Article 23 Statement Trees as a Public Good Network

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A

pril 28, 2026

TO: Members of Arlington Town Meeting

FROM: Trees as a Public Good Network (TreesPublicGood@gmail.com)

SUBJ: Support for Proposed Substitute Motion to Warrant Article #23 to Improve Tree

Preservation & Public Health

W

e are writing to express strong support for the proposed Substitute Motion,

submitted by Robin Bergman and Jennifer Cutraro, to Warrant Article #23, “Bylaw

Amendment/Tree Preservation and Protection.”

T

he Trees as a Public Good Network is an advocacy organization for protecting forests

and urban trees across Massachusetts. Our volunteer network membership includes

individuals, members of other advocacy groups, and scientists. We base our advocacy

on studies published in peer-reviewed scientific journals.

T

his bylaw addition will maintain current protections during construction and

redevelopment for trees with a 6-inch diameter or larger in the setback areas and specify

additional protections during construction and redevelopment for trees with a 10-inch

diameter or larger on the remaining property. This upgrade will bring Arlington closer to

current tree-preservation laws in Cambridge, Newton, and Brookline, which protect trees

with a 6-inches or larger diameter on the entire property, with and without construction.

Preserving mature trees in our communities is essential to climate resilience,

public health, and environmental justice. Neighborhood trees reduce excessive heat,

air pollution, and stormwater flooding.

1, 2

Urban trees substantially improve public health

and save lives.

1, 3

Urban trees are associated with lower crime rates, calmer traffic, and

1

Climate Adaptation Actions for Urban Forests and Human Health, 2021,

https://www.fs.usda.gov/nrs/pubs/gtr/gtr_nrs203.pdf.

2

Rahman et al. 2023, “A Comparative Analysis of Urban Forests for Storm-Water Management,”

https://doi.org/10.1038/s41598-023-28629-6.

3

McDonald et al 2021, “Tree Cover & Temperature Disparity in US Urbanized Areas,”

https://doi.org/10.1371/journal.pone.0249715.

Submitted by Robin L Bergman, Town Meeting Member, Precinct 12.

better pavement—all of which save taxpayer monies.

3, 4, 5

Tree canopy distribution within and across cities is an environmental justice issue, including

in Arlington.

3, 6, 7

There are 16 state-recognized environment justice census blocks in

Arlington.

8

And a 2021 study shows that in Northeast US cities, low-income blocks have

30% less tree cover and are 7°F hotter.

3

On average, every year, US urban neighborhoods

with few trees have hundreds more deaths and tens of thousands more doctors’ visits than

urban neighborhoods with more trees.

9

Almost all of Arlington’s neighborhoods lost tree canopy between 2018 and 2023,

according to the TreeCanopy.US (a database run by the Arbor Day Foundation, with the

USDA Forest Service and PlanIT Geo). This database shows tree canopy losses in

Arlington by census block, ranging from a few percent to a high of 21.6% around the

high school. This high school neighborhood—which is a state-recognized EJ census

block—was down to only 15.6% tree canopy cover in 2023. Given the documented

public health impacts of such tree canopy loss, proactive measures by the Town to

reduce tree canopy loss are entirely appropriate.

We agree with Senator Ed Markey: "While we invest in new trees, we can’t cut down

mature trees because mature trees store more carbon and provide relief from the urban

heat island effect" (speaking at “Why Trees Matter for Green Development,” Trees as a

Public Good Webinar, April 21, 2024, https://tinyurl.com/YTreesMatter, 6:50 time mark).

We can— and must—have both trees and housing.

The Substitute Motion to Warrant Article #23 will promote greener, “low-impact

development” (LID) housing practices by encouraging

builders and architects to consider existing mature trees

in their plans. For example, if a property has a 28-inch

oak where a planned garage would be placed and a 6-

inch Norway maple on the other side of the property that

would remain, the greener approach is to flip the

floorplan so as to cut the 6-inch maple and preserve the

28-inch oak. Sometimes, best LID practice is as simple

as curving a driveway to preserve a mature tree.

4

Lin et al. 2021, “Street Trees and Crime,” https://doi.org/10.1016/j.ufug.2021.127366.

5

McPherson and Muchnick, 2005, “Effects of Street Tree Shade on Asphalt Concrete Pavement

Performance,” https://www.fs.usda.gov/psw/topics/urban_forestry/products/cufr639mcpherson-JOA-

pavingshade.pdf.

6

Boston Tree Equity Maps, Speak for the Trees, https://treeboston.org/tree-equity-maps/.

7

Keeping Cool, American Forests, https://www.treeequityscore.org/stories/keeping-cool.

8

EJ status is based on based on median household income, minority population, and language isolation,

see https://www.mass.gov/info-details/massgis-data-2020-environmental-justice-populations.

9

McDonald et al. 2024, “Current inequality and future potential of US urban tree cover for reducing heat-

related health impacts,” https://doi.org/10.1038/s42949-024-00150-3. A PDF of this article is attached.

Maintaining mature trees makes a significant difference to a

neighborhood. Even one single tree does a vast amount

of climate and public health work. The sweetgum tree

pictured here has a 13″ trunk diameter.

10

Every year, it

reduces heat enough to save a total of 173 kiloWatt hours of

energy for both houses shown; it diverts 1,604 gallons of

stormwater from streets, storm drains, and surrounding

properties; and it removes 187 pounds of CO

2

as well as

other particle pollutants.

11

Loss of one tree affects the heat,

air quality, and flooding for multiple properties. The larger the

tree, the more neighborhood properties affected.

We urge Arlington’s Town Meeting Members to vote in favor of the proposed

Substitute Motion, submitted by Robin Bergman and Jennifer Cutraro, to Warrant

Article #23 because it offers an essential improvement to Arlington’s tree preservation

bylaw that will promote better climate resilience and public health.

Sincerely,

Melissa Brown, Sarah Freeman, Kate O’Connor, and Don Ogden

Steering Committee Members

on behalf of Trees as a Public Good Network

10

This sweetgum tree is mature but not full grown. Sweetgums grow to 60–75 feet, and each foot of trunk

diameter gives an estimate of 20–25 feet tall.

11

Benefit calculations were done by scientist Robert Leverett (carbon) and MyTree software (stormwater

& energy) using the species, dimension (13" DBH), and location of the tree in relation to each house.

npj |

urban sustainability Article

Published in par tnership with RMIT University

https://doi.org/10.1038/s42949-024-00150-3

Current inequality and future potential of

US urban tree cover for reducing heat-

related health impacts

Check for updates

Robert I. McDonald

1,2,3

,TanushreeBiswas

4

, T. C. Chakraborty

5

, Timm Kroeger

6

,

Susan C. Cook-Patton

7

&JosephE.Fargione

8

Excessive heat is a major and growing risk for urban residents. Here, we estimate the inequa lity in

summertime heat-related mortality, morbidity, and elect ricity consumption across 5723 US

municipalities and other places, housing 180 million people during the 2020 cen sus. On average, trees

in majority non-Hispanic white neighborhoods cool the air by 0.19 ± 0.05 °C more than in POC

neighborhoods, leading annually to trees in white neighborhoods helping prevent 190 ± 139 more

deaths, 30,131 ± 10,406 more doctors’ visits, and 1.4 ± 0.5 terawatt-hours (TWhr) more electricity

consumption than in POC neighborhoods. We estimate that an ambitious reforestation program

would require 1.2 billion trees and reduce population-weighted average summer tempera tures by an

additional 0.38 ± 0.01 °C. This temperature reduction would reduce annual heat-related mortality by

an additional 464 ± 89 people, annual heat-relate d morbidity by 80,785 ± 6110 cases, and annual

electricity consumption by 4.3 ± 0.2 TWhr, while increasing annual carbon sequestration in trees by

23.7 ± 1.2 MtCO

2

eyr

−1

and decreasing annual electricity-related GHG emissions by

2.1 ± 0.2 MtCO

2

eyr

−1

. The total economic value of these benefits, including the value of carbon

sequestration and avoided emissions, would be USD 9.6 ± 0.5 billion, although in many

neighborhoods the cost of planting and maintaining trees to achieve this increased tree cover would

exceed these benefits. The exception is areas that currently have less tree cover, often the majority

POC, which tend to have a relatively high return on investment from tree planting.

Climate change is already here, with myriad impacts on human health and

society that are projected to increase over time as changes intensify

1,2

.One

important impact of climate change is increased risk from excessive heat, as

climate change increases in average summer temperature as well as the

frequency, intensity, and duration of heat waves. Already, in an average year,

heat stress kills roughly 6100 Americans and 356,000 people globally

3

,as

measured by epidemiological studies of excess deaths due to high tem-

peratures. Note that the statistical estimate of excess deaths is considered

more accurate than medical system records that often list another cause of

death

4

(e.g., the US Centers for Disease Control estimated an annual average

of 1200 deaths from heat stress in the US based on medical records

5

). Higher

air temperatures increase mortality and morbidity by causing heat stroke

and exhaustion and by exacerbating existing cardiovascular, pulmonary,

and renal diseases

6,7

. Summer temperatures in the United States (US), the

focus of this paper, are increasing, and heat waves are becoming more

frequent as climate change intensifies, with the average number of days with

a dangerous heat index (high temperature and humidity) forecast to triple

by 2050

8

.

Projected impacts on mortality are uncertain, varying among studies.

One study projected that by 2090–2099, climate change in warmer regions

of the world could increase annual heat-related mortality by 3.0% percen-

tage points to 12.7%, depending on the region and the greenhouse gas

1

The Nature Conservancy in Europe, Berlin, Germany.

2

CUNY Institute for Demographic Research, New York, NY, USA.

3

Humboldt University, Berlin, Germany.

4

Washington Program, The Nature Conservancy, 74 Wall Street, Seattle, WA 98121, USA.

5

Atmospheric, Climate, & Earth Sciences Division, Pacific Northwest

National Laboratory, 902 Battelle Blvd, Richland, WA 99354, USA.

