Assessing Urban Forest 

Effects and Values

Northern 

Research Station

Resource Bulletin NRS-43

United States

Department of

Agriculture

Forest Service

Scranton’s Urban Forest
Published by: For additional copies:

USDA FOREST SERVICE USDA Forest Service 

11 CAMPUS BLVD SUITE 200 Publications Distribution

NEWTOWN SQUARE PA 19073-3294 359 Main Road

 Delaware, OH 43015-8640

October 2010 Fax: (740)368-0152

Visit our homepage at: http://www.nrs.fs.fed.us

Abstract

An analysis of trees in the urbanized portion of Scranton, PA, reveals that this area has 

about 1.2 million trees with canopies that cover 22.0 percent of the area. The most common 

tree species are red maple, gray birch, black cherry, northern red oak, and quaking aspen. 

Scranton’s urban forest currently store about 93,300 tons of carbon valued at $1.9 million. 

In addition, these trees remove about 4,000 tons of carbon per year ($83,000 per year) and 

about 65 tons of air pollution per year ($514,000 per year). Trees in urban Scranton are 

estimated to reduce annual residential energy costs by $628,000 per year. The structural, or 

compensatory, value is estimated at $322 million. Information on the structure and functions 

of the urban forest can be used to inform urban forest management programs and to 

integrate urban forests within plans to improve environmental quality in the Scranton area.

The Authors

DAVID J. NOWAK is a research forester and project leader, ROBERT E. HOEHN III, is 

a forester, DANIEL E. CRANE is an information technology specialist, JACK STEVENS 

is a forester with the U.S. Forest Service’s Northern Research Station at Syracuse, NY. 

VINCENT COTRONE is an extension urban forester with Penn State Cooperative 

Extension.

Manuscript received for publication 28 June 2010

Acknowledgments

This study was funded by the Northeast Pennsylvania (NEPA) Urban Forestry Program, 

sponsored through the U.S. Forest Service, Northeastern Area, State and Private Forestry, 

directed by the Pennsylvania Urban & Community Forestry Council in cooperation with 

the Pennsylvania Department of Conservation and Natural Resources Bureau of Forestry, 

and the Penn State Cooperative Extension. We thank the following people for their project 

support, review and assistance with data collection related to this project:

Lynn Conrad, program administrator, NEPA Urban & Community Forestry Program

Bernie Dincher, technical specialist, NEPA Urban & Community Forestry Program

Ellen Roane, urban forestry coordinator, Department of Conservation & Natural Resources 

(DCNR) Bureau of Forestry

Brad Elison, district forester, DCNR Bureau of Forestry

John Maza, service forester, DCNR Bureau of Forestry

Donna Murphy, center coordinator, Mid-Atlantic Center for Urban & Community Forestry, 

U.S. Forest Service, Northeastern Area State and Private Forestry

Jeremy Wint, intern, Keystone College

Briana Betress, intern, Keystone College

Bernie McGurl, director, Lackawanna River Corridor Association

Sean Haggerty, Lackawanna River Corridor Association

Molly Dincher, volunteer

Diane Dincher, volunteer
1

Executive Summary

Trees in cities can contribute signifi cantly to human health and environmental quality. 

Unfortunately, relatively little is known about the urban forest resource and what it 

contributes to the local and regional society and economy. To better understand the 

urban forest resource and its numerous values, the U.S. Forest Service, Northern Research 

Station, developed the Urban Forest Eff ects (UFORE) model. Results from this model 

are used to advance the understanding of the urban forest resource, improve urban forest 

policies, planning and management, provide data to support the potential inclusion of 

trees within environmental regulations, and determine how trees aff ect the environment 

and consequently enhance human health and environmental quality in urban areas.

Forest structure is a measure of various physical attributes of the vegetation, including 

tree species composition, number of trees, tree density, tree health, leaf area, biomass, 

and species diversity. Forest functions, which are determined by forest structure, 

include a wide range of environmental and ecosystem services such as air pollution 

removal and cooler air temperatures. Forest values are an estimate of the economic 

worth of the various forest functions.

To help determine the vegetation structure, functions, and values of the urban portion 

of Scranton, PA, a vegetation assessment was conducted during the summer of 2006. 

For this assessment, one-tenth acre fi eld plots were sampled and analyzed using the 

UFORE model. Th is report summarizes results and values of:

• Forest structure

• Potential risk to forest from insects or diseases

• Air pollution removal

• Carbon storage

• Annual carbon removal (sequestration)

• Changes in building energy use

Urban forests 

provide numerous 

benefi ts to society, 

yet relatively little 

is known about this 

important resource.

In 2006, the UFORE 

model was used to 

survey and analyze 

the trees in the 

urbanized portion 

of Scranton.

The calculated 

environmental 

benefi ts of the 

urban forest are 

signifi cant, yet 

many environmental, 

economic, and 

social benefