Content uploaded by Sara Meerow
Author content
All content in this area was uploaded by Sara Meerow on Sep 20, 2021
Content may be subject to copyright.
1
Planning for extreme heat: A national survey of U.S. planners
Forthcoming in the Journal of the American Planning Association
Sara Meerow1 and Ladd Keith2
Abstract
Problem, Research Strategy, and Findings: Extreme heat is the deadliest climate hazard in the
United States. Climate change and the urban heat island effect are increasing the number of
dangerously hot days in cities worldwide and the need for communities to plan for extreme heat.
Existing literature on heat planning focuses on heat island mapping and modeling, while few
studies delve into heat planning and governance processes. We surveyed planning professionals
from diverse cities across the U.S. to establish critical baseline information for a growing area of
planning practice and scholarship, which future research can build on. Survey results show that
planners are concerned with extreme heat risks, particularly environmental and public health
impacts from climate change. Planners already report impacts from extreme heat, particularly to
energy and water use, vegetation and wildlife, public health, and quality of life. Especially in
impacted communities, planners claim they address heat in plans and implement heat mitigation
and management strategies such as urban forestry, emergency response, and weatherization, but
perceive many barriers related to human and financial resources and political will.
Takeaway for practice: Planners are concerned about extreme heat, especially in the face of
climate change. They are beginning to address heat through different strategies and plan types,
but we see opportunities to better connect planners to existing heat information sources and
leverage existing planning tools, including vegetation, land use regulations, and building codes
to mitigate risks. While barriers to heat planning persist, including human and capital resources,
planners are uniquely qualified to coordinate communities’ efforts to address the rising threat of
extreme heat.
Keywords: extreme heat, resilience, heat resilience, climate change, urban heat island
Introduction
Extreme heat causes more deaths in the United States (U.S.) than other weather-related
disasters combined (Jones et al., 2021), and cities are getting hotter due to climate change and
the urban heat island (UHI) (Kim et al., 2020). Climate change is increasing global temperatures,
and the UHI, which is caused by the built environment and waste heat, amplifies warming trends
in cities (Stone & Rodgers, 2001). Extreme heat has always been deadly. The 1995 Chicago heat
wave, for example, caused 700 deaths (Davis et al., 2003), over 70,000 are thought to have died
in the 2003 European heat wave (Robine et al., 2008), and hundreds of excess deaths occured
during the 2021 heatwave in the US Pacific Northwest and Canada (Schiermeier, 2021). A global
1 School of Geographical Sciences and Urban Planning, Arizona State University,
Sara.meerow@asu.edu, https://orcid.org/0000-0002-6935-1832
2 School of Landscape Architecture and Planning , The University of Arizona, ladd@arizona.edu
https://orcid.org/0000-0002-5549-0372
2
study suggests that over a third of all heat-related deaths in the last three decades are attributable
to climate change (Vicedo-Cabrera et al., 2021).
In addition to health impacts, extreme heat threatens urban infrastructure, ecosystems,
and economies. Heat impacts are highly inequitable, with marginalized communities
disproportionately impacted due to disparities in infrastructure, vegetation, and social
vulerability (Hoffman et al., 2020; Mitchell & Chakraborty, 2018; Wilson, 2020). Planners play
an important role in shaping urban environments and are well-positioned to address extreme
heat. Yet, the literature on heat planning is limited, and in comparison to other hazards, practical
guidance and regulations related to heat are underdeveloped (Keith et al., 2020). This may be
changing, as shown by high-profile international heat initiatives, such as the Extreme Heat
Resilience Alliance, the appointment of the world’s first Chief Heat Officer in Miami (Pittman,
2021), and the creation of an Office of Heat Response and Mitigation in Phoenix.
This study documents the current state of heat planning across the U.S. through a survey
of planning professionals. We begin by introducing the growing threat of extreme heat and its
relevance for planning. Drawing on recent literature, we argue that little is known about how and
why cities are planning for extreme heat. Our survey helps to fill that gap, showing that
communities across the U.S. are experiencing heat impacts and planners are concerned about
heat risks, especially from climate change. Most communities reportedly address heat in various
plans through both heat mitigation and management strategies, but planners also report many
human and capital resource barriers. Statistical analyses show that planners using more heat
information and exposed to past or future heat are more concerned and implement more heat-
related strategies and plans. This study provides a foundation for future heat planning research
and practice.
The growing threat of extreme heat
Extreme heat is increasing in cities across the U.S. because of climate change and the
UHI effect. Climate change has already increased annual average temperatures over the
contiguous U.S. by 1.8°F (1°C) since 1900, with projected increases of up to 12°F (6.7°C) by
2100 (Hayhoe et al., 2018). Extreme heat events have increased in intensity, frequency, and
duration since the 1950s, with changes accelerating in recent decades (Perkins-Kirkpatrick &
Lewis, 2020). The UHI effect increases temperatures in urban areas compared to rural and
natural areas due to the form and material use in the built environment, anthropogenic heat
waste, and land cover and vegetation changes (Oke, 1982; Oke, 1973; Stone & Rodgers, 2001).
While the magnitude of the UHI effect varies by geography, climate, city size, urban form, and
measurement approach, temperatures in urban areas can be 0.9–7.2°F (0.5-4°C) higher during
the day and 1.8–4.5°F (1-2.5°C) higher at night (Hibbard et al., 2017). We use the term “extreme
heat” to denote both acute and chronic heat risk in cities exacerbated by climate change and the
UHI effect.
Extreme heat has implications across urban systems, including public health, the
economy, urban ecology, water and energy usage, and infrastructure (Turek-hankins et al.,
2021). Heat is the deadliest of weather-related disasters (Hondula et al., 2015). Elevated body
temperatures can cause heat stroke, which if untreated leads to organ failure and death, and heat
can strain the cardiovascular system causing heart failure (Kovats & Hajat, 2008). Gasparrini et
al. (2015) estimated that 0.35% of total mortality in the U.S. is attributable to high temperatures
(approximately 8,750 deaths each year), but a recent study (Shindell et al., 2020) suggests it is
3
closer to 12,000, and predicts annual heat deaths would increase by almost 100,000 by 2100
under high climate change scenarios.
Cases of heat illness requiring formal medical treatment exceed heat-related mortality by
multiple orders of magnitude (Knowlton et al., 2009). Hospital admissions increase for mental
health–related issues by as much as 7.3 percent during extreme heat events (Hansen et al., 2008)
and the risk of suicide also increases (Thompson et al., 2018). Heat increases aggression and
violence (Anderson, 2001) and recent research shows that crime rates increase almost 2% when
temperatures exceed 90°F (32°C) in Los Angeles (Heilmann et al., 2021).
Economic productivity is affected by heat – an estimated 153 billion labor-hours were
lost in 2017 globally (Watts et al., 2018). By 2050, economic productivity is estimated to
decrease by 20% during hot months (Dunne et al., 2013). Extreme heat also erodes the richness
and diversity of urban ecosystems (Brans et al., 2018) and affects plants’ growth and ranges
(Nitschke et al., 2017). Water and energy use increases when it is hotter. A low-temperature
increase of only 1°F (0.6°C) increases single-family water use by about 290 gallons per month
(Guhathakurta & Gober, 2007). An estimated three to eight percent of U.S. electrical demand is
attributed to increased air conditioning from the UHI effect (Grimm et al., 2008).
Heat strains infrastructure too, reducing the reliability and operation of energy
infrastructure (Ward, 2013) and water treatment plants (Zuo et al., 2015). Blackouts during a
heat wave can be disastrous – modeling suggests that in this scenario, most residents in Phoenix,
Atlanta, and Detroit would face an increased risk of heat illness (Stone et al., 2021). Heat
impacts are nevertheless context specific and vary based on day and nighttime temperatures,
humidity, exposure, and acclimation (Turek-hankins et al., 2021; U.S. Environmental Protection
Agency, 2006).
Extreme heat is unevenly distributed across cities and compounds existing risk factors
and systemic inequities (Hsu et al., 2021; Kim et al., 2020; Wilson, 2020). The planning
profession had a role in furthering many of these inequities. For example, formerly redlined
neighborhoods, still largely populated by low income and minority residents, are 4.68°F (2.6°C)
warmer on average due largely to differences in vegetation and patterns of the built environment
(e.g., proximity to major highways) (Hoffman et al., 2020). Even if temperatures were consistent
across neighborhoods, research shows that some individuals are more vulnerable to heat (Putnam
et al., 2018). Populations more likely to experience heat illness or death include the elderly,
children, those who have chronic health conditions, lower incomes, lack air conditioners or
cannot pay to cool their home, are experiencing homelessness, are institutionalized, or work
outside (Hondula et al., 2015; Jones et al., 2021; Kovats & Hajat, 2008).
Planning for heat resilience
In the face of growing risks, cities can plan for heat resilience, or take steps to equitably
prepare for and adapt to extreme heat through both heat mitigation and management strategies
(Keith et al., 2020). Compared to other hazards, such as flooding, the governance of extreme heat
is less established (Hamstead et al., 2020; Koop et al., 2017). One study that catalogued 3,500
online resources for climate adaptation found that only 4% focused on heat, while 21% dealt
with sea-level rise and 14% flooding (Nordgren et al., 2016). Moreover, while heat planning
research is rapidly growing, 60% of papers published between 2013-2018 focused on modeling,
while less than 7% focused on planning processes (Keith et al., 2020). Studies that do examine
heat planning typically focus on a few notable cases, like Phoenix or Los Angeles (Hondula et
al., 2019). Generalizable data on local heat resilience efforts are lacking.
4
While more heat governance is needed, it is unclear who bears primary responsibility
(Guyer et al., 2019; Klok & Kluck, 2018; Mees et al., 2015). Planning professionals have the
opportunity to become heat resilience leaders. First, planners already coordinate across
disciplines, departments, and levels of government, which researchers and practitioners alike
have identified as critical for effective management of extreme heat (Bolitho & Miller, 2017;
Guyer et al., 2019; Hamstead et al., 2020; Mees et al., 2015). Second, the planning profession is
committed to addressing climate change, mitigating hazards, and enhancing community health
(American Planning Association (APA), 2020; Meerow & Woodruff, 2019; Schwab, 2010), and
past and present planning actions directly affect extreme heat today (Stone & Rodgers, 2001;
Wilson, 2020). Third, planners appear concerned about extreme heat. In an all-hazards survey,
planners ranked heat 4th out of 14 possible natural hazards in terms of concern, with 70%
reporting being concerned about extreme heat (APA, 2018). Fourth, while most practicing
planners may not be knowledgable about urban climatology, high-resolution heat hazard and
climate information is increasingly available if planners know where to look (Kim et al., 2020;
US Global Change Research Program, 2016).i
Heat resilience strategies
Numerous strategies have been proposed for enhancing resilience to extreme heat, and
their relative efficacy continues to be researched and debated (Keith et al., 2020; Krayenhoff et
al., 2021; Middel et al., 2020). These strategies fall into two main categories: heat mitigation and
heat management (Figure 1). Heat mitigation strategies are design and planning interventions
aimed at reducing the built environment’s contribution to extreme heat. These include land use
policies, urban design, urban greening, and waste heat. Heat management strategies are efforts to
prepare for and respond to extreme heat, and include strategies related to energy, personal
exposure, public health, and emergency preparedness.
The importance of land use planning strategies for mitigating hazards is well documented
(Burby 1998), but heat has rarely been the focus. In fact, the most influential literature on land
use (e.g. Burby et al., 2000) fails to mention extreme heat. Despite this, the character of the built
environment affects local climates, and conservation of natural areas, development of ventilation
corridors, and urban geometry can have a cooling effect (Lambert-Habib et al., 2013; Stone &
Rodgers, 2001). Urban design strategies refer to site-level elements that mitigate heat, such as
building and street orientation to maximize shade, constructed shade structures, and the use of
‘cool’ or more reflective pavements (Middel et al., 2020; Middel & Schneider, 2021). Whether
through urban forestry, green stormwater infrastructure, green roofs, and parks, increasing
vegetation is a commonly cited heat resilience strategy (Dhalluin & Bozonnet, 2015; Guindon &
Nirupama, 2015). A meta-analysis found that a 10% increase in tree canopy cover yields a
0.54°F (0.3°C) air temperature reduction (Krayenhoff et al., 2021). Vegetation was shown to
universally decreases nighttime temperatures – which is especially important for public health –
in a study of eight U.S. cities, but the reduction was greater in arid cities (Ibsen et al., 2021).
Vegetation does have disadvantages (Roman et al., 2020) and tradeoffs, such as water use
(Gober et al., 2010). Reducing waste heat can also mitigate heat, for example, through reduced
air conditioning and vehicle use, weatherization programs, and increasing the reflectivity of roofs
and other surfaces (Corburn, 2009; Hatvani-Kovacs et al., 2018).
Heat management includes energy-related strategies, such as enhancing energy grid
resilience to avoid power outages during heat events (Stone, Mallen, Rajput, Gronlund, et al.,
2021) and policies to make energy, and thereby cooling, more accessible and affordable (Berisha
5
et al., 2017). Reducing personal exposure involves changes to how infrastructure, facilities, and
workers operate to minimize human exposure to extreme heat (CDC, 2015; Kjellstrom et al.,
2009). Public health interventions for extreme heat include public education and information
campaigns (O’Neill et al., 2009). Emergency preparedness strategies focus on warning systems
for extreme heat, planning and coordinating responses to heat emergencies, and establishing
cooling centers (e.g., libraries, churches) where people can seek shelter and assistance (Berisha
et al., 2017).
A global review of the adaptation literature suggests geographic variation in implemented
heat-related strategies (Turek-hankins et al., 2021). Similar to planning for other hazards, heat
strategies can also be applied across a community’s network of plans, including comprehensive,
hazard mitigation, climate action, resilience, and emergency response plans (Berke et al., 2015;
Yu et al., 2020). The planning profession’s role in public participation for these plans is critical,
as engaging stakeholders in heat-related policies can improve outcomes (Akompab et al., 2013).
Figure 1. Heat resilience strategies framework highlighting the different categories of heat
mitigation and heat management strategies.
Barriers to planning for heat resilience
Case studies of heat planning suggest many barriers exist (Keith et al., 2020). These include a
lack of coordination and regulations on heat (Lambert-Habib et al., 2013), relatively low
prioritization of heat issues in many cities (Lu et al., 2017; Mahlkow et al., 2016), and
uncertainties about which strategies are most effective (Middel et al., 2020). Literature on
6
barriers to climate change adaptation planning more broadly suggests that fragmented
institutions, political leadership, public support, and human and capital resources are among the
biggest challenges (Eisenack et al., 2014; Hamin et al., 2014; Oulahen et al., 2018; Shi et al.,
2015). It is unclear whether these apply to heat planning across the U.S.
Climate information is another potential barrier to effective planning (Kim et al., 2020)
and previous evaluations of climate adaptation, resilience, sustainability, and hazard mitigation
plans have found that the “fact base” commonly needs improvement (Berke & Godschalk, 2009;
Woodruff et al., 2018). While heat mapping and modeling is a common focus of heat research,
the use of that information by planners is not well understood (Keith et al., 2020; Kim et al.,
2020). Studies suggest that climate change projections are rarely used and planners focus more
on past experiences ( Eliasson, 2000; Mahlkow & Donner, 2017). Kim et al. (2020) also note that
high-resolution climate data in plan-making is not readily available, meaning planners have to
seek out data directly from climate scientists. Moreover, mapping and modelling alone does not
capture important social variables, perceptions, or networks (Zaidi & Pelling, 2015), so
additional information sources are needed to plan for extreme heat, including socio-demographic
data, vulnerability maps, and housing quality information.
With so many potential barriers, it is critical to identify what factors encourage action.
The literature on climate change planning suggests that city capacity (e.g., size and resources),
climate change perceptions (closely tied to politics in the U.S.), experience with climate impacts,
political leadership, and membership in city networks are all predictors of planning (Reckien et
al., 2018; Shi et al., 2015). Here again, it is unclear whether this also applies to heat planning.
Assessing the state of heat planning across the U.S.
While recognition of the inequitable risks of extreme heat and role of planning in
addressing them is increasing, it is unclear how or why this is translating into action in
communities across the U.S. We addresses this gap by surveying planning professionals from
diverse communities to understand how they are planning for extreme heat. The online survey
included heat-specific questions on risk perceptions, impacts, sources of information, planning,
the effectiveness and implementation of heat strategies, and barriers to action. We field the
survey on two samples: 1) planners from a stratified random sample of U.S. cities; 2) a
convenience sample of planners recruited through professional networks.
Survey design
We co-produced survey questions iteratively with heat experts, including urban planners,
urban heat mappers, and climate service providers. We also adapted several questions (e.g., on
heat risk perceptions) from a survey conducted by the National Drought Mitigation Center for
the APA (2018). Our survey (Appendix 1ii) was administered using Qualtrics and incentivized
with an opportunity to win a gift card.
Our dual sampling strategy provides a comprehensive understanding of the current state
of heat planning in the U.S. The first sample aims to be representative of how cities across the
U.S. are planning for extreme heat. The second sample captures the perspectives of planning
professionals more broadly, but especially those with an interest in heat and climate planning.
For the stratified random sample we drew from a dataset of the 1000 largest U.S. cities
(Jones 2015) and grouped these into ten regions, using the Fourth National Climate Assessment
(NCA) climatic regions for the contiguous states and adding Alaska, Hawaii, and Puerto Rico
7
(US Global Change Research Program, 2018). We used climatic regions to better understand
planning efforts in cities facing different heat risks and other climate challenges. We further
stratified the sample by population into small (population less than 50,000), medium (50,000-
300,000), and large cities (over 300,000) based on Pitt & Bassett (2014). We intentionally
included small- and medium-sized cities, which are often understudied (Bell & Jayne, 2009). We
randomly selected five small, medium, and large cities from each of the contiguous regions and
the two largest cities in Alaska, Hawaii, and Puerto Rico.iii The result was a sampling frame of
111 cities (Appendix 2). We then searched the city government websites to identify city planner
contacts.iv We received completed surveys from 69 cities (Figure 2), for a response rate of 62%.
The convenience sample consisted of planning professionals nationwide (Figure 2). A
link to the survey was distributed through the researchers’ social media and relevant networks
(e.g., various APA divisions; Appendix 3). While 116 planning professionals completed the
survey, we focus on the 98 responses from planning professionals reportedly working at the local
level to make the results comparable to the random sample.
Figure 2. Surveyed planners’ communities: The map on the top shows the 69 cities that
responded in the random sample and the NCA regions. The map on the bottom shows the cities
and number of respondents per state where the 98 convenience sample respondents indicated
working.
8
Survey statistical analysis
We calculated descriptive statistics for all survey questions. We used T-tests to compare mean
responses by city size and region and between the random and convenience sample. When
describing these results, we denote a difference as statistically significant if p<0.05. We
compiled additional indicators for all random sample cities that we expect to be associated with
extreme heat action or concern based on the climate change planning literature. We grouped
these into 7 thematic indicesv representing: 1) city capacity; 2) political orientation; 3) historic
heat exposure and impacts; 4) projected future heat exposure; 5) heat vulnerable populations; 6)
use of heat information; and 7) membership in international city networks (Detailed description
of variables, rationale, and sources in Appendix 17). We then used them to fit multivariate
regression models predicting heat concern and implementation of strategies and plans addressing
heat.vi
Random sample survey results: Planning for extreme heat across the U.S.
Our results show that planners across the U.S. are concerned with extreme heat risks,
particularly to the environment and public health. They were more concerned about climate
change than the UHI as a contributor to extreme heat. Most planners indicated that their
communities have been affected by extreme heat. While most planners claimed to be addressing
heat in plans and implementing mitigation and management strategies such as urban forestry,
emergency response, and weatherization, they perceived many barriers to advancing heat
planning, with the most significant related to human and financial resources and political will.
Extreme heat impacts
Planners in the random sample widely reported extreme heat impacts, with 84%
indicating their city had been impacted. Energy use (67%) was most common, followed by water
use (60%), urban vegetation or wildlife (49%), public health (47%), and quality of life (44%).
We expected that regions would be affected differently by extreme heat. Indeed, energy use was
the most reported impact in the Northeast (7/7, 100%), Southern Great Plains (11/13, 85%), and
Southeast (6/9, 67%), but it was tied with public health in the Midwest (5/7, 71%) and Northwest
(4/9, 44%). Water use was top in the Southwest (9/11, 82%) and Northern Great Plains (6/11,
55%), and quality of life and urban vegetation or wildlife in Hawaii and the Pacific (2/2, 100%)
(Appendix 5). Only five (7%) respondents chose the ‘other’ option, suggesting the answer
choices captured most of the primary effects. Respondents also mentioned agriculture or food
production, water (water quality, streamflow, flooding), wildfires, equity issues, recreation,
fishing, population, crime, mental health, and increased mortality rates.
Concern about extreme heat
We find that most planners (73%) were somewhat concerned about heat overall, and 30%
were very concerned. More planners were very concerned about environmental (43%) than
health (36%) and economic (13%) outcomes (Appendix 3). Somewhat surprisingly, the same
number (13%) of the planners were not at all concerned with economic effects, whereas just 4%
were not concerned about environmental and 6% with health consequences of heat (Appendix 7).
It is interesting that we find greater concern about climate change than the UHI (48% versus 33%
very concerned) because while the planning profession plays an important role in climate
9
change, local planning tools and processes arguably more directly shape the built environment
and corresponding UHI effect.
Except for the Northern Great Plains, most respondents in all regions were at least
somewhat concerned about extreme heat overall (Appendix 8). The Northeast stands out with
71% (5/7) of the respondents being very concerned, significantly higher than the overall sample.
This does not mirror geographic trends in public concern, which tended to be higher in the
southern regions of the country (Howe et al., 2019). Differences in mean levels of concern were
not statistically significant for any other region.
We do not find a clear relationship between city size and overall concern with extreme
heat (Appendix 9). Differences in mean levels of concern for the three categories of city size
were not statistically significant, although the mean was higher for large cities. This is somewhat
surprising, since research has established UHI intensity increases with city size (Oke, 1973;
Zhou et al., 2017), and consequently, heat mitigation is often percieved as an urban challenge.
Figure 3. Reported impacts from extreme heat
10
Figure 4. Average concern for environmental, public health, and economic impacts of extreme
heat (darker, orange) and concern for extreme heat caused by climate change and the urban heat
island effect (lighter, yellow)
Heat Information Sources
Most planners (70%) reported that some heat information source was used in their
communities, including vegetation and tree canopy maps (77%), the Heat Index (74%), and
historic temperature data (63%). Importantly, the responses do not tell us who uses them or how
they inform decisions. Less frequently used information sources included future projections
(46%), real-time temperature readings (46%), ambient air temperature maps (39%), heat
vulnerability maps (29%), land surface temperature maps (18%), and future scenarios (12%).
Additional open-ended responses included sources such as hot-weather tips for pets and heat-
related illnesses.
When asked what information sources were not used because they were not available –
information gaps that could be filled by researchers and climate service providers (organizations
that produce information or products on climate impacts) – planners reported future scenarios
(68%), land surface temperature maps (65%), and heat vulnerability maps (54%) as being the
least available. The lack of future heat scenarios supports a recent recommendation by Kim et al.
(2020) that planners should work more with climate scientists to integrate climate projections
into plans. Planners also need to be better connected to existing information sources.
Finally, our finding that the majority of respondents lacked heat vulnerability maps is
particularly problematic in light of the intersection of racial injustice and heat and calls to center
equity in addressing urban heat (Wilson, 2020). Heat vulnerability assessments need to be
conducted and disseminated to planners.
No information source was deemed not useful by more than a quarter of surveyed
planners. In response to an open-ended question about what information would be most helpful,
planners reported that comparisons to historic data, information on micro-environments, how
materials and development shape the UHI effect, vegetation species most effective in heat
mitigation, and public climate change perceptions would be helpful.
11
Figure 5. Heat information Sources: Sources that planners reported using (top/orange bar); and
sources that planners do not use because they are unavailable (lower/red bar)
Level of government and plan types addressing heat
We asked planners to indicate which level(s) of government they thought should be
responsible for extreme heat planning. Most planners (68%) responded that local governments
should be responsible for extreme heat planning. A large percentage also reported that the county
level (49%), regional level (43%), state level (61%), and national level (48%) should be
responsible.
Research has shown that a community’s network of plans collectively shapes resilience to
hazards such as flooding (Yu et al., 2020), and our survey suggests that heat is also addressed in
a range of plans. This confirms that heat planning “is marked by a high degree of pluralism”
spanning city departments and levels of government (Bolitho & Miller, 2017, p. 690). While
extreme heat may be less regulated than other hazards (Hamstead et al., 2020), the majority of
planners (65%) reported addressing extreme heat in at least one of their community’s plans.