6

Global Science, The Nature Conservancy, 4245 Fairfax Drive, Arlington, VA 22203, USA.

7

Tackle

Climate Change Program, The Nature Conservancy, 4245 Fairfax Drive, Arlington, VA 22203, USA.

8

North America Region, The Nature Conservancy, 1101 W River

Pkwy # 200, Minneapolis, MN 55415, USA.

e-mail: rob_mcdonald@tnc.org

npj Urban Sustainability | (2024) 4:18 1

1234567890():,;

1234567890():,;

Submitted by

Robin L Bergman, Town Meeting Member, Precinct 12.

(GHG) emissions scenario used. In the US specifically, annual heat-related

mortality is forecast to increase by 3.5% percentage points under the high

emissions scenario (RCP8.5)

9

. By contrast, another study in just the Eastern

US projected an additional 11,562 annual deaths by 2050, which would

represent a large increase in annual heat-related mortality

10

.Whilethesetwo

examplesfromtheliteratureillustratethewiderangeofprojectionsofhealth

impacts from increased heat waves, there is general agreement in the lit-

erature that climate change in the US will make heat waves more frequent

and intense, increasing mortality and morbidity

11,12

.

Trees can reduce solar radiation hitting surfaces such as asphalt and

concrete, reducing temporary heat storage in these materials, which would

later be emitted as thermal radiation. Trees also lose water to transpiration as

they respire, and this phase change of water from liquid to gas increases

latent heat flux

13

. The net effect of trees is, therefore, to reduce ambient air

temperature, particularly during the daytime. The effect of trees is fairly

local, reducing air temperatures primarily within a few hundred meters

horizontal distance from the tree canopy, although this distance varies with

factors such as the size of the canopy patch

14

,windspeed,andrelative

humidity

7

. It is important to note that while air temperature is correlated

with health impacts, there are other heat stress metrics that take into account

factors that are also important for thermal comfort, like humidity

15

and

direct solar radiation

16,17

.

Heat action planning has been used by governments at many scales to

reduce the risk to vulnerable populations. Common components of heat

action planning include the creation of early warning systems, cooling

centers, and response plans by medical institutions

18,19

. One part of heat

action planning can be actions to reduce ambient outdoor air

temperature

20,21

, and many city governments have begun to consider the

additional role trees can play to reduce ambient outdoor air

temperatures

22–28

. Moreover, trees already provide significant heat-

reduction benefits

7

. One study in the United States estimated that without

urban tree canopy, annual mortality would have been 1200 deaths greater

than at present

29

.

Research on urban tree cover shows that its distribution is quite

unequal. One way to measure this is to compare tree canopy across US

Census blocks, for which we have associated socioeconomic data. In most

US cities, low-income or people-of-color (POC) neighborhoods have less

tree cover than high-income or non-Hispanic white (hereafter simply

“white”) neighbo rhoods

30–39

. One national survey of 5723 municipalities in

theUS(thesamesetofmunicipalities used in this study) found that in 92%

of communities, low-income blocks have less tree cover than high-income

blocks, with low-income blocks on average having 15.2% less tree cover and

being 1.5 °C hotter (summer surface temperature) than high-income

blocks

40

. Multiple other studies show similar results using a variety of dif-

ferent methodologies, consistently findingthatinmostUScities,lower

income and minority populations live in neighborhoods with higher sum-

mer surface temperatures

41,42

.

Beyond the health impacts, heat has other impacts on society. For

instance, Santamouris and others

43

found an average increase of 0.45–4.6%

in peak electricity load for each 1 °C increase in air temperature. Trees near

buildings can be one way to decrease electricity use, among other strategies

such as implementing energy efficiency programs, reducing building albedo

(“cool roofs”), and creating incentives to conserve electricity

44

.Treecover

reduces ambient air temperature and shades buildings from solar insolation,

reducing the energy demand for indoor cooling from 2.3% to 90%,

depending on the study

45

. This avoids electricity consumption and also

avoids greenhouse gas emissions from electricity generation.

Urban trees also sequester and store carbon

46

, among other ecosystem

services

47

. Urban trees are one kind of natural climate solution (NCS),

defined as conservation, restoration, or management actions that increase

carbon sequestration or avoid greenhouse gas emissions

48

.Inthisstudy,we

use estimates of net carbon sequestration by urban trees, accounting for both

sequestration and emissions associated with tree mortality. Planting new

trees in urban areas (afforestation or reforestation depending on site history,

but for simplicity referred to as reforestation in this paper) will lead to net

carbon storage as the trees grow, even after accounting for GHG emissions

associated with tree mortality

49,50

.

Previous estimates of the urban reforestation potential in the United

States were designed to estimate the maximum carbon sequestration

potential. For instance, Fargione et al.

46

assumed that essentially all land in

urban areas not currently developed (e.g., impervious surfaces, buildings) or

under other land use (e.g., agriculture, sports fields, or golf courses) could be

available for tree planting, estimating that (19.17–30.15 95% CI) 23.3 Mt

CO

2

yr

−1

of carbon sequestration was possible, around 1.9% of the total NCS

potential in the US

46

.Morerecently,Cook-Pattonetal.

51

modeled fully

planting all low-density open space in urban areas across the contiguous US

where forest naturally occurs and estimated that 52.5 Mt CO

2

yr

−1

of carbon

sequestration was possible.

However, only a fraction of the potentially available urban open area is

likely to be plantable due to municipal codes and land-use regulations,

landowner preferences, and conflicts with other land uses that require sparse

vegetation like lawns. See Treglia et al. for an example of how these con-

straints may be considered in one municipality, New York City, with

abundant geospatial data

52

. Moreover, past efforts at estimating the NCS

potential of urban reforestation have often been based on 30 m Landsat-

derived tree cover data. These coarser datasets miss many single tree

canopies in urban areas and can significantly underestimate current tree

cover

53

, particularly in landscapes with <30% tree cover

54

. Underestimation

of the current urban forest canopy may lead to an overestimation of

reforestation potential.

While there have been studies of individual cities or sets of cities

22–27

,

here we conduct a quantitative national prospective study for the US, of a

large sample of thousands of cities, to estimate the potential that increases in

urban tree cover have to reduce mortality, morbidity, and electricity con-

sumption, examining how the potential of trees for heat-risk reduction

varies across gradients of race/ethnicity. Understanding the potential of

urban trees to serve as an adaptation to more frequent and intense heat

waves is particularly important at this moment, when there is substantial

investment in NCS as countries plan to achieve their Nationally Determined

Contribution (NDC) commitments under the Paris Climate Accords

55

,and

the US has committed to increased funding for climate-related forestry

programs as part of the Inflation Reduction Act

56

. Similarly, substantial

investment in climate adaptation is likely in the next decade by entities like

the Green Climate Fund

57

. Well-targeted, cost-effective investment in

nature-based solutions for climate mitigation and adaptation is difficult

without spatially detailed data on reforestation potential and need

58

.

In this paper, we use a high-resolution (2 m) tree cover map for all

100 US urbanized areas >500 km

2,

which contain 5723 municipalities or

other Census-designated places that housed 180 million people in 2020

(68% of the people who live in urbanized areas in the United States or 55%

of the national population)

40

. For this large sample of 5723 municipalities

(of which 50.6%, or 91 million people, represent majority white neigh-

borhoods, 49.4%, or 89 million people, represent majority POC neigh-

borhoods), we estimate reforestation potential at the Census block-level,

accounting for variation in impervious cover among blocks and assuming

that reforestation is only likely up to levels commonly seen in other blocks

of similar impervious surface cover. We estimate the change in air tem-

perature due to tree planting and the resultant reductions in human

mortality, morbidity, and electricity consumption. In addition, we esti-

mate the carbon mitigation potential achievable through this large-scale

urban reforestation scenario. Across our analyses, we propagate statistical

uncertainty to describe the precision of our estimates. The major goals of

this paper are to:

•

Quantify the inequality in the protective value trees currently provide

by reducing mortality and morbidity across income and race/ethnicity.

•

Estimate, for a variety of planting scenarios, the increase in urban tree

canopy, the number of trees planted, the net carbon sequestration, and

the costs. For each urban reforestation scenario, quantify the likely

reduction in summer air temperatures, as well as the associated

reduction in mortality, morbidity, and electricity consumption.

https://doi.org/10.1038/s42949-024-00150-3 Article

npj Urban Sustainability | (2024) 4:18 2

•

Examine the return-on-investment (ROI) curve of urban reforesta-

tion’s effect on avoided mortality, morbidity, and electricity consump-

tion, to test whether ROI varies systematically with neighborhood

income or race/ethnicity.

Results

Current national benefits

On average, across the country, neighborhoods with a majority of people of

color (POC) have 11% less tree canopy cover and 14% more impervious

surface than white neighborhoods (Table 1; unless otherwise stated, we

present in this paper differences expressed as percentage points). We esti-

mate that the current tree canopy, on average, reduces air temperatures by a

population-weighted 1.01 ± 0.03 °C in white neighborhoods compared to

0.82 ± 0.03 °C in POC neighborhoods.

We estimate that in the majority of white neighborhoods, trees

annually help avoid 632 ± 100 deaths. The annual economic value of that

avoided mortality is USD 4.5 billion (Fig. 1). In contrast, we estimate that

even though the total population in each race/ethnicity type is of relatively

equal size (91 million in white neighborhoo ds vs. 89 million in POC

neighborhoods), in majority POC neighborhoods trees help avoid 442 ± 97

deaths annually, an annual economic value of avoided mortality of USD 3.1

billion. This differential in benefits extends to other types of benefits.

Morbidity is avoided because tree cover is 30,131 ± 10,406 greater in white

neighborhoods than in POC neighborhoods (Table 1). Similarly, these trees

reduce total electricity consumption in white neighborhoods by

1.4 ± 0.5 TWhr more than it does total consumption in POC neighbor-

hoods (Table 1).

