scenarios.
This implies that cheaper and more easily implemented albedo
strategies
due to their immediate effectiveness and shorter
payback periods (Stone, 2012)
may be prioritized to achieve
health protections. In Philadelphia’s oldest tracts, GREEN and
COMBINED strategies provide higher average avoided mortality
rates than ALBEDO policies alone. In Atlanta, there is no difference
between differently aged tracts, though for the majority of the
metro area the COMBINED strategies are more effective at lowering
rates of mortality than either policy alone.

371

3.3. Income and avoided mortality
In Atlanta, urban heat management strategies reduced mortality rates more in low income than in middle and high income tracts
while the reverse was true between middle and high income
groups in Philadelphia (Fig. 2, Panel B). In Atlanta, the poorest
tracts had an average avoided mortality rate that was 1.5 times
greater than the average rate in middle income tracts when
following ALBEDO policies, 1.9 times greater under GREEN policies,
and 1.8 times greater with both sets of policies implemented. In all
income categories of Atlanta tracts, the rate for COMBINED
strategies was higher than for either policy alone. In Philadelphia,
there was an advantage to GREEN and COMBINED urban heat
management strategies over ALBEDO alone, but not in the poorest
tracts. There were no differences in mean avoided mortality rates
along income categories in Phoenix.
3.4. Race and avoided mortality
The distribution of benefits to different races varied between
metro areas (Fig. 2, Panel C). For Atlanta, urban heat management
strategies tend to benefit majority non-white tracts more than
majority white tracts. This was true for each urban heat
management policy, with the largest difference observed under
the GREEN scenario, where average reduction in mortality rates for
non-white tracts was 42% higher than in majority white tracts. In
Phoenix the reverse was true for all strategies: majority white
tracts tended to benefit more from urban heat management than
non-white tracts. The highest average avoided mortality rates in
Phoenix were under ALBEDO policies, and majority white tracts
enjoyed a rate 27% higher than non-white tracts. The disparity was
even greater under GREEN policies, where average avoided
mortality rates for white tracts were 78% higher than non-white
tracts. In Philadelphia, there were differences between the impacts
of ALBEDO and other policies, but no difference along lines of race
for any specific policy.
4. Discussion
The results of our analysis suggest disparities in the effectiveness of the urban heat management strategies by age, income, and
race with varying results depending on the region and the
strategies pursued. While previous work demonstrates that
climate adaptive land use planning and design can produce
reductions in projected rates of mortality over time (Stone et al.,
2014), this study finds clear differences in the demographic
distribution of these benefits. As temperatures in urbanized areas
continue to rise in response to both global and regional scale
warming phenomena, municipal and regional governments may
require and/or incentivize heat management strategies involving
enhanced vegetative cover and surface reflectivity. For these and
other strategies to maximally benefit population health, it is
necessary that the distribution of characteristics affecting population vulnerability to climate change impacts be considered.
Disparities in avoided mortality rate by age, income, and race
differ across the three metropolitan regions examined through this
study, however some consistencies can be identified. First, for most
metro-scenario combinations, there is clear evidence that tracts
with a higher median age benefit more from physical heat
management strategies than younger portions of the metro areas.
Though the precise mechanisms of these protections must still be
explored further, this is an important finding. It is supportive of
efforts to protect what are typically the populations most at risk for
heat-related illness. Such efforts include targeted physical land
cover interventions, which result in enhanced vegetative cover and
cool materials at the neighborhood scale.

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J. Vargo et al. / Environmental Science & Policy 66 (2016) 366–374

While emergency response plans for extreme heat often focus
on weather emergency information, the siting of cooling centers,
and, in the most extreme cases, evacuation efforts for elderly
populations, our work suggests that built environment interventions designed to lessen the intensity and duration of extreme
temperatures are protective of these populations across a diversity
of urban and climatic settings. Municipal and regional governments should consider broadening conventional emergency
response planning for heat waves to include mitigation strategies
focused on cool land covers in neighborhoods characterized by
large or concentrated elderly populations. Analyses like this one,
which are based on scenario-based climate and health modeling,
shed important light on the effectiveness of specific strategies and
for particular populations (Huang et al., 2011).
Next, heat management strategies focused on increasing
vegetative cover and albedo are most protective of lower income
populations in regions in which these populations are disproportionately located in close proximity to the historic urban core. In
Atlanta, lower income populations benefited more from heat
management strategies than middle and upper income Atlantans.
Tracts containing low-income populations are more concentrated
within high density areas of Atlanta than in Philadelphia or
Phoenix. Phoenix, in particular, provides a strong counterpoint to
Atlanta, with a larger percentage of low income households
concentrated in lower density, suburban census tracts than in close
proximity to the urban core (Fig. 3, Panel D). As a result of this
pattern, heat management strategies
as applied here with
greater impact on modifying highly impervious zones such as
downtown districts characterized by high building densities and
extensive surface parking
may be less effective in Phoenix at
reducing heat risk for populations with limited access to air
conditioning and other resource-dependent protective measures.
The spatial association between income and imperviousness
observed across Atlanta and Philadelphia is even more pronounced
with respect to disparities in race and avoided heat mortality in
these two regions. Under all of the land cover scenarios simulated,
non-white residents benefit more in Atlanta, while white residents
disproportionately benefit in Phoenix. This finding contrasts
previous studies in Phoenix, which demonstrated larger cooling
effects of similar land cover conversions in more impervious
neighborhoods (Gober et al., 2009; Jenerette et al., 2011; Middel