However, no single plan type addressed heat in most communities.
The largest share of planners addressed extreme heat in a sustainability, climate action, or
resilience plan (36%), followed by emergency response plans (25%), comprehensive plans
(25%), and hazard mitigation plans (19%). These findings suggest that if heat strategies are not
coordinated across the full network of plans, policies are likely to work at cross purposes and
lead to undesired or inequitable outcomes (Berke et al., 2015). For researchers studying heat
planning, it is important to assess all these community plans.
Notably, only 10% of planners reported addressing heat in building codes and 9% in
zoning codes and regulations. As an example of heat-related zoning, Phoenix requires new
sidewalks be 75% shaded (Burgess & Foster, 2019). Zoning codes and regulations have some of
the strongest regulatory ‘teeth’ and are among the most effective hazards planning tools
(Schwab, 2010), so the lack of heat considerations there threatens implementation. While
planners may not report addressing heat through these strategies, research has demonstrated that
land use and urban design affect urban climates (Stone & Rodgers, 2001), and building codes
(e.g. for insulation, wall thickness, window panes, or cool roof requirements) shape the thermal
12
comfort of occupants, energy costs associated with cooling, and waste heat (Coseo & Larsen,
2014; Nahlik et al., 2017). Planners need to recognize how their planning decisions influence
local climates.
Strategies for addressing heat
We find several interesting trends in the heat mitigation and management strategies
planners reported implementing and those perceived as most effective. Most respondents
reported implementing at least one strategy (87%). Urban forestry and vegetation was the top
strategy (73%), and deemed most effective (mean 3.68/4). Planners’ broad consensus on cooling
benefits of urban vegetation is generally supported in the literature (Krayenhoff et al., 2021).
Other top implemented strategies included emergency response (66%), weatherization
(51%), manmade shade structures (50%), urban design (49%), and warning systems (49%).
These strategies represent a mix of urban heat mitigation and management strategies. It is
slightly surprising that urban design strategies were not more common given the extensive
literature on the relationship between the built environment, urban design, and the UHI effect
(Keith et al., 2020; Stone & Rodgers, 2001).
The two least commonly implemented strategies, regulations and staff, are closely related
to the likelihood of plan implementation. Only 24% of respondents reported having heat
regulations. This reinforces an earlier finding suggesting a lack of regulatory teeth in heat
planning. The least implemented strategy appears to be dedicated heat staff (8%), although
unsurprisingly, large cities were much more likely to have heat staff (20%) than small (6%) or
medium (4%) cities. This contrasts with the high perceived effectiveness of heat staff (mean
3.16/4). Similarly, vulnerability assessments were assessed sixth most effective, but less
commonly implemented. Staff tasked with coordinating heat across departments might help
clarify responsibilities and break down silos.
We find several regional differences in implementation and perceived effectiveness of
strategies (Appendices 13 & 14). Planners in the Northeast reported implementing significantly
more strategies, with over 80% (6/7) implementing urban forestry and cooling centers, warning
systems, emergency response, weatherization, green roofs, and vulnerability assessments. In
contrast, planners in the Northwest implemented significantly fewer strategies than the overall
sample.
Planners in large cities reported implementing more strategies (Appendix 15), consistent
with the literature on climate change adaptation planning more broadly (Hamin et al., 2014; Shi
et al., 2015). Almost all strategies were implemented in large cities including urban forestry
(94%), weatherization (93%), and emergency response (92%). The two exceptions were drinking
fountains (47%) and staff (20%), although these were even less common in medium or small
cities. If small and medium communities do not plan for heat resilience, worsening heat could
lead to economic losses that erode future planning capacity and reinforce disparities between
communities.
13
Figure 6. Implemented strategies: primarily heat management strategies are shown above
(darker, red), primarily mitigation strategies are listed below (lighter, orange) with the percent of
communities that reported implementing them.
Heat planning barriers
Consistent with the literature, planners report significant barriers to heat planning (Keith
et al., 2020). All 12 barrier choices were rated at least as a slight barrier (mean ≥ 2.00), and nine
barriers were rated at least as moderate (mean ≥3.00). Funding tied with time and staff as the
most significant barrier (mean 3.74), followed by higher priority issues (3.55), leadership (3.33),
public support (3.25), expertise to understand existing information (3.17), knowledge of heat
strategies (3.15), and coordination between agencies and/or jurisdictions (3.03). The spatial
resolution of existing information (2.94), temporal scale of existing information (2.89), data on
extreme heat risk (2.87), and uncertainty/reliability of existing information (2.86) were less
problematic, but still on average at least a slight barrier to extreme heat planning. These results
suggest that planners see heat information as less of a problem than governance issues like
human and capital resources and political will. This is consistent with the literature on lack of
funding and staff being barriers to climate change adaptation planning (Eisenack et al., 2014;
Hamin et al., 2014; Olazabal & Ruiz De Gopegui, 2021; Oulahen et al., 2018; Shi et al., 2015),
and are also likely barriers to other urban planning efforts. Only three respondents wrote in an
‘other’ barrier. All three mentioned extreme heat was not a priority for the community;
unsurprisingly they were not in the hottest regions.
14
Comparing the random and convenience samples
Planners in the convenience sample were, on average, significantly more concerned with
extreme heat than the random sample, and this included overall (random: 3.00; convenience:
3.38), environmental (random: 3.19; convenience: 3.48), economic (random: 2.64; convenience:
2.89), and public health impacts (random: 2.97; convenience: 3.48), as well as the UHI (random:
2.93; convenience: 3.22) and climate change (random: 3.29; convenience: 3.62) as a contributor
to extreme heat risk (Appendix 6). Most (54%) of the convenience sample respondents indicated
being very concerned overall and with environmental (64%) and public health (61%) impacts
(Appendix 7). This is not surprising, since respondents who self-select to take a survey on
extreme heat and who are part of related networks would likely be professionals who are more
interested in the topic. This does reinforce the need to include diverse cities in planning studies
and why it is important to use a deliberate sampling strategy. Focusing only on the largest cities
or a convenience sample would likely overstate current heat planning efforts.
As anticipated, a larger percentage of planners in the convenience sample (87%) used
heat information than in the random sample, likely again due to self-selection and interest in
extreme heat planning, although the difference was not statistically significant (Appendix 10). In
fact, the share of the convenience sample reporting using each type of information was between
5% and 41% higher for all information types. The largest difference was in the use of land
surface temperature maps, which 59% of the convenience sample reported using, compared to
18% of the random sample. A greater share of the convenience sample thought every level of
government should be responsible than the targeted sample (Appendix 11). More planners in the
convenience sample reported addressing heat in a sustainability, climate action, or resilience plan
(52%), hazard mitigation plans (40%), and emergency response plans (27%), but fewer claimed
to do so in a comprehensive plan (19%). This difference may reflect the fact that the general
sample contained more heat and climate planning specialists who would likely be familiar with
sustainability, climate action, or resilience plans and hazard mitigation plans, while random
sample planners focus on comprehensive plans (Appendix 11). When it comes to implementing
heat strategies, the convenience sample suggests more of a focus on heat management than
mitigation (Appendix 12).
Planners in the convenience sample indicated the same top five barriers as the random
sample, although they rated higher priority issues (mean 3.46) as a more significant barrier than
time and staff (3.39) (Appendix 16). On average, the convenience sample tended to rate barriers
as a little less significant than the planners in the random sample. It could be that these planners
see heat as a bigger risk and are not willing to let potential obstacles impede action. The only
barrier that general sample planners rated more problematic than the random sample was
coordination between agencies and/or jurisdictions (mean 3.61) and data on extreme heat risk
(2.99).
15
Predictors of planners’ extreme heat concern and action
Our regression models suggest that either past or future heat exposure and use of different
heat information sources predict concern for extreme heat, the number of different heat strategies
implemented, and the number of plan types that address heat. Overall concern and the number of
strategies implemented were both only significantly correlated with the number of heat
information sources reportedly used and the projected heat index. There was also a marginal
(p<0.1) relationship between the heat vulnerable populations index and the number of strategies
implemented. The number of plans addressing heat was significantly predicted by the number of
heat information sources and the experienced heat index, while city capacity was marginally
significant (p<0.1). Projected heat was not a significant predictor of the number of plans
addressing heat, suggesting plans may reflect past experiences more than future climate
projections – a finding consistent with prior research (e.g., Eliasson, 2000). Future research
should examine heat strategies across cities’ networks of plans to see whether some communities
with a diverse portfolio of heat strategies are implementing them in select types of plans. While
we hypothesize that richer knowledge of heat risks, and therefore use of different heat
information, would lead to greater concern, it is possible that the relationship is reversed, with
concern driving the use of heat information sources.
It is surprising that many other variables that we expected to be predictors of heat
concern and action based on the literature are not significant in our models, including city
capacity, political and educational attributes of the population, membership in city networks, and
heat vulnerable populations. This could be the result of the small sample size, or there may be
another explanation. For example, while membership in city networks has been shown to predict
climate planning (Reckien et al., 2018), heat may not be a major focus of these networks’
resources since it only accounts for a small percentage of climate change adaptation resources
(Nordgren et al., 2016). With the growing interest in heat resilience planning, future studies
should apply the same variables to more cities or conduct interviews to test these relationships.
16
Table 1. Predictors of heat concern and action
Dependent variable
Concern (Overall)
Number of plans addressing
heat
Number of heat
strategies
implemented
City capacity -0.068
0.143*
0.005
(0.074)
(0.080)
(0.053)
Heat vulnerable
populations
0.024
0.146
0.120*
(0.085)
(0.093)
(0.062)
Politics
-0.048
-0.049
0.054
(0.090)
(0.107)
(0.067)
City network membership
0.010
-0.066
-0.022
(0.093)
(0.101)
(0.071)
Heat information
0.159***
0.223***
0.144***
(0.048)
(0.049)
(0.034)
Projected heat
0.177
**
0.024
0.209
***
(0.081)
(0.092)
(0.061)
Experienced heat 0.040
0.065***
0.027
(0.025)
(0.025)
(0.018)
Constant 2.641***
-0.331
1.328***
(0.179)
(0.237)
(0.142)
Observations
69
69
69
Log Likelihood
-76.325
-92.422
-174.158
theta
7.420
*
(4.090)
Akaike Inf. Crit.
168.651
200.843
364.316
Note:
*p<0.1; **p<0.05; ***p<0.01
17
Conclusion
Heat is the deadliest weather and climate-related hazard in the U.S., and poses a growing
threat to communities due to the UHI effect and climate. Planners have an important role to play
in both mitigating heat in the built environment and managing inequitable heat risks. Despite
growing recognition of this imperative, relatively little research to date has focused on heat
planning processes. We conducted a survey of planners from across the U.S. to establish a
baseline of the state of heat planning, including risk perceptions, impacts, planning effort,
information sources, strategies, and barriers. Most planners are concerned about heat impacts to
their communities, especially from climate change. The majority of communities have been
affected by heat, particularly energy and water use, vegetation, and health. Most planners said
their communities were addressing heat in some plan, most commonly sustainability, climate
change, or resilience plans, but no plan type addressed heat in the majority of surveyed
communities. Planners agreed that the local level should be most responsible for addressing
extreme heat, although a large percentage also felt that county, regional, state, and federal
jurisdictions had an important role to play. When addressing heat, planners rely on different
sources of heat information, most commonly vegetation maps, the Heat Index, and historical
temperatures. Future scenarios and land surface temperature maps are desired but not seen as
widely available, highlighting a potential opportunity for those who develop climate resources
(climate service providers).
Many communities are implementing heat strategies and planners seem convinced they
will be effective, especially urban forestry and vegetation. A minority of communities reported
providing utility assistance or conducting vulnerability assessments. Planners see many barriers
to effective heat planning, mostly related to human and capital resources. Staff time, for
example, was a perceived barrier and very few communities reported having staff dedicated to
heat. Likely due to the sample size, observed differences between regions and community sizes
were not statistically significant. Notable exceptions included the finding that planners in the
Northeast were significantly more concerned about extreme heat and implemented significantly
more strategies. Large cities implemented more strategies than small- or medium-sized ones. Our
statistical analysis of potential climate-related, sociodemographic, and political drivers of
concern for extreme heat and action suggest that both the number of heat information sources
used and projected heat exposure are related to overall concern for heat and the number of
strategies implemented, while heat information and experienced heat are related to the number of
plans addressing heat.
Planning for extreme heat is an emerging focus area, and future research can build off
this study by validating survey responses through analyses of city plans, conducting in-depth
case studies of heat planning processes in specific communities, and surveying planners in more
cities or in the future to see how extreme heat planning evolves. With planners well-positioned to
take a leading role in addressing the growing threat of extreme heat, we expect that more
communities will be addressing this challenge in the near future.
18
References
Akompab, D. A., Bi, P., Williams, S., Saniotis, A., Walker, I. A., & Augoustinos, M. (2013).
Engaging stakeholders in an adaptation process: Governance and institutional arrangements
in heat-health policy development in Adelaide, Australia. Mitigation and Adaptation
Strategies for Global Change, 18(7), 1001–1018. https://doi.org/10.1007/s11027-012-9404-
4
American Planning Association. (2020). Climate Change Policy Guide.
Anderson, C. A. (2001). Heat and violence. Current Directions in Psychological Science, 10(1),
33–38. https://doi.org/10.1111/1467-8721.00109
American Planning Association. (2018). Drought Planning in a Multihazards Context Survey
Report.
Bell, D., & Jayne, M. (2009). Small cities? Towards a research agenda. International Journal of
Urban and Regional Research, 33(3), 683–699. https://doi.org/10.1111/j.1468-
2427.2009.00886.x
Berisha, V., Hondula, D., Roach, M., White, J. R., McKinney, B., Bentz, D., Mohamed, A.,
Uebelherr, J., & Goodin, K. (2017). Assessing adaptation strategies for extreme heat: A
public health evaluation of cooling centers in Maricopa County, Arizona. Weather, Climate,
and Society, 9(1), 71–80. https://doi.org/10.1175/WCAS-D-16-0033.1
Berke, P., & Godschalk, D. (2009). Searching for the Good Plan. Journal of Planning Literature,
23(3), 227–240. https://doi.org/10.1177/0885412208327014
Berke, P. R., Newman, G., Lee, J., Combs, T., Kolosna, C., & Salvesen, D. (2015). Evaluation of
Networks of Plans and Vulnerability to Hazards and Climate Change: A Resilience
Scorecard. Journal of the American Planning Association, 81(4), 287–302.
https://doi.org/10.1080/01944363.2015.1093954
Berko, J., Ingram, D. D., Saha, S., & Parker, J. D. (2014). Deaths attributed to heat, cold, and
other weather events in the United States, 2006–2010. National Health Statistics Reports,
2014(76), 1–15.
Bolitho, A., & Miller, F. (2017). Heat as emergency, heat as chronic stress: policy and
institutional responses to vulnerability to extreme heat. Local Environment, 22(6), 682–698.
https://doi.org/10.1080/13549839.2016.1254169
Brans, K. I., Engelen, J. M. T., Souffreau, C., & De Meester, L. (2018). Urban hot-tubs: Local
urbanization has profound effects on average and extreme temperatures in ponds.
Landscape and Urban Planning, 176, 22–29.
https://doi.org/10.1016/j.landurbplan.2018.03.013
Burby, B. R. J., Deyle, R. E., Godschalk, D. R., & Olshansky, R. B. (2000). Creating Hazard
Resilient Communities through land-use planning. Natural Hazards Review, 1(May), 99–
106. https://doi.org/http://ascelibrary.org/doi/10.1061/%28ASCE%291527-
6988%282000%291%3A2%2899%29
Burby, R. J. (Ed.). (1998). Cooperating with Nature: Confronting Natural Hazards with Land-
Use Planning for Sustainable Communities. The National Academies Press.
https://doi.org/10.17226/5785
Burgess, K., & Foster, E. (2019). Scorched: Extreme Heat and Real Estate. Urban Land Institute.
CDC. (2015, January 13). Hierarchy of Controls. U.S. Centers for Disease Control and
Prevention. https://www.cdc.gov/niosh/topics/hierarchy/default.html
Corburn, J. (2009). Cities, climate change and urban heat island mitigation: Localising global
environmental science. Urban Studies, 46(2), 413–427.