Patterns within urbanized areas

There is significant variation in current tree cover within urbanized areas,

which leads to variation in the potential for tree cover increases to provide

benefits. To demonstrate typical patterns, we provide a case study for

Washington, DC, in Fig. 2. At a neighborhood scale, there is significant

variation in tree cover, driven by differences in settlement density and urban

form, as well as the existence of patches of forests within protected areas or

otherwise undevelopable sites (Fig. 2a). Neighborhoods in DC that pre-

dominately house people of color have higher population density (average of

4828 people km

−2

), and are more often centrally located, than neighbor-

hoods that are predominately comprised of non-Hispanic white people,

which are more often suburban or exurban (3962 people km

−2

). On average,

tree cover is 12% lower in POC neighborhoods than in white neighborhoods

due to the correlation with population density (and hence impervious

surface cover)

40

as well as other historical factors, such as redlining

59

.

Across the entire Washington, DC urbanized area, there is an overall

gradient in summer land surface temperature (Fig. 2b), with less densely

populated suburbs and exurbs having lower land surface temperature than

dense urban core neighborhoods, due to their lower impervious surface

cover and greater tree cover. This pattern means that the greatest reduction

in heat risk from additional planting, measured as annual avoided deaths per

million people (M), occurs in denser, often POC, neighborhoods (Fig. 2c).

However, the tree planting potential, measured in number of additional

stems or in potential carbon sequestration, is greatest in suburban and

exurban areas (Fig. 2d), which have low cover of impervious surfaces and

hence more space for planting, even though those are the neighborhoods

that already have disproportionately high tree cover. Thus, unless the total

plantable area is increased in POC neighborhoods, benefits from increased

tree cover are distributed unequally.

Patterns among urbanized areas

There is also variation among urbanized areas in the amount of tree cover

and the protective benefits trees are providing from avoided heat-related

mortality and morbidity. The largest benefitintermsofdeathsavoidedisin

the urbanized areas with the greatest population, such as those centered

around New York City and Los Angeles (Fig. 3). This is not surprising since

there is a greater population potentially at risk from heat in the largest

Table 1 | Inequality in tree canopy cover, air temperature, heat-related mortality, morbidity, and electricity use

Race/ethnicity Tree canopy

cover (%)

Impervious sur-

face (%)

Median reduction in summer air temperature

due to trees (°C)

Reduction due to trees in annual

mortality

Reduction due to trees in annual

morbidity

Reduction due to trees in annual

electricity (TWhr)

Majority non-

Hispanic white

35 42 1.01 ± 0.03 632 ± 100 114,936 ± 7444 6.2 ± 0.3

Majority POC 24 56 0.82 ± 0.03 442 ± 97 84,805 ± 7271 4.8 ± 0.3

The median reduction in air temperature and total reduction in impacts is shown relative to a hypothetical case with no urban tree canopy cover to quantify the ecosystem service that urban tree canopycover provides to heat-health. Neighborhoods are divided into those that

are predominately non-Hispanic white or those that are predominately peopleof color (POC). Confidence intervals shown are calculated fromregression analysis and error propagation, see “Methods” sectionfor details. Tree coverand impervious surface cover are measured

using remote sensing, and so the mean for our sample of cities is known with high precision (<0.1%), see the Methods for a discussion of the accuracy of classified remotely sensed imagery. The totals presented are for the 5723 municipalities in our sample.

https://doi.org/10.1038/s42949-024-00150-3 Article

npj Urban Sustainability | (2024) 4:18 3

urbanized areas, and so in absolute terms the number of avoided deaths due

to trees is greatest in these urbanized areas. Other factors matter as well,

however. Urbanized areas that are densely settled, with a lot of impervious

surface cover, and yet have significant tree cover will have a relatively high

number of avoided deaths due to trees, all else being equal. For instance,

Boston (population 4.5 million) has both high impervious surface cover and

relatively high population density, and trees currently help avoid an esti-

mated 54 deaths annually. In comparison, Atlanta (population 5.1 million)

has lower impervious surface cover and lower population density, and trees

currently help avoid an estimated 32 deaths annually.

However, the protective benefits trees currently provide in terms of

avoided mortality are unequally distributed with respect to race/ethnicity.

To account for differences in population between majority POC and

majority white neighborhoods, we calculated the protective rate, the number

of heat-related deaths avoided annually due to trees per million people. The

protectiverateisgenerallylowerinmajorityPOCneighborhoodsthanin

majority white neighborh oods (urbanized areas with yellow to red colors in

Fig. 3). The inequality in protective rate is greatest in the Northeast of the US,

in the so-called Northeast Corridor stretching from Washington (DC) to

Boston (MA). These cities have substantial inequality in tree cover and

surface temperature among neighborhoods, which leads to a substantial

inequality in air temperature and the protective benefits of trees in reducing

heat-related mortality

40

. Northeastern US cities are characterized by dense

city centers

60

, often formed prior to the widespread availability of the

automobile

61

, which often contain many of the POC neighborhoods and

lower-density suburbs and exurbs that are often majority white

40

.Con-

versely, there are urbanized regions where the protective rate is greater in

POC than in white neighborhoods, such as Southeastern US cities like

Atlanta

40

. In these cities, most neighborhoods are at lower population

densities and there is lower inequality in tree cover and surface temperature

between neighborhoods

40

.

National benefits of increased urban tree cover

Reforestation in our sample cities to an ambitious reforestation scenario

(bringing each block up to the 90th percentile observed in each impervious

surface category in each city; see methods section for details) could reduce

population-weighted mean summer air temperatures by 0.38 ± 0.014 °C

(Table 2), with reductions up to 1.8 °C possible for specific neighborhoods.

We estimate that this ambitious reforestation scenario would involve the

addition of 1.2 billion trees and would reduce annual heat-related mortality

by 464 ± 83 people, in addition to the current benefits that trees provide.

There is roughly similar potential to reduce mortality in white and POC

neighborhoods under the ambitious planting scenario (Fig. 1).

The ambitious reforestation scenario would deliver other benefits as

well. It would reduce annual heat-related morbidity by an additional

80,785 ± 6110 cases and would increase net carbon sequestration in trees by

23.7 ± 0.2 MtCO

2

eyr

−1

above the status quo (Table 2). It would also reduce

annual electricity consumption by 4.3 ± 0.2 TWhr yr

−1

, avoiding electricity-

related GHG emissions annually by 2.1 ± 0.1 MtCO

2

eyr

−1

.Note,however,

that even lower levels of tree canopy cover increases could still deliver

substantial benefits. For instance, aiming for a nominal 5% increase in urban

tree canopy cover where feasible would reduce annual morbidity by

23,614 ± 1832 cases (29% of the morbidity reduction achieved under the

ambitious planting scenario).

Spatial pattern of benefits of additional tree planting

Under the ambitious reforestation scenario, the largest reforestation

potential, in terms of trees planted or additional net carbon sequestration,

occurs in the urbanized areas with the largest geographic extent (Fig. 4). For

instance, the Chicago and New York City urban areas are among the largest

in extent in the United States, and both have an increase in net carbon

sequestration of more than 1 MtCO

2

eyr

-1

under the ambitious reforestation

scenario. However, the potential additional net carbon sequestration varies

by climate, with arid regions generally having lower feasible tree canopy

targets due to natural limits on tree canopy in arid climates (see the

“Methods” section for details), and humid regions in naturally forested

biomes having higher feasible tree canopy targets. Similarly, the potential

additional net carbon sequestration also varies by population density, with

greater reforestation possible in lower population density areas, which have

less impervious surface and hence higher proportions of plantable area. For

the same reason, within urban areas, the greatest reforestation potential is

often in suburban neighborhoods that are often majority white (e.g., Fig. 2,

lower right). If reforestation in urbanized areas were to be prioritized solely

based on potential additional carbon sequestration, then suburban neigh-

borhoods would often have priority.

However, patterns are different with regard to tree canopy cover’srole

in reducing mortality and morbidity. The greatest increase in protective rate

(avoided annual deaths per million people) under the ambitious reforesta-

tion scenario occurs in the northeastern and western US (Fig. 4). These

urbanized areas are relatively dense, with a high proportion of impervious

surfaces. The addition of new tree canopy cover thus tends to shade

impervious surfaces, reducing the urban heat island effect more sub-

stantially than if tree canopy shaded grass or other sparse vegetation.

Moreover, these urbanized areas are in naturally forested biomes, so larger

increases in tree cover are possible than in arid areas, where water limitations

might limit tree canopy cover expansion.

For avoided mortality, we defined the return on investment (ROI) in

tree planting as avoided annual heat-related mortality per tree planted.

Within urbanized areas, the greatest increase in ROI under the Ambitious

reforestation scenario is in dense neighborhoods, often located in city

centers and often majority POC neighborhoods (e.g., Fig. 2,lowerright).

Thus, if reforestation in urbanized areas was to be prioritized solely based

upon the potential protective health benefits of trees, then dense urban

neighborhoods would have priority. For this reason, on average across the

US, the ROI of tree planting is greater in majority POC neighborhoods than

in majority white neighborhoods (Fig. 5, top panel). This is true for any level

of planting ambition, from the 5% nominal target to the ambitious refor-

estation scenario. For instance, the 5% nominal target for majority POC

neighborhoods would avoid more mortality than the 5% nominal target for

majority white neighborhoods, with only half the number of trees as in white

neighborhoods. It is worth stressing, however, that there is significant var-

iation among neighborhoods in ROI. If we examine the mortality reduction

Fig. 1 | Mortality and urban tree canopy cover for our sample of 5723 US

municipalities. Shown are the current annual reduction in mortality due to trees, as

well as the additional reduction in mortality possible under the ambitious planting

scenario. This quantity can be expressed not just in the annual lives saved (left axis)

but also in the estimated annual value of avoiding this mortality (right axis).

https://doi.org/10.1038/s42949-024-00150-3 Article

npj Urban Sustainability | (2024) 4:18 4

benefits of the 5% of neighborhoods with the highest ROI (Fig. 5, bottom

panel), we find the same general pattern of POC neighborhoods having

higher ROI than white neighborhoods, but the number of deaths reduced

per million trees planted is an order of magnitude greater in these high

priority blocks than the overall national average.

Economic benefits of tree canopy increase

Total economic benefits of additional tree cover are shown in Table 3 for

the 5% nominal target and the ambitious reforestation scenario. Benefits

valued in this study are avoided mortality, avoided morbidity, avoided

electricity consumption, and avoided damag es resulting from higher

atmospheric carbon dioxide conc entrations, the latter due to avoided

GHG emissions from avoided electricity generation and from increased

carbon sequestration by the additional tree cover. Under the Ambitious

tree planting scenario, USD 9.6 ± 0.4 billion in annual benefits would

accrue. The 5% target tree planting scenario delivers fewer annual

benefits, USD 2.5 ± 0.1 billion. The largest share of economic value is

contributed by the carbon sequestered in the trees, followed closely by

avoided heat-related mortality. Avoided electricity consumption, avoi-

ded GHG emissions from electr icity consumption, and avoided heat-

related morbidity all have meaningful but relatively small er total eco-

nomic values.

The economic benefits of the Ambitious tree canopy scenario are split

relatively equally (Table 4) between POC neighborhoods (USD 3.9 ± 0.2

billion) and white neighborhoods (USD 5.0 ± 0.2 billion). Annual tree

planting and maintenance costs would exceed annual benefits for both POC

neighborhoods(costsofUSD19.5±4.4billion,ReturnonInvestment,

ROI = 0.20) and, especially, white neighborhoods (USD 40.5 ± 9.4 billion,

ROI = 0.12). However, if tree canopy increases were targeted to the neigh-

borhoods with the highest ROI, the situation looks different, with the annual

economic benefits roughly equal to the costs of annual tree planting and

maintenance. Indeed, a 5% nominal target for additional tree planting in

High-ROI POC neighborhoods is estimated to provide USD 32 ± 4 million

in benefits but to cost only USD 29 ± 7 million in planting and maintenance,

an ROI of 1.12.

Discussion

Our results show that trees provide substantial benefits in terms of avoided

heat-related mortality and morbidity in the US but that inequality in urban

tree canopy cover leads to inequality in these protective benefits. In our

sample of 5723 municipalities, we estimate that, annually, trees in white

neighborhood s help avoid 190 more deaths, 30,131 more doctors’ visits, and

1.4 TWh more electricity consumption than in POC neighborhoods,

despite the nearly equal number of people in white and POC

Fig. 2 | Inequality in the Washington, DC

urbanized area. a Zoom-in of a purple rectangle

shown in other panels. Tree cover varies sig-

nificantly from block to block. Neighborhoods that

are predominately people of color (POC) are shown

in gray and are primarily east of Rock Creek Park,

while the majority of non-Hispanic white neigh-

borhoods are west of the park. Note that Rock Creek

Park itself has no residents. b Land surface tem-

perature in summer by census block, as observed by

satellite imagery. For reference, the extent of the

zoomed-in panel in the upper left is outlined in

purple. c The potential reduction in heat risk due to

additional tree planting (increase in protective rate,

in avoided annual deaths per million people).

d Potential additional carbon sequestered due to

new tree planting within census blocks. While tree

planting in the city center occurs at higher popula-

tion densities and so benefits more people with heat

reduction, the greatest number of potential trees can

be planted in suburban areas.

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npj Urban Sustainability | (2024) 4:18 5

neighborhoods. While POC neighborhoodshavelesstreecoverthanwhite

neighborho ods, their higher impervious surface area means that tree cover is

more often shading impervious surfaces and thus provides relatively larger

cooling benefits per unit canopy area. Climate change is expected to increase

the frequency and intensity of heat waves, increasing mortality and

morbidity

9

. This will likely elevate the public health importance of heat

action planning in the coming decades. We believe that heat action planning

should consider outdoor temperature, its public health impacts, the equity

of these impacts, and strategies to mitigate these impacts and their inequity,

such as increased investment in tree planting and maintenance.

We found that an ambitious reforestation scenario in these 5723

municipalities would annually save an additional 464 lives otherwise

threatened by heat, equal to around 8% of current total heat-related deaths

in the US

3

. It should be noted that this is just excess mortality associated with

heat, and other studies that looked at health benefits from all pathways often

show larger benefits. For instance, a study in Philadelphia estimated that an

ambitious increase in tree canopy cover could avoid annually 298–618

premature adult deaths, due to all causes

62

. A similar study that looked at a

sample of almost one thousand European cities estimated that an increase in

NDVI to a level recommended by the World Health Organization could

reduce mortality from all causes by 32,000–64,000 deaths annually

63

.

Fig. 3 | Protective value of urban tree canopy for

large US urbanized areas. The size of the circle is

proportional to the avoided annual mortality due to

urban tree canopy in the urbanized area. The color of

the circle indicates the difference in the protective

rate (annual deaths avoided due to urban tree

canopy per million population) between people-of-

color (POC) majority neighborhoods and non-

Hispanic white majority neighborhoods. Negative

values indicate that POC neighborhoods have a

lower protective rate than white neighborhoods.

Table 2 | Estimated potential of reforestation in urbanized areas to provide benefits to human well-being for increases in tree

canopy cover in 5% increments above current tree canopy

Target (%) Reduction in median summer air temperature

due to additional tree cover (°C)

Additional annual

avoided mortality

Additional annual

avoided morbidity

Additional annual avoided elec-

tricity consumption (TWhr)

Additional carbon

storage (MtCO

2

e)

5 0.11 ± 0.004 138 ± 25 23,614 ± 1832 1.2 ± 0.1 5.6 ± 0.1

10 0.19 ± 0.007 250 ± 44 42,855 ± 3280 2.2 ± 0.1 10.4 ± 0.1

15 0.26 ± 0.010 331 ± 59 56,993 ± 4340 3.0 ± 0.2 14.2 ± 0.2

20 0.31 ± 0.011 386 ± 69 66,535 ± 5064 3.5 ± 0.2 17.1 ± 0.2

25 0.34 ± 0.013 420 ± 75 72,626 ± 5524 3.8 ± 0.2 19.2 ± 0.2

30 0.36 ± 0.013 440 ± 79 76,260 ± 5792 4.0 ± 0.2 20.6 ± 0.2

35 0.37 ± 0.014 451 ± 81 78,326 ± 5938 4.1 ± 0.2 21.7 ± 0.2

40 0.37 ± 0.014 457 ± 82 79,468 ± 6017 4.2 ± 0.2 22.4 ± 0.2

Ambitious

scenario

0.38 ± 0.014 464 ± 83 80,785 ± 6110 4.3 ± 0.2 23.7 ± 0.2

Shown are the annual reduction in mortality, morbidity, and electricity during heat waves, as well as the additional annual carbon storage. Error ranges show the 95% confidence interval of the estimate. The

ambitious target scenario shows hitting the maximum possible planting, given our assumptions, across the 5723 municipalities in our sample. See text for details.

https://doi.org/10.1038/s42949-024-00150-3 Article

npj Urban Sustainability | (2024) 4:18 6

A more direct comparison to our work is a recent paper in Europe,

which estimated that tree planting up to a 30% target in all neighborhoods

would reduce European deaths from heat by around 33%

64

. One potential

reason for the difference between our study and this study is the differences

in how targets were set. Our targets considered physical and social limita-

tions in dense neighborhoods to tree planting while the European study did

not. Another potential reason is simply that European cities are, on average

denser with more impervious surfaces than American cities, so tree cover is

more likely to shade impervious surfaces, resulting in more significant heat

reduction benefits.

Treesprovideotherbenefits as well. We estimated that our ambitious

tree restoration scenario could reduce annual heat-related morbidity by an

Fig. 4 | Ambitious reforestation potential for large

US urbanized areas. The size of the circles is pro-

portional to the potential additional carbon storage

under the ambitious reforestation scenario. The

color of the circle indicates the increase in the pro-

tective rate (annual deaths avoided due to urban tree

canopy per million population) under the same

reforestation scenario.

Fig. 5 | Mortality reductions as a function of

planting ambition. Blocks are classified as either

majority people of color (POC) or majority non-

Hispanic white. For each census block, additions of

tree cover of 5%, 10%, etc., up to an Ambitious

Scenario, the maximum possible given our

assumptions. Each point along the curve thus

represents an additional 5% increase in tree cover,

wherever this is possible. Error bars are the 95%

confidence interval of reduction in annual heat-

related mortality. a Results for all census blocks. For

instance, planting 200 million additional trees in

white blocks for a 5% increase in tree cover reduces

annual heat-related mortality by an additional 62

lives. b Results for high ROI census blocks, defined

as in the top 5% of ROI (see methods for details).

Note the very different scales in the two plots. For

instance, planting 0.4 million additional trees in

white blocks for a 5% increase in tree cover reduces

annual heat-related mortality by an additional

2 lives.

https://doi.org/10.1038/s42949-024-00150-3 Article

npj Urban Sustainability | (2024) 4:18 7

additional 80,785 cases, increase carbon sequestration in trees by

23.7 MtCO

2

eyr

−1

, and decrease electricity-related GHG emissions by

2.1 ± 0.1 MtCO

2

eyr

−1

. While expanding the urban forest by the additional

1.2 billion trees needed under this Ambitious Scenario would be a large

financial investment, we want to emphasize that the heat-health benefits

would likely offset a significant fraction of the costs. Indeed, targeted actions

to increase tree canopy in high-ROI neighborhoods can result in benefits

that equal or exceed tree planting and maintenance costs. Trees, of course,

provide many other benefits not modeled in this paper, both to overall

health

65

and to human well-being

47

, and consideration of these other ben-

efits would increase the total estimated benefits and ROI of increased urban

tree canopy. Moreover, while our methods allowed for spatial variation in

the impact of tree canopy on LST and air temperature by climate zone and

region, as well as accounted for block-level variation in impervious surface

cover, they do not allow for variation in model parameters with a city,

potentially underestimating benefits at some sites within a city (see the

Caveat and Limitations portion of the Methods section for a more detailed

discussion).