A

B
% impervious

et al., 2014). Our finding that Phoenix’s white residents benefit
more may be due in part to our larger unit of analysis and approach
used in modifying hypothetical land cover. Earlier Phoenix
modeling exercises examined much finer-scale and highly targeted
land cover modifications with the outcome of temperature
reductions. Here, we are testing the efficacy of policies, albeit
policies tied to land cover modifications, applied uniformly across
the region and thus to differing degrees in different locations. Also,
the outcome of a health endpoint is novel and introduces a new
factor impacting the non-linearity of relationships between land
cover modifications and tested outcomes.
Patterns of race and avoided heat mortality are closely
associated with the spatial distribution of non-white populations,
with a large and predominantly African American population
principally situated in the southern reach of the urbanized core in
Atlanta and, to a less concentrated extent, in Philadelphia (Fig. 1).
Atlanta is one US city where a stronger relationship between
weather variables and health impacts among non-whites has been
observed previously (Kalkstein and Davis, 1989). Phoenix has a
higher percentage of non-white population overall with a large
Hispanic population and several majority non-white tracts housing
Reservations for First Nations’ peoples. These tracts are situated to
the south and east of the central business district and characterized
by very low built densities. As a result, heat management strategies
designed to mitigate the heat production of concentrated
imperviousness appear to be less protective of health for that
group.
Our analysis relies on temperature as the sole predictor of a
strategy’s effectiveness. It is important to note that temperature is
only one of several variables important to the human energy
balance (Barnett et al., 2010; Zhang et al., 2014, 2012). Also, our
estimates of avoided summer mortality rely on an annual baseline
mortality scaled to approximate daily deaths. An update and
refinement to the mortality modeling underlying this work could
improve our results by providing more accurate baseline mortality
in summer months, by more precisely assessing the impact of daily
temperature rather than summer averages, and by more closely
defining the mortality-temperature relationship for specific
locations. Certainly regional differences in the temperaturemortality relationship would be expected to affect results (Wu
et al., 2014). Importantly these relationships may also change over

C
% 65 and over

D
% nonwhite

% in poverty (HH <$30K)

60%

40%

20%

0%
Atlanta Philadelphia Phoenix

Atlanta Philadelphia Phoenix

Urban Core Tracts

Atlanta Philadelphia Phoenix

Atlanta Philadelphia Phoenix

Non-core tracts (suburban and rural)

Fig. 3. Average tract composition ((A) impervious surface, (B) age, (C) race, (D) poverty) for urban core and the rest of the metropolitan area in Atlanta, GA, Philadelphia, PA,
and Phoenix, AZ.

J. Vargo et al. / Environmental Science & Policy 66 (2016) 366–374

time as populations adapt to warming conditions within locations.
Studies can make use of improved epidemiological data on heat
and use regional substitutes to approximate future adaptive
capacity of, say northern latitude populations to respond to heat as
current more southerly populations (Gasparrini et al., 2015;
Greene et al., 2011). Finally, the non-parametric nature of some
of these data — specifically Philadelphia’s middle age and income
groups — require larger studies with more observations to reach
more concrete conclusions.
In concert, these findings support the conclusion that to be
maximally beneficial for health, climate-adaptive land use
planning and design strategies must consider, reflect, and respond
to variation in population vulnerability. Where urban thermal
hotspots and population vulnerability spatially overlap, such as in
Atlanta and, to a lesser extent, Philadelphia, physical heat
management strategies designed to enhance neighborhood
vegetation and albedo are expected to produce greater human
health benefits than in other areas. Where urban thermal hotspots
and heat vulnerability are less spatially aligned, as observed in
Phoenix, uniformly-applied physical climate-adaptive strategies
are likely to be less effective for protecting health and may need to
be combined with a wider array of non-physical strategies, such as
heat wave early warning systems, the siting of cooling centers, and
other emergency response measures (Bradford et al., 2015). In
short, the results of our study support the development of regionspecific urban heat management plans responsive to the unique
demographic (age, income, race) and material (urban form, urban
design, climate zone) composition of metropolitan regions. The
public health community, together with urban planners, city
arborists, and developers, have shared interests and responsibilities to respond to extreme heat events not only through emergency
response planning but in the adoption of mitigation strategies
designed to lessen the frequency and intensity of extreme heat at
the urban scale (Rosenzweig et al., 2011).
By building on prior work that focused on climate adaptation
and heat-related mortality, this study examined whether conventional heat mitigation strategies designed to increase the regionwide coverage of green and cool materials disproportionately
benefit some portions of metropolitan regions. The results of this
work suggest that the spatial association between built density and
the regional location of vulnerable populations is a key indicator of
the potential effectiveness of physical heat mitigation strategies in
large urbanized areas. As climate adaptation planning is further
integrated into the routine planning functions of cities, municipal
governments may choose from an array of 1) heat mitigation and 2)
emergency response strategies to lessen the risk of extreme heat to
urban populations. The findings from this work add to a body of
evidence suggesting a combination of these two approaches in
response to the unique physical and demographic characteristics of
a metropolitan area to protect public health against the impacts of
climate change.
Acknowledgements:
This work was supported by the Centers for Disease Control and
Prevention (project #5U01EH000432-02), Climate Change Program: Environmental Impact on Human Health.
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