19
https://doi.org/10.1177/0042098008099361
Coseo, P., & Larsen, L. (2014). How factors of land use/land cover, building configuration, and
adjacent heat sources and sinks explain Urban Heat Islands in Chicago. Landscape and
Urban Planning, 125, 117–129. https://doi.org/10.1016/j.landurbplan.2014.02.019
Davis, R. E., Knappenberger, P. C., Michaels, P. J., & Novicoff, W. M. (2003). Changing heat-
related mortality in the United States. Environmental Health Perspectives, 111(14), 1712–
1718. https://doi.org/10.1289/ehp.6336
Dhalluin, A., & Bozonnet, E. (2015). Urban heat islands and sensitive building design - A study
in some French cities’ context. Sustainable Cities and Society, 19, 292–299.
https://doi.org/10.1016/j.scs.2015.06.009
Dunne, J. P., Stouffer, R. J., & John, J. G. (2013). Reductions in labour capacity from heat stress
under climate warming. Nature Climate Change, 3(6), 563–566.
https://doi.org/10.1038/nclimate1827
Eisenack, K., Moser, S. C., Hoffmann, E., Klein, R. J. T., Oberlack, C., Pechan, A., Rotter, M.,
& Termeer, C. J. A. M. (2014). Explaining and overcoming barriers to climate change
adaptation. Nature Climate Change, 4(10), 867–872. https://doi.org/10.1038/nclimate2350
Gasparrini, A., Guo, Y., & Hashizume, M. (2015). Mortality risk attributable to high and low
ambient temperature: a multicountry observational study. The Lancet, 14(6), 464–465.
https://doi.org/10.1016/S0140-6736(14)62114-0
Gober, P., Brazel, A., Quay, R., Myint, S., Grossman-Clarke, S., Miller, A., & Rossi, S. (2010).
Using watered landscapes to manipulate urban heat island effects: How much water will it
take to cool phoenix? Journal of the American Planning Association, 76(1), 109–121.
https://doi.org/10.1080/01944360903433113
Grimm, N. B., Faeth, S. H., Golubiewski, N. E., Redman, C. L., Wu, J., Bai, X., & Briggs, J. M.
(2008). Global change and the ecology of cities. In Science (Vol. 319, Issue 5864, pp. 756–
760). American Association for the Advancement of Science.
https://doi.org/10.1126/science.1150195
Guhathakurta, S., & Gober, P. (2007). The impact of the Phoenix urban heat Island on residential
water use. Journal of the American Planning Association, 73(3), 317–329.
https://doi.org/10.1080/01944360708977980
Guindon, S. M., & Nirupama, N. (2015). Reducting risk from urban heat island effects in cities.
Natural Hazards, 77(2), 823–831. https://doi.org/10.1007/s11069-015-1627-8
Guyer, H. E., Putnam, H. F., Roach, M., Iñiguez, P., & Hondula, D. M. (2019). Cross-sector
management of extreme heat risks in Arizona. Bulletin of the American Meteorological
Society, 100(3), ES101–ES104. https://doi.org/10.1175/BAMS-D-18-0183.1
Hamilton, I. G., Davies, M., & Gauthier, S. (2010). London’s urban heat island: a multi-scaled
assessment framework. Proceedings of the Institution of Civil Engineers, May, 33–34.
https://doi.org/http://dx.doi.org/10.1680/udap.10.00046
Hamin, E. M., Gurran, N., & Emlinger, A. M. (2014). Barriers to municipal climate adaptation:
Examples from coastal massachusetts smaller cities and towns. Journal of the American
Planning Association, 80(2), 110–122. https://doi.org/10.1080/01944363.2014.949590
Hamstead, Z., Coseo, P., Alkhaled, S., Boamah, E. F., David, M., Middel, A., & Rajkovich, N.
(2020). Thermally resilient communities: creating a socio-technical collaborative response
to extreme temperatures. 1, 218–232.
Hansen, A., Bi, P., Nitschke, M., Ryan, P., Pisaniello, D., & Tucker, G. (2008). The effect of
heat waves on mental health in a temperate Australian City. Environmental Health
20
Perspectives, 116(10), 1369–1375. https://doi.org/10.1289/ehp.11339
Hatvani-Kovacs, G., Bush, J., Sharifi, E., & Boland, J. (2018). Policy recommendations to
increase urban heat stress resilience. Urban Climate, 25, 51–63.
https://doi.org/10.1016/j.uclim.2018.05.001
Hayhoe, K., Wuebbles, D. J., Easterling, D. R., Fahey, D. W., Doherty, S., Kossin, J. P., Sweet,
W. V., Vose, R. S., & Wehner, M. F. (2018). Chapter 2 : Our Changing Climate. Impacts,
Risks, and Adaptation in the United States: The Fourth National Climate Assessment,
Volume II. https://doi.org/10.7930/NCA4.2018.CH2
Heilmann, K., Kahn, M. E., & Tang, C. K. (2021). The urban crime and heat gradient in high and
low poverty areas. Journal of Public Economics, 197, 104408.
https://doi.org/10.1016/j.jpubeco.2021.104408
Hibbard, K. ., Hoffman, F. M., Huntzinger, D., & West, T. O. (2017). Ch. 10: Changes in Land
Cover and Terrestrial Biogeochemistry. Climate Science Special Report: Fourth National
Climate Assessment, Volume I (D. J. Wuebbles, D. W. Fahey, K. A. Hibbard, D. J. Dokken,
B. C. Stewart, & T. K. Maycock (Eds.)). https://doi.org/10.7930/J0416V6X
Hoffman, J. S., Shandas, V., & Pendleton, N. (2020). The Effects of Historical Housing Policies
on Resident Exposure to Intra-Urban Heat: A Study of 108 US Urban Areas. Climate, 8(1),
12. https://doi.org/10.3390/cli8010012
Hondula, D. M., Davis, R. E., Saha, M. V., Wegner, C. R., & Veazey, L. M. (2015). Geographic
dimensions of heat-related mortality in seven U.S. cities. Environmental Research, 138,
439–452. https://doi.org/10.1016/j.envres.2015.02.033
Hondula, D. M., Sabo, J. L., Quay, R., Chester, M., Georgescu, M., Grimm, N. B., Harlan, S. L.,
Middel, A., Porter, S., Redman, C. L., Rittmann, B., Ruddell, B. L., & White, D. D. (2019).
Cities of the Southwest are testbeds for urban resilience. Frontiers in Ecology and the
Environment, 17(2), 79–80. https://doi.org/10.1002/fee.2005
Howe, P. D., Marlon, J. R., Wang, X., & Leiserowitz, A. (2019). Public perceptions of the health
risks of extreme heat across US states, counties, and neighborhoods. Proceedings of the
National Academy of Sciences of the United States of America, 116(14), 6743–6748.
https://doi.org/10.1073/pnas.1813145116
Hsu, A., Sheriff, G., Chakraborty, T., & Manya, D. (2021). Disproportionate exposure to urban
heat island intensity across major US cities. Nature Communications, 12(1), 2721.
https://doi.org/10.1038/s41467-021-22799-5
Ibsen, P. C., Borowy, D., Dell, T., Greydanus, H., Gupta, N., Hondula, D. M., Meixner, T.,
Santelmann, M. V, Shiflett, S. A., Sukop, M. C., Swan, C. M., Talal, M. L., Valencia, M.,
Wright, M. K., & Jenerette, G. D. (2021). Greater aridity increases the magnitude of urban
nighttime vegetation-derived air cooling. Environmental Research Letters, 16(3), 034011.
https://doi.org/10.1088/1748-9326/abdf8a
Ingegaerd Eliasson. (2000). The use of climate knowledge in urban planning. Landscape and
Urban Planning, 48(1–2), 31–44. https://www.erfelijkheid.nl/ziektes/syndroom-van-down-
trisomie-21
Jones, B., Dunn, G., & Balk, D. (2021). Extreme Heat Related Mortality: Spatial Patterns and
Determinants in the United States, 1979–2011. Spatial Demography, 1–23.