There is greater return-on-investment in tree cover increases in

majority POC neighbor hood s for heal th benefits, relative to majority-white

neighborhoods. That is, for a given number of trees planted, urban greening

projects in POC neighborhoods will, on average, have a greater reduction in

mortality and morbidity than projects in majority-white neighborhoods.

Similarly, tree planting projects that target neighborhoods with dis-

proportionately low tree cover will deliver disproportionate benefits to POC

households because POC households tend to live in neighborhoods with less

tree cover. Given the high tree cover inequality in the US with respect to

race/ethnicity and income

40

,programssuchastheInflation Reduction Act

that aim to reduce heat-health impacts will have to address tree cover

inequality, either explicitly or implicitly, if they are to target trees where they

will have the greatest benefit. Of course, there are other reasons to consider

race/ethnicity and socioeconomic status in heat action planning, as these

populations are more vulnerable to heat hazards due to, among other things,

lower access to air conditioning

66

.

The estimated net carbon sequestration potential of reforestation in

our sample of 5723 municipalities, under our Ambitious Scenario, is

23.7 MtCO

2

eyr

−1

.ThisislowerthanCook-Pattonetal.’sestimate

51

of

52.5 MtCO

2

yr

−1

and higher than Fargione et al.’sestimate

46

of 23.3 Mt

CO

2

yr

−1

, both of whose estimates cover all US urban areas (although the

two studies used different definitions of what is urban). Another difference is

that our definition of what could potentially be planted is more restrictive

than in Cook-Patton et al.

51

due to our consideration of both physical and

social barriers to planting, which may further explain why our estimate is

lower. Our estimation of reforestation potential in urbanized areas for

sequestration is about 2.6% of the overall US potential for NCS across all

natural climate solutions pathways in the US identified by Fargione et al.

46

.

Table 3 | Economic benefits of tree planting for 5723 munici-

palities in the United States

Variable Economic value (annual

millions USD) at 5%

target

Economic value (annual mil-

lions USD) at ambitious sce-

nario target

Avoided mortality 982 ± 101 3299 ± 339

Avoided morbidity 32 ± 2 108 ± 7

Avoided electricity

consumption

204 ± 14 703 ± 46

Carbon

sequestration

1221 ± 65 5166 ± 287

Avoided GHG

emissions from

electricity

98 ± 6 344 ± 22

Total 2536 ± 121 9620 ± 447

Shown are the results for the 5% planting scenario and the ambitious planting scenario.

Table 4 | Economic benefits and costs of additional tree planting by race/ethnicity of census blocks

Target Priority Economic bene fits (annual million

USD) POC

Economic bene fits (annual million

USD) White

Cost of planting and maintenance (annual

million USD) POC

Cost of planting and maintenance (annual

million USD) White

ROI

POC

ROI

White

5% increase in

tree cover

High ROI 32 ± 4 18 ± 2 29 ± 7 19 ± 4 1.12 0.95

5% increase in

tree cover

Other

blocks

1053 ± 64 1283 ± 49 4696 ± 1084 9794 ± 2261 0.22 0.13

All blocks 1086 ± 64 1301 ± 49 4725 ± 1084 9813 ± 2261 0.23 0.13

Ambitious scenario High ROI 81 ± 10 45 ± 5 86 ± 20 58 ± 13 0.94 0.77

Ambitious scenario Other

blocks

3805 ± 222 4967 ± 193 19,464 ± 4493 40,517 ± 9352 0.20 0.12

All blocks 3886 ± 222 5012 ± 193 19,550 ± 4493 40,575 ± 9352 0.20 0.12

Shown is data for tree planting only in populated census blocks where human well-being benefits occur. Data are shown for neighborhoods that are majority people of color (POC) as well as majority non-Hispanic white (White). Census blocks are split into two priority

categories, high return on investment (ROI) blocks and other blocks. High ROI is defined as neighborhoods in the top 5% of ROI.

https://doi.org/10.1038/s42949-024-00150-3 Article

npj Urban Sustainability | (2024) 4:18 8

For context, urbanized areas occupy about 2.8% of the total land area of the

US

67

. We also estimated that avoided electricity consumption due to addi-

tional trees would decrease electricity-related GHG emissions by

2.1 MtCO

2

eyr

−1

, for a total net climate mitigation potential of our ambi-

tious reforestation scenario of 25.8 MtCO

2

eyr

−1

, a modest amount of car-

bon relative to the entire US carbon budget, but not insignificant, being the

annual equivalent emissions of 5.7 million gasoline-powered cars

68

.

We caution that there is, to some extent, a spatial disconnect between

optimal sites for climate mitigation and climate adaptation. Although the

carbon benefit per additional tree did not vary in our study and is expected to

be the same from exurbs to downtown, the overall potential for carbon

sequestration is greater in neighborhoods with more plantable areas (i.e.,

fewerimpervioussurfaces).Thisismorefrequentlythecaseinthesuburbs

or exurbs than in the city center. Patterns for avoided electricity con-

sumption from space cooling (and hence avoided GHG emissions) are the

opposite, being greater in densely populated blocks, but are an order of

magnitudesmallerthancarbonsequestrationintermsofMtCO

2

yr

−1

.Heat-

related health benefits also are greatest when trees are planted close to where

people live and work, which is more frequently the case in city centers. We

note that greater heat-related mitigation benefits in more densely populated

neighborhoods would likely hold for other tree-provided ecosystem services

that need to be generated close to people, such as air pollution mitigation,

stormwater mitigation, and the benefits to physical and mental health from

exposure to nature

47

. The varying spatial scales of different ecosystem ser-

vices lead to different optimal places for tree planting

69

, with heat-reduction

benefits needing to be provided within 100 m or so of people

7

.Carbon

sequestration is, on the other hand, essentially global since the atmosphere is

well-mixed and not restricted to locations near people. We stress, however,

that this spatial disconnect between heat mitigation and carbon mitigation

benefits is only partial, and sites can be found that have good returns for

both. If both benefits are important, planners would do well to consider the

joint return on investments from both benefits when making decisions

about where to plant.

Realizing the climate mitigation and adaptation benefits of the

reforestation scenarios considered in this paper requires overcoming the

“wrong pocket problem”

70

. This problem occurs when the per son or

institution that pays for an action is not the one benefitin g from that

action. Urban forestry agencies in municipalities do not generally have

climate mitig ation or adaptation as part of their mission, so while they

must pay for increased tree planting and mainte nance, they may not see

abenefit in terms of their mission. Convers ely, health agencies see heat

action plannin g as part of their mission and would welcome the mor-

tality and morbidity reductions of increased tr ee planting, but many

have neither the capacity nor the budg etary authority to plant trees.

Regulations, inc entives, or investments created to increase the provision

of ecosystem services can give value to the se benefits and solve the wrong

pocket problem by creating an incentive for different institutions to

work together.

Investment at a higher level of government by agencies that have

climate mitigation or climate adaptation as part of their mission can

overcome the sometimes narrower goals and capacities of municipal

forestry offices. To the extent that natural climate solutions are considered

as part of federal policies to mitigate and adapt to climate change, refor-

estation in urbanized areas may be an investment that generates relatively

modest carbon sequestration but delivers significant local climate adap-

tation benefits

71

. This may be particularly important in low-income

neighborhoods, which generally have less tree cover and are hotter than

high-income neighborhoods

40,72

. Although there is a growing interest in

reforestation in urbanized areas to meet climate mitigation goals, our

work shows that a single-minded focus on carbon sequestration could

further exacerbate inequities by shifting planting efforts outside of his-

torically POC neighborhoods. Instead, putting equity first and making

targeted investments in tree planting and maintenance in neighborhoods

most at risk from heat could help correct some of the historic racial

inequality in tree cover.

Methods

Our analysis proceeded in four phases. First, we assembled spatial data from

multiple sources and compiled them into a common analysis unit. Second,

we developed an algorithm that would set a plausible ambitious reforesta-

tion target, given other land-use constraints. Third, we estimated the heat

mitigation-related benefits of the current tree canopy and of future planting

scenarios up to the ambitious planting scenario. The benefits evaluated were

avoided mortality, avoided morbidity, avoided electricity consumption,

avoided release of greenhouse gases from avoided electricity consumption,

and carbon sequestration in aboveground tree biomass. Fourth, we valued

these benefits in monetary terms. In the following section, we discuss each of

these phases in detail. We end the Methodolog y section with a broader

conceptual discussion of some caveats and limitations of our analysis, dis-

cussing how they might affect our results and how future research might be

able to improve upon our estimates.

Data sources

OurstudyfocusedonallUSurbanizedareaslargerthan500km

2

in extent

40

.

Urbanized area is defined by the US Census Bureau as census blocks that

exceed a population density threshold (1000 people per square mile) as well

as contiguous blocks with more than 500 people per square mile that link

nearby blocks of high-density settlements

67,73

. There are 100 urbanized areas

that are >500 km

2

in extent, housing 180 million people during the 2020

census. Each urbanized area contains many municipalities or other census-

designated places; in total, our study area contains 5723 such communities

40

.

These span the range of population sizes and densities, from a large central

city like New York City (8.4 million people) to small communities at the

edges of metro areas with just a few thousand people. These urbanized areas

also span the range of biomes in the United States, from forests to deserts to

grasslands.

The fundamental unit of analysis for our study is the census block, as

defined by the US Census Bureau. These are the finest spatial resolution data

on population and demography available in the United States, which makes

them appropriate for an analysis of the cooling effects of trees for different

demographic groups. The median censusblockinourcities(excluding

census blocks with no population, such as parks) has 57 people in it, but this

varies widely, with 80% of blocks having between 16 and 198 population.

The average person lives in a census block of 4.5 ha (80% range: 1.2–50 ha).

Importantly, census blocks are designed to be smaller in area in city centers

and thus capture more fine-grained detail and to be larger in rural areas with

lower population density.

Our tree cover maps are taken from McDonald et al.