https://doi.org/10.1007/s40980-021-00079-6
Keith, L., Meerow, S., & Wagner, T. (2020). Planning for extreme heat: A review. Journal of
Extreme Events, 6(2), 1–27. https://doi.org/10.1142/S2345737620500037
Kim, S., Sun, F., & Irazábal, C. (2020). Planning for Climate Change: Implications of High
21
Temperatures and Extreme Heat for Los Angeles County (CA). Journal of the American
Planning Association, 87(1), 34–44. https://doi.org/10.1080/01944363.2020.1788415
Kjellstrom, T., Gabrysch, S., Lemke, B., & Dear, K. (2009). The “hothaps” programme for
assessing climate change impacts on occupational health and productivity: An invitation to
carry out field studies. Global Health Action, 2(1). https://doi.org/10.3402/gha.v2i0.2082
Klok, E. J. (Lisette., & Kluck, J. (Jeroen). (2018). Reasons to adapt to urban heat (in the
Netherlands). Urban Climate, 23, 342–351. https://doi.org/10.1016/j.uclim.2016.10.005
Knowlton, K., Rotkin-Ellman, M., King, G., Margolis, H. G., Smith, D., Solomon, G., Trent, R.,
& English, P. (2009). The 2006 California heat wave: Impacts on hospitalizations and
emergency department visits. Environmental Health Perspectives, 117(1), 61–67.
https://doi.org/10.1289/ehp.11594
Koop, S. H. A., Koetsier, L., Doornhof, A., Reinstra, O., Van Leeuwen, C. J., Brouwer, S.,
Dieperink, C., & Driessen, P. P. J. (2017). Assessing the Governance Capacity of Cities to
Address Challenges of Water, Waste, and Climate Change. Water Resources Management,
31(11), 3427–3443. https://doi.org/10.1007/s11269-017-1677-7
Kovats, R. S., & Hajat, S. (2008). Heat Stress and Public Health: A Critical Review. Annual
Review of Public Health, 29(1), 41–55.
https://doi.org/10.1146/annurev.publhealth.29.020907.090843
Krayenhoff, E. S., Broadbent, A. M., Zhao, L., Georgescu, M., Middel, A., Voogt, J. A., Martilli,
A., Sailor, D. J., & Erell, E. (2021). Cooling hot cities: A systematic and critical review of
the numerical modelling literature. Environmental Research Letters.
https://doi.org/10.1088/1748-9326/abdcf1
Lambert-Habib, M. L., Hidalgo, J., Fedele, C., Lemonsu, A., & Bernard, C. (2013). How is
climatic adaptation taken into account by legal tools? Introduction of water and vegetation
by French town planning documents. Urban Climate, 4, 16–34.
https://doi.org/10.1016/j.uclim.2013.04.004
Lu, P., Shen, Y. T., & Lin, T. H. (2017). Environmental risks or costs? Exploring flooding and
the urban heat Island effect in planning for policymaking: A case study in the Southern
Taiwan Science Park. Sustainability, 9(12). https://doi.org/10.3390/su9122239
Mahlkow, N., & Donner, J. (2017). From Planning to Implementation? The Role of Climate
Change Adaptation Plans to Tackle Heat Stress: A Case Study of Berlin, Germany. Journal
of Planning Education and Research, 37(4), 385–396.
https://doi.org/10.1177/0739456X16664787
Mahlkow, N., Lakes, T., Donner, J., Köppel, J., & Schreurs, M. (2016). Developing storylines
for urban climate governance by using Constellation Analysis — insights from a case study
in Berlin, Germany. Urban Climate, 17, 266–283.
https://doi.org/10.1016/j.uclim.2016.02.006
Meerow, S., & Woodruff, S. (2019). Seven principles for strong climate change planning.
Journal of the American Planning Association, 86(1), 39–46.
Mees, H. L. P., Driessen, P. P. J., & Runhaar, H. A. C. (2015). “Cool” governance of a “hot”
climate issue: public and private responsibilities for the protection of vulnerable citizens
against extreme heat. Regional Environmental Change, 15(6), 1065–1079.
https://doi.org/10.1007/s10113-014-0681-1
Middel, A., & Schneider, F. A. (2021). 50 Grades of Shade. Bulletin of the American
Meteorological Society. https://doi.org/10.1175/BAMS-D-20-0193.1.
Middel, A., Turner, V. K., Schneider, F. A., Zhang, Y., & Stiller, M. (2020). Solar reflective
22
pavements-A policy panacea to heat mitigation? Environmental Research Letters, 15(6).
https://doi.org/10.1088/1748-9326/ab87d4
Mitchell, B. C., & Chakraborty, J. (2018). Exploring the relationship between residential
segregation and thermal inequity in 20 U.S. cities. Local Environment, 23(8), 796–813.
https://doi.org/10.1080/13549839.2018.1474861
Nahlik, M. J., Chester, M. V., Pincetl, S. S., Eisenman, D., Sivaraman, D., & English, P. (2017).
Building Thermal Performance, Extreme Heat, and Climate Change. Journal of
Infrastructure Systems, 23(3), 04016043. https://doi.org/10.1061/(asce)is.1943-
555x.0000349
Nitschke, C. R., Nichols, S., Allen, K., Dobbs, C., Livesley, S. J., Baker, P. J., & Lynch, Y.
(2017). The influence of climate and drought on urban tree growth in southeast Australia
and the implications for future growth under climate change. Landscape and Urban
Planning, 167, 275–287. https://doi.org/10.1016/j.landurbplan.2017.06.012
Nordgren, J., Stults, M., & Meerow, S. (2016). Supporting local climate change adaptation:
Where we are and where we need to go. Environmental Science & Policy, 66, 344–352.
https://doi.org/10.1016/j.envsci.2016.05.006
O’Neill, M. S., Carter, R., Kish, J. K., Gronlund, C. J., White-Newsome, J. L., Manarolla, X.,
Zanobetti, A., & Schwartz, J. D. (2009). Preventing heat-related morbidity and mortality:
New approaches in a changing climate. In Maturitas (Vol. 64, Issue 2, pp. 98–103).
https://doi.org/10.1016/j.maturitas.2009.08.005
Oke, T. (1982). The energetic basis of the urban heat island. Quarterly Journal of the Royal
Meteorological Society.
Oke, T. R. (1973). City Size and the Urban Heat Island. Atmospheric Environment, 7(8), 769–
779.
Olazabal, M., & Ruiz De Gopegui, M. (2021). Adaptation planning in large cities is unlikely to
be effective. Landscape and Urban Planning, 206(September 2020), 103974.
https://doi.org/10.1016/j.landurbplan.2020.103974
Oulahen, G., Klein, Y., Mortsch, L., O’Connell, E., & Harford, D. (2018). Barriers and Drivers
of Planning for Climate Change Adaptation across Three Levels of Government in Canada.
Planning Theory and Practice, 19(3), 405–421.
https://doi.org/10.1080/14649357.2018.1481993
Perkins-Kirkpatrick, S. E., & Lewis, S. C. (2020). Increasing trends in regional heatwaves.
Nature Communications, 11(1), 1–8. https://doi.org/10.1038/s41467-020-16970-7
Pittman, C. (2021, May 12). Facing rising temperatures, Miami appoints chief heat officer. The
Washington Post. https://www.washingtonpost.com/climate-solutions/2021/05/12/climate-
urban-heat-miami/
Putnam, H., Hondula, D. M., Urban, A., Berisha, V., Iniguez, P., & Roach, M. (2018). It’s not
the heat, it’s the vulnerability: Attribution of the 2016 spike in heat-associated deaths in
Maricopa County, Arizona. Environmental Research Letters, 13(9).
https://doi.org/10.1088/1748-9326/aadb44
Reckien, D., Salvia, M., Heidrich, O., Church, J. M., Pietrapertosa, F., De Gregorio-Hurtado, S.,
D’Alonzo, V., Foley, A., Simoes, S. G., Krkoška Lorencová, E., Orru, H., Orru, K., Wejs,
A., Flacke, J., Olazabal, M., Geneletti, D., Feliu, E., Vasilie, S., Nador, C., … Dawson, R.
(2018). How are cities planning to respond to climate change? Assessment of local climate
plans from 885 cities in the EU-28. Journal of Cleaner Production, 191, 207–219.
https://doi.org/10.1016/j.jclepro.2018.03.220
23
Robine, J. M., Cheung, S. L. K., Le Roy, S., Van Oyen, H., Griffiths, C., Michel, J. P., &
Herrmann, F. R. (2008). Death toll exceeded 70,000 in Europe during the summer of 2003.
Comptes Rendus - Biologies. https://doi.org/10.1016/j.crvi.2007.12.001
Roman, L. A., Conway, T. M., Eisenman, T. S., Koeser, A. K., Ordóñez Barona, C., Locke, D.