40

,whichmapped

tree cover for the same set of 100 urbanized areas, using the boundaries of

the urbanized area set after the 2010 Census. For consistency with that

dataset, we use the same boundary file for this analysis rather than the 2020

urbanized area boundaries. Aerial photos taken during the summer growing

season at approximately 2 m resolution from the National Agricultural

Imagery Program (NAIP) were classified into a forest/non-forest grid using

Google Earth Engine’s cloud computing platform. Training data for the

supervised classification came from Nowak and Greenfield

74

,which

examined tree cover at 10,000 control points throughout urban and com-

munity areas of the United States. After creating several texture variables

and band indices (e.g., NDVI), this information, along with information

from the original spectral bands, was classified using a random forests

classifier built off the training data. Pixel-level classification accuracy varies

by biome but averages 82%.

Importantly, however, our unit of analysis for this paper was the census

block level, not the pixel level. Census blocks contain many 2 m pixels, so the

estimate of tree cover at the census block level can be more accurate than at

the pixel-level scale if any classification errors are uncorrelated. We validated

our estimates of tree cover at the census block level against an independent

dataset, the accurate high-resolution tree cover maps produced by Urban

Tree Canopy assessments

75,76

. At the block level, our estimates of tree cover

were highly linearly correlated with those of the validation dataset

(R = 0.97). On average, the median absolute block-level error of our tree

https://doi.org/10.1038/s42949-024-00150-3 Article

npj Urban Sustainability | (2024) 4:18 9

cover estimate, as compared to the validation dataset, was 6.0%. Thus, while

the pixel-level accuracy of our classification was only moderate, our esti-

mates of tree cover at the block level were highly correlated with an inde-

pendent validation dataset. Much more information on our classification

methodology is available in McDonald et al.

40

.

Our information on demography and socio-economic status comes

from the US Census Bureau’s 2020 decennial census, downloaded from the

National Historical Geographic Information System (NHGIS)

77

.Population

and its breakdown by race and ethnicity were available at the Census block

level. Details of the GIS analysis and processing of this data can be found in

McDonald et al.

40

, which used the 2010 Census but otherwise had similar

processing of Census data.

Impervious surface data were taken from the National Land Cover

Database 2019 product

78

. This dataset was derived from Landsat imagery,

and measures as a continuous variable the percent impervious surface in

each pixel. While 30 m data on the impervious surface cover is coarser than

our forest cover map, the fact that there is a continuous estimate of

imperviousness helps address sub-pixel heterogeneity.

Land surface temperature (LST) was derived from the Collection 2 LST

science product from Landsat 8 at 100 m resolution. Pixel level quality flags

were used to remove cloud-contaminated pixels, following the methodology

of Chakraborty et al.

79

. All data between 2016 and 2020 were accessed

through the Google Earth Engine platform

80

to create mean summer (June,

July, August; JJA) LST composites. This 5-year period averages over year-to-

year variability in LST while also being long enough to ensure that most

regions have usable, cloud-free pixels for which LST can be estimated.

Reforestation scenarios

Next, we defined an algorithm to set an ambitious reforestation target, given

other land-use and climatic constraints. Conceptually, we wanted to con-

sider two constraints: a physical constraint (trees can generally only be

planted on non-impervious surfaces, excluding rare landscape features such

as planter boxes) and a social/political/climatic constraint (planting trees is

constrained by numerous other considerations, including competing land

uses, landowner preferences, zoning and building codes, and climatic

conditions). For more conceptual discussion of these two constraints, see the

“Defining plantable area” section below.

Tree cover is negatively correlated with impervious surface cover

40

.We

divided the landscape into five impervious surface categories (0–20%,

20–40%, 40–60%, 60–80%, 80–100%) to reflect the different landscape

contexts from lightly settled suburbs to dense urban core. Our general

strategy was to model different scenarios in each US Census block, where

tree cover was increased by intervals of 5% (e.g., from 5% to 10%), poten-

tially up to an Ambitious Scenario where all previous surface was greened.

However, tree planting was stopped when there were likely to be other

constraints. This likely constraint was defined at the 90th percentile of

observed tree cover in each impervious surfa ce category for each urbanized

area. In other words, tree planting was halted at the 90th percentile of tree

cover for blocks in that urbanized area with similar landscape contexts

(imperviousness). This approach implicitly accounts for differences among

cities (e.g., in climate), as well as urban form and zoning and building codes.

Once our ambitious reforestation scenario was estimated at the Census

block level, we constructed a set of scenarios for all our sample cities, each

aiming to hit a certain nominal increase in tree cover. Thus, for instance, the

5% increase planting scenario aimed to increase tree cover in each census

block by 5%. If this nominal increase exceeded the Ambitious Scenario for a

censusblock,thentreecoverforthatcensusblockwasheldattheAmbitious

Scenario tree cover amount instead. We constructed planting scenarios with

increases of 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, and Ambitious (i.e.,

all census blocks set at the maximum possible reforestation amount, given

our assumptions), respectively.

Note that an increase in tree canopy cover, as seen from directly

overhead above the tree canopy, necessarily decreases other land covers

since total land cover in a region must sum to 100%. To account for this in

our planting scenarios, we made the simplest possible assumption that

additional tree cover occurs randomly over other land covers in proportion

to their cover before planting. For instance, if in an impervious surface

category in a particular city, 25% of the area not already in tree cover was

impervious and 75% was in sparse vegetation, we assumed that any tree

canopy increases would occur 25% over impervious surfaces and 75% over

sparse vegetation, displacing these land cover types accordingly.

Finally, we calculated the number of new stems that would need to be

planted to achieve this increase in the tree canopy based on the average

canopy size of 19.6 m

2

stem

−1

derived from Nowak and Greenfield

81

.Note

that planted trees grow their tree canopy over time, with some tree species in

mesic environments taking 3–5 years to reach 5 m height and crown dia-

meter and more than 10 years to reach 10 m height and crown diameter

82

.

Our tree planting scenarios assume spacing appropriate for adult trees, and

we are estimating benefits for the tree canopy associated with those

adult trees.

Estimating benefits

We focused in our analysis on the most importa nt heat-related benefits that

trees provide, as identified in previous manuscripts

7,29,83

,aswellasoncarbon

sequestration

46

.

Values of average annual carbon sequestration per m

2

of urban tree

canopy were taken from Nowak et al.

84

. They assembled data on tree growth

rates and carbon accumulation in tree biomass using information from 28

cities and 6 states to estimate average gross carbon sequestration per m

2

of

tree canopy. They then considered average mortality rates and associated

release of stored carbon, estimating that there was 0.226 kg C annual average

net carbon sequestration per m

2

of the canopy. We acknowledge that this

estimate does not include greenhouse gas emissions from tree planting and

management practices that result in greenhouse gas emissions, which can be

significant, but we are not aware of national representative numbers that

would account for this effect. For comparison, Kendall and McPherson

85

estimate that over the lifecycle of California-planted urban trees (note that

many urban trees are not planted but spontaneously regenerate), carbon

dioxide emissions from tree planting amount to a 20–50% reduction in

mean annual net carbon dioxide storage rates.

To estimate the likely reduction in summer air temperature that would

occur due to reforestation, we used a two-step regression approach, similar

to that of Zhang et al.

86

. Another recent paper in the Lancet by Lungman

et al.

64

also uses a two-step approach, estimating as we do the effect of trees

on LST, then the effect of LST on air temperature. This two-step regression

approach allows us to set up two regressions that each relate observed

information to observed values of predictor variables, allowing us greater

interpretability for each regression and greater flexibility in functional form

than if we tried to conduct a one-stage regression. Note also that the

observed data for LST (derived from satellite imagery and available for all

census blocks in our study area) and air temperature (measured at a smaller

number of air temperature sensors) are at different spatial and temporal

scales. By conducting two separate regressions, we avoid the loss of infor-

mation that would occur from combining these two datasets.

In our first step, we statistically related impervious cover and urban tree

cover to LSTat thecensus block level.Urbanized areas were divided into two

climatic groups, mesic and xeric, based on biomes (see McDonald et al.

29

for

details). For each climatic group, a linear regression was conducted using

PROC GLM in SAS, relating LST (the dependent variable) to both imper-

vious cover and urban tree cover. A fixed effect for urbanized areas was also

included. To avoid potential issues of spatial autocorrelation, we take a

sparse sample of 10,000 census blocks out of the 2.3 million census blocks in

our sample area. The sparse sample represents 0.4% of the total number of

Census blocks and ensures that census blocks are, on average, quite far from

one another and are thus more likely to be independent statistically in terms

of the dependent variable, LST (i.e., any errors in the estimation of LST in

one census block are likely to be spatially uncorrelated with errors in other

census blocks). Five observations were dropped due to missing data. Results

from our regression are shown in Table 5.TheR

2

for xeric regions was 0.85,

whilethatformesicregionswas0.76.

https://doi.org/10.1038/s42949-024-00150-3 Article

npj Urban Sustainability | (2024) 4:18 10

In our second step, we statistically related air temperature information

with LST. The air temperature data came from the Global Historical Cli-

matology Network

87

for summer (JJA) average daily mean (see McDonald et

al.

29

for details on data handling and processing). There were 423 air tem-

perature stations within our study area. A linear regression was conducted

using PROC GLM in SAS, relating air temperature (the dependent variable)

to LST. The fitted slope was allowed to vary by biome (i.e., biome is included

as a fixed effect in interaction with LST in the model). A fixed effect for

urbanized areas was also included. The number of air temperature sensors is

relatively small, they are measured independently by different machines, and

they are relatively far from one another, so we did not suspect there would be

spatial autocorrelation in the dependent variable after accounting for LST.

Results from our regression are shown in Table 6.TheR

2

forthisregression

was0.49,lowerthanforourstatistical model explaining LST, perhaps

reflecting the fact that there is a broad variety of factors that affect air

temperature beyond the local surface temperature

88

. Regardless, we can

propagate any uncertainty in estimating air temperature in our analysis,

which is an advantage of our statistical approach as opposed to more

mechanistic models.