H., Jenerette, G. D., Östberg, J., & Vogt, J. (2020). Beyond ‘trees are good’: Disservices,
management costs, and tradeoffs in urban forestry. Ambio, 615–630.
https://doi.org/10.1007/s13280-020-01396-8
Schiermeier, Q. (2021). Climate change made North America’s deadly heatwave 150 times more
likely. Nature, 1–4. https://doi.org/10.1038/d41586-021-01869-0
Schwab, J. C. (2010). Hazard Mitigation: Integrating Best Practices into Planning (Issue 560).
Shi, L., Chu, E., & Debats, J. (2015). Explaining Progress in Climate Adaptation Planning
Across 156 U.S. Municipalities. Journal of the American Planning Association, 81(3), 191–
201. https://doi.org/10.1080/01944363.2015.1074526
Shindell, D., Zhang, Y., Scott, M., Ru, M., Stark, K., & Ebi, K. L. (2020). The Effects of Heat
Exposure on Human Mortality Throughout the United States. GeoHealth, 4(4), 1–12.
https://doi.org/10.1029/2019GH000234
Stone, B., Mallen, E., Rajput, M., Broadbent, A., Krayenhoff, E. S., Augenbroe, G., &
Georgescu, M. (2021). Climate change and infrastructure risk: Indoor heat exposure during
a concurrent heat wave and blackout event in Phoenix, Arizona. Urban Climate, 36,
100787. https://doi.org/10.1016/j.uclim.2021.100787
Stone, B., Mallen, E., Rajput, M., Gronlund, C. J., Broadbent, A. M., Krayenhoff, E. S.,
Augenbroe, G., O’Neill, M. S., & Georgescu, M. (2021). Compound Climate and
Infrastructure Events: How Electrical Grid Failure Alters Heat Wave Risk. Environmental
Science & Technology, 55(10), 6957–6964. https://doi.org/10.1021/acs.est.1c00024
Stone, B., & Rodgers, M. O. (2001). Urban form and thermal efficiency: How the design of
cities influences the urban heat island effect. Journal of the American Planning Association,
67(2), 186–198. https://doi.org/10.1080/01944360108976228
The Trust for Public Land. (2019). Urban heat islands for U.S. cities.
https://www.arcgis.com/home/item.html?id=4f6d72903c9741a6a6ee6349f5393572
Thompson, R., Hornigold, R., Page, L., & Waite, T. (2018). Associations between high ambient
temperatures and heat waves with mental health outcomes: a systematic review. Public
Health, 161, 171–191. https://doi.org/10.1016/j.puhe.2018.06.008
Turek-hankins, L. L., Perez, E. C. De, Scarpa, G., & Ruiz-diaz, R. (2021). Climate change
adaptation to extreme heat: A global systematic review of implemented action. Oxford
Open Climate Change.
U.S. Environmental Protection Agency. (2006). Excessive Heat Events Guidebook in Brief (Issue
June).
U.S. Global Change Research Program. (2016). US Climate Resilience Toolkit.
https://toolkit.climate.gov/tool/climate-explorer-0
U.S. Global Change Research Program. (2018). Fourth National Climate Assessment.
https://www.globalchange.gov/browse/reports
Vicedo-Cabrera, A. M., Scovronick, N., Sera, F., Royé, D., Schneider, R., Tobias, A., Astrom,
C., Guo, Y., Honda, Y., Hondula, D. M., Abrutzky, R., Tong, S., Coelho, M. de S. Z. S.,
Saldiva, P. H. N., Lavigne, E., Correa, P. M., Ortega, N. V., Kan, H., Osorio, S., …
Gasparrini, A. (2021). The burden of heat-related mortality attributable to recent human-
induced climate change. Nature Climate Change, 19, 59. https://doi.org/10.1038/s41558-
24
021-01058-x
Ward, D. M. (2013). The effect of weather on grid systems and the reliability of electricity
supply. Climatic Change, 121(1), 103–113. https://doi.org/10.1007/s10584-013-0916-z
Watts, N., Amann, M., Arnell, N., Ayeb-Karlsson, S., Belesova, K., Berry, H., Bouley, T.,
Boykoff, M., Byass, P., Cai, W., Campbell-Lendrum, D., Chambers, J., Daly, M., Dasandi,
N., Davies, M., Depoux, A., Dominguez-Salas, P., Drummond, P., Ebi, K. L., … Costello,
A. (2018). The 2018 report of the Lancet Countdown on health and climate change: shaping
the health of nations for centuries to come. In The Lancet (Vol. 392, Issue 10163, pp. 2479–
2514). Lancet Publishing Group. https://doi.org/10.1016/S0140-6736(18)32594-7
Wilson, B. (2020). Urban Heat Management and the Legacy of Redlining. Journal of the
American Planning Association, 86(4), 443–457.
https://doi.org/10.1080/01944363.2020.1759127
Woodruff, S. C., Meerow, S., Stults, M., & Wilkins, C. (2018). Adaptation to Resilience
Planning: Alternative Pathways to Prepare for Climate Change. Journal of Planning
Education and Research, 0739456X1880105. https://doi.org/10.1177/0739456X18801057
Yu, S., Brand, A. D., & Berke, P. (2020). Making Room for the River: Applying a Plan
Integration for Resilience Scorecard to a Network of Plans in Nijmegen, The Netherlands.
Journal of the American Planning Association, 86(4), 417–430.
https://doi.org/10.1080/01944363.2020.1752776
Zaidi, R. Z., & Pelling, M. (2015). Institutionally configured risk: Assessing urban resilience and
disaster risk reduction to heat wave risk in London. Urban Studies, 52(7), 1218–1233.
https://doi.org/10.1177/0042098013510957
Zhou, B., Rybski, D., & Kropp, J. P. (2017). The role of city size and urban form in the surface
urban heat island. Scientific Reports, 7(1), 1–9. https://doi.org/10.1038/s41598-017-04242-2
Zuo, J., Pullen, S., Palmer, J., Bennetts, H., Chileshe, N., & Ma, T. (2015). Impacts of heat
waves and corresponding measures: A review. Journal of Cleaner Production, 92, 1–12.
https://doi.org/10.1016/j.jclepro.2014.12.078
Author Bio
Sara Meerow is an assistant professor in the School of Geographical Sciences and Urban
Planning at Arizona State University. Ladd Keith is an assistant professor in the School of
Landscape Architecture and Planning at The University of Arizona.
Acknowledgements
We thank Mokshda Kaul, Autumn Mittemiller, Erika Lynn Schmidt, and Tess Wagner for their
assistance with this paper. We also thank Pete Aniello, Jeremy Hoffman, Tonya Haigh, David M.
Hondula, Cody Knutson, Liza Kurts, Hunter Jones, and Nancy Beller-Simms for their expertise
in co-producing the survey. Finally, we thank the survey respondents for their participation and
insights. This work was supported in part by the National Oceanic and Atmospheric
Administration’s Regional Integrated Sciences and Assessments (RISA) program through grant
NA17OAR4310288 with the Climate Assessment for the Southwest program at the University of
Arizona.
Funding: National Oceanic and Atmospheric Administration’s Regional Integrated Sciences and
Assessments (RISA) program through grant NA17OAR4310288 with the Climate Assessment
for the Southwest program at the University of Arizona.
25
i County-level future climate projections are publicly available through the U.S. Climate
Resilience Toolkit (US Global Change Research Program, 2016). Additionally, the Trust for
Public Land (2019) has developed publicly accessible land surface temperature maps for nearly
14,000 U.S. cities.
ii All appendices are available in the online supplementary material:
https://doi.org/10.48349/ASU/HT5FHV
iii We started with the large cities in each region, and where categories had fewer than five cities
(e.g. the Northern Great Plains) we selected additional cities from the next population category to
fill in.
iv While planner titles varied by location, we looked first for a planning director, then a planning
manager or administrator, principal planner, planner IV, III, II, and I. Most of the respondents
reported specializing in comprehensive/long-range planning (62%), community development
(57%), or land use and code enforcement (54%). We sent an initial email to the first contact in all
cities, sent an email reminder one week later, and then followed up with a phone call for those
who had not responded after two weeks. We then repeated these steps for the second contact in
nonresponsive cities. We sought replacement contacts where emails bounced back. The survey
was open from November 4, 2019 until February 1, 2020.
v For each variable in each index, we normalized the values so they all had a mean of 0 and a
variance of 1.
vi Since the number of strategies implemented and number of plans that address heat are count
variables where the variance is at least double the mean, Poisson models were used for the
regression (negative binomial regression for strategies implemented and Poisson for plans
because the data was too sparse for a negative binomial).