Note that, therefore, the air temperature impacts of our tree planting

scenarios are a function of multiple factors: the amount of plantable area in a

Census block; the fraction of impervious surface before planting, which

determines how much impervious surface cover is reduced through

planting; the aridity and biome of an urbanized area, and an urbanized area

fixed effect. We summarize the effect of multiple factors in Supplementary

Table 1, which lists (by urbanized area and impervious surface category) the

realized change in tree cover and impervious surfaces for the 5% target

scenario, as well as the estimated change in LST and air temperature. Not

surprisingly, the greatest decline in impervious surface area with planting is

generally in impervious surface category 5 (defined as neighborhoods with

80–100% impervious surface cover). The only exception is when there is not

enough plantable area in impervious category 5 neighborhoods to approach

the 5% target for tree canopy increase. Conversely, the smallest decline in

impervious surface area with planting is generally in impervious surface

category 1 (defined as neighborhoods with 0–20% impervious surface

cover). For this reason, the greatest decline in LST occurs in impervious

category 5, while there are lesser declines in impervious category 1, with

similar patterns across climate variables (aridity and biome). Asheville, NC,

has the greatest average city-level decline in LST in impervious category 5

(0.49 °C). Patterns for air temperature declines are similarly generally

greater in impervious category 5 and less in impervious category 1, with

similar patterns across climate variables (aridity and biome). Bonita Spring,

FL, has the greatest decline in air temperature in category 5 (0.21 °C).

Finally, note that we estimate statistically the change in LST and air tem-

perature at the scale of a US census block, which includes a variety of land-

use types. This makes our estimates of temperature impact per unit increase

in tree cover different from (and lower in magnitude than) the estimates of

tree cooling efficiency of Zhao et al.

89

, who used Landsat-based estimates of

tree cover and LST to quantify change in LST for those pixels with tree

canopy.

This study focuses on tree canopy and its benefits relative to the race/

ethnicity of neighborhoods. Within our sample of cities, race/ethnicity is

correlated to the impervious surface category (Table 7), with most neigh-

borhoods in categories 4 and 5 being majority POC, while most neigh-

borhoods in categories 1 and 2 are majority non-Hispanic white. This

pattern is discussed extensively in McDonald et al.

40

,resultsfromlow-

income households, which are more likely to be POC, being more frequently

located in US Census blocks with high population density and high

impervious surface cover in city centers rather than less dense suburbs.

Race/ethnicity is also correlated with aridity, as the arid southwest of the US

has a larger fraction of POC in all impervious categories than does the mesic

region of the country. This latter pattern is primarily caused by a higher

share of people of Hispanic origin, who are counted in the POC category for

our study. Since impervious surface category and aridity are two of the

explanatory variables used to predict the impact of tree canopy increases on

air temperature, the correlation of race/ethnicity with these variables

necessarily implies that the impact of tree canopy increases on air tem-

perature varies with respect to race/ethnicity.

To estimate the health impacts of an estimated change in air tem-

perature due to tree canopy expansion, we follow the methodology of

McDonald et al.

29

. For estimating avoided mortality, we use published

epidemiological studies that relate changes in air temperatures to changes in

mortality over time. Specifically, we use Bobb et al.

90

, who estimated the

heat–mortality relationship for 105 US cities. In this study, we use Bobb

et al.’s regional estimates of the heat-mortality response curve, listed in our

Supplementary Table 2. For morbidity estimation, our analysis is based on

Gronlund et al.

91

, who estimated heat-related hospitalizations as a function

ofairtemperatureforalargepopulationintheUSandthenscaledfromthe

Gronlund et al. numbers to estimate emergency department visits and

doctor’soffice visits

29

.

We estimated avoided electricity consumption due to tree cover, fol-

lowing the methodology in McDonald et al.

29

. We base our analysis on

Santamouris et al.

43

, a literature review that collected empirical estimates of

Table 5 | Model parameters from regression relating LST to tree cover and impervious surface cover

Type III SS Mean SS F value P Estimate SE

MESIC. Tree cover (fraction) 1443 1443 310 <0.001 −2.88 0.16

MESIC. Impervious surface cover (fraction) 15,684 15,684 3367 <0.001 9.06 0.16

MESIC. NAME (N = 86) 68,869 810 174 <0.001 Multiple

ARID. Tree cover (fraction) 1031 1031 212 <0.001 −8.06 0.55

ARID. Impervious surface cover (fraction) 327 327 67 <0.001 2.78 0.34

ARID. NAME (N = 14) 35,906 2762 567 <0.001 Multiple

Separate regression analyses were undertaken for mesic and arid cities. A fixed effect for urbanized areas (NAME) was included. Shown is first the regression for mesic cities (N = 8498 with complete data),

followed by the regression results for arid cities (N = 1497 with complete data).

Table 6 | Model parameters from regression relating air

temperature to LST

Biomes Estimate SE

Temperate broadleaf and mixed forests 0.33 0.030

Temperate coniferous forests 0.35 0.030

Tropical and subtro pical grasslands, savannas, and shrublands 0.40 0.033

Temperate grasslands, savannas, and shrublands 0.36 0.028

Flooded grasslands and savannas 0.46 0.034

Mediterranean forests, Wwoodlands, and scrub 0.25 0.026

Deserts and xeric shrublands 0.35 0.023

Regression slopes were allowed to vary by the biome in which an urbanized area is located. The

overall regression also included an intercept term and was highly significant (F = 57.8, P < 0.001).

Shown are the regression results for census blocks with air sensors (N = 423).

https://doi.org/10.1038/s42949-024-00150-3 Article

npj Urban Sustainability | (2024) 4:18 11

increases in electricity use during periods of high air temperature. We subset

the results in Table 1 of their paper to just look at US studies, which gave a

range of 2.9–8.5% increase in electricity consumption per 1 °C increase in air

temperature. We assume that the shift in summer daily mean temperatures

we modeled would only increase electricity consumption for space cooling

during summer months (One quarter of the year). From this assumption,

we calculated an estimated annual increase in electricity consumption,

assuming no increase in other seasons. We then multiplied the percent

increase in electricity consumption by city-level data on residential elec-

tricity use. To estimate avoided GHG emissions due to avoided electricity

consumption, we multiplied avoided electricity consumption by the average

carbon intensity of US electricity generation, taken from EPA data.

Finally, when calculating statistics at the level of urbanized areas or

nationally, we used population-weighted statistics to give greater weight to

Census Blocks with greater population.

Valuation and extrapolation

Our valuation methodology follows McDonald et al.

29

and is extensively

described in the supplementary methods section of that paper. The costs of

tree planting and maintenance costs (annualized) were assessed for a sample

of US cities

7,23

, and we use those values hereafter adjusting for inflation.

To briefly summarize, for av oided mortality, we use a value of a

statistical life approach (VSL), adjusted for remaining life year s(e.g.

refs.

92,93

), usin g a range in the US of $5.4–13.4 M (2015$)

94

. Most heat-

related deaths occur in people 55 years or older

92,95

, an d so we apply

Murphy and Topel’s

93

life cycle shape for the value of a life-year for 11

age classes to calculate an ove rall average, age-weighted VSL, accounting

for the age distribution of mortality from heat-related eve nts, of $5.7M

(2015$, range: $3.3–8.2M).

For morbidity, we use a cost-of-illness (COI) approach to assess the

economic burden associated with heat-related illnesses (HRI) that were

avoided due to tree cover. Economic calculations were done separately for

avoided emergency department (ED) visits

96

, avoided hospitalizations

97

,and

outpatient visits

98

. We also estimated lost work productivity as the product of

total work loss days (from HRI-related hospitalizations, ED visits, and out-

patient visits) and the average US daily earnings rate (see McDonald et al.

29

for details).

For electricity consumption, we used the results of Santamouris and

colleagues

43

, which for US studies give a range of 2.9–8.5% increase in

electricity consumption per 1 °C increase in air temperature. Data on

average household residential electricity consumption and average cost per

KWh, by electric utility were taken from the US Energy Information

Administration (EIA, 2016) form EIA-861. See McDonald et al.

29

for details.

While our methodology is an accurate estimate of the average relationship

between changes in air temperature and electricity consumption, we

acknowledge that for particular buildings or neighborhoods the relationship

mighthaveagreaterorlesserslope.Note that we are accounting for different

regional climates by the different regional regressions used to model LST

and air temperature, as well as the information on local electricity con-

sumption and cost by the electric utility.

For carbon sequestration and avoided GHG emissions due to

avoided electricity consumption, we used the n ew EPA proposed

Social Cost of Carbon (SCC) for 2020 of USD 190 expressed at 2020

prices, assuming a 2% discount r ate

99

. Th is estimate follows that of

recent science by Rennert et al.

100

. The SCC measures the total (global)

estimated ec onomic damages, expressed i n monetary terms, that

result from one a dditional ton of carbon dioxide in the atmosphere.

These damages from increased atmospheric CO

2

concentrations are

separate from and fully additional to, the local damages from health

and electricity use impacts that increases in urban tree canopy avoid

via their local cooling effects. Where necessary, all economic costs and

benefits presented in this paper were standardized to USD in 2022,

using the US Bureau of Labor Statistics Consumer Price Index

101

.

Caveats and limitations

In this section, we discuss conceptually some caveats and limitations of our

analysis. We do not repeat the technical details of our methodology pre-

sented above. Rather, we discuss how these kinds of limitations, common

and in some cases currently unavoidable in this kind of research, might

affect our results. We also discuss how future research projects might

overcome some of these limitations and improve the accuracy of our

estimates.

As in many geospatial analyses of the linkage between land cover,

temperature, and health, we had to deal with spatial and temporal differ-

ences in the underlying data. For instance, our tree cover data is high-

resolution (2 m) binary data, while the best available impervious surface

cover data for our sample of 5723 municipalities is at 30 m resolution but

provides continuous estimates of the percent impervious. While the dif-

ference in spatial resolution between the two datasets will affect the accuracy

of our results, it was unavoidable given the best available input datasets. One

of the reasons we chose the census block as our unit of analysis, rather than

the pixel level, was so that information from many pixels could be averaged,

reducing the overall classification error rate. Similarly, the spatial boundaries

for which 2016 forest cover was classified correspond with the 2010 US

Census Bureau definition, which was altered for the 2020 US Census. The

shifting of spatial boundaries is relatively minor, occurring generally at the

edge of urbanized areas where population density is relatively low (Sup-

plementary Fig. 1). We chose to minimize temporal differences, using our

2016 forest cover data and the 2020 census data on demography, rather than

the alternative of using 2010 census data for demography.

Our general approach when dealing with spatial or temporal differ-

ences in input datasets is to avoid inconsistencies where possible but to

choose methodologies that minimize their impact when not possible. The

empirical regressions we used to link tree cover and impervious surface

cover to land surface temperature (LST), as well as LST to air temperature,

are one example of a methodology that corrects inconsistencies in input

datasets. The regression parameters are chosen to maximally explain the

predicted variables, and so linear transformations of explanatory variables

alter the parameter but not the predicted variable. For instance, suppose

with a high-resolution sensor, the impervious cover was measured at X%,

Table 7 | Race/ethnicity composition of our sample, relative to impervious surface and aridity

Impervious surface

category

Arid population, millions

(%) White

Arid population, millions

(%) POC

Mesic popula tion, millions

(%) White

Mesic population, millions

(%) POC

Total population,

millions

1(0–20%) 0.7 (54%) 0.6 (46%) 15.6 (79%) 4.1 (21%) 21.0

2 (20–40%) 2.0 (54%) 1.6 (46%) 25.9 (66%) 13.4 (34%) 42.9

3 (40–60%) 4.5 (36%) 8.1 (64%) 22.9 (52%) 20.7 (48%) 56.2

4 (60–80%) 3.0 (22%) 10.8 (78%) 10.6 (38%) 17.2 (62%) 41.8

5 (80–100%) 0.8 (22%) 2.7 (78%) 5.2 (36%) 9.2 (64%) 17.8

Overall categories (32%) (68%) (55%) (45%)

Population totals, inmillions, are shown. Percentage of thepopulation within each aridity categoryand each impervious surface category is shown in parentheses. For instance,in arid urbanized areas, there

are 2.7 million POC in impervious surface category 5, 78% of the total population of arid cities in this impervious surface category.

https://doi.org/10.1038/s42949-024-00150-3 Article

npj Urban Sustainability | (2024) 4:18 12

but with the 30 m resolution data we used the impervious surface cover was

measured as some fraction of this, fX%. If a regression relating impervious

surface cover X to LST estimated a slope β, then a regression relating fX to

LST would estimate a slope β/f, but the predicted values of LST would not

change. If the relationship between impervious surface cover measured at

different resolutions is not a linear function, the effect on the predicted

values of LST is more complex to assess. In general, however, empirically

relating measurable variables to one another helps account for small dif-

ferences in spatial and temporal scale.

As with any analysis of the potential for increasing urban tree canopy

cover, ours had to define decision criteria for what is plantable. There is

some subjectivity in defining the maximum plantable area. In theory, an

urban area could install green roofs on many buildings, depave substantial

areas of impervious surfaces, and otherwise take expensive but feasible steps

to increase urban tree canopy cover and vegetative area. What is easier to

objectively define is how a certain percentage point increase in tree canopy

cover will affect temperatures. In our study, we consider a range of tree cover

increases (5%, 10%, 15%, etc.), plus an “Ambitious” scenario where tree

cover is maximized subject to two constraints, discussed below. We

acknowledge that other studies could defineamoreorlessambitioussce-

nario of tree planting by making different assumptions about constraints.

Our first constraint, as in other studies

7,23

,istoconsiderthefraction

impervious as unplantable. Note that this constraint is implemented at the

census block level, not the pixel level. We acknowledge that there are very

detailed studies, such as Treglia et al.

52

.thatusefine spatial resolution data at

the pixel level that consider how new trees might overhang impervious

surfaces in pixels adjacent to where the stem was planted. Note, however,

that at the US Census Block scale, it is rare that the amount of tree cover

exceeds the previous fraction (i.e., 1—impervious cover). Supplementary

Fig. 2 shows the relationship between tree cover and the impervious fraction

for the New York City urbanized area. In only 5.5% of the census blocks,

does the tree cover exceed the previous fraction (i.e., 1—impervious), and in

those cases where it does, tree cover exceeds the previous by a median of

3.7%p. For almost all census blocks, tree cover does not substantially exceed

the previous fraction.

Our second constraint tries to account for many other factors that vary

among urban areas, such as differences in aridity, regulations, urban form,

and landowner preferences. We acknowledge that a detailed study in one

municipality

52

may have spatial information on many of these factors. Such

an approach becomes infeasible, however when dealing with thousands of

municipalities, as is the case in this study. Following other published

studies

7,23

, we set a target at the 90th percentile of tree cover observed in

neighborhood s of similar impervious surface cover. Our way of setting an

Ambitious target using the observed distribution within an impervious

category within an urban area implicitly accounts for factors that vary

among urban areas, such as climate, urban form, and regulations. It is also an

ambitious target implying, for example, that the Charlotte urbanized area

(mesic climate) could raise population-weighted tree cover from 50% to

67% percent, while the Phoenix urbanized area (arid climate) could go from

10% to 22%. By definition, however, there are 10% of neighborhoods that

exceed our 90th percentile target, and we acknowledge our “Ambitious

Scenario” is not the maximum imaginable.

One important innovation in our methodology is to calculate tree cover

and its benefits at the local (census block) level when possible. When we

present averages or use them in calculations, we use the population-

weighted average rather than the simple average to better represent the tree

cover the typical person experiences. To see why the type of average matters,

consider our Ambitious Scenario, where the simple average increase in the

New York City urbanized area was from 26% to 44%, while the population-

weighted average increase was from 22% to 35%. Focusing on the highest

impervious surface category used in setting our targets (80–100% imper-

vious), where 7.4 million people lived in the New York urbanized area in

2020, the population-weighted average increase in tree cover under the

Ambitious Scenario was from 6.8% to 12%. In other words, a dispropor-

tionate part of the tree cover increase in the urbanized area would occur in

relatively sparsely populated census blocks, while dense census blocks in

Manhattan would have relatively small increases. Contrast our methodol-

ogy with the assumption made in some studies that a city might hit a certain

tree cover target (e.g., 30%) at a simple average level, essentially assuming all

neighborhoods hit that goal and then estimating health benefits using that

simple average change calculation. Such an approach would potentially

overstate the tree cover increases that are possible in more densely populated

neighborhoods, which in the US are often predominately people of color

40

.

Finally, another challenge for any study of many municipalities is

deciding when to use a national average, perhaps relatively precisely esti-

mated,versuswhentoaccountforregional variation. Our approach has

been to use regional estimates where they can be consistently estimated for

our 5723 municipalities and to otherwise use the best available national

estimates. For instance, our empirical regressions relating tree cover and

impervious cover to LST (Supplementary Fig. 3) and LST to air temperature

(Supplementary Fig. 4) allow for regional variatio n in the parameters of the

model. The amount of regional variation allowed (i.e., the regional

boundaries) depended on the patterns in our dataset, as described above in

the subsection “Estimating benefits” section. Our estimate of mortality

impacts comes from Bobb et al.

90

, who also allowed their regression para-

meters to vary by region (Supplementary Table 2). Readers should note,

however, that there are many other studies of heat/mortality relationships,

using different indices of thermal comfort and showing various response

curves

102–104

.

Sometimes, however, national averages were the best available. For

instance, we used data on average planting and maintenance costs from a

sample of US cities

7,23

. These were estimates for municipal staff planting and

maintenance, and we acknowledge other groups that use volunteer labor,

such as non-profit organizations might have lower costs. While this dataset

of costs is sufficient to calculate a national average, we do not have infor-

mation for each of the 5723 municipalities in our database, and we

acknowledge that individual municipalities might have quite different

planting costs. Similarly, we use the US national average for canopy size

from the US Forest Service but do not have access to information on the

average canopy size for each municipality. Undoubtedly, tree canopy size

varies by municipality due to the species planted, planting age, and climate,

among other factors. In general, the use of accurate national averages means

our national estimates are correct but we cannot map regional or local

variability in these factors. Future US national studies would benefitfrom

more spatially resolved data on planting costs and canopy size.

Reporting summary

Further information on research design is available in the Nature Research

Reporting Summary linked to this article.

Data availability

The datasets generated during the current study are available in the Data

Dryad repository, https://doi.org/10.5061/dryad.zgmsbcckf.

Code availability

The underlying code for this study is available in Zenodo and can be

accessed via the linked Data Dryad repository, https://doi.org/10.5061/

dryad.zgmsbcckf.

Received: 27 April 2023; Accepted: 28 February 2024;

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Acknowledgements

T.C.C. was supported by the Pacific Northwest NationalLaboratory, which is

operated for DOE by Battelle Memorial Institute under contract DE-AC05-

76RL01830, and by COMPASS-GLM, a multi-institutional project supported

by the U.S. DOE, Office of Science. S.C.C.-P.’s time on this project was

supported by the Bezos Earth Fund. Co-authors who work at The Nature

Conservancy (TNC) were supported by the members and donors of TNC.

The funders played no role in the study design, data collection, analysis, and

interpretation of data, or the writing of this manuscript.

Author contributions

R.I.M. designed and led the analysis. T.B. calculated tree cover for the

municipalities in our sample. T.C.C. calculated land-surface temperatures.

T.K. designed the economic valuation analysis. S.C.C.-P. helped design the

rules for where reforestation was feasible, and with J.E.F. helped design the

calculation of carbon sequestration. All co-authors helped write the

manuscript.

Competing interests

The authors declare no competing interests.

Additional information

Supplementary information The online version contains

supplementary material available at

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.

Correspondence and requests for materials should be addressed to

Robert I. McDonald.

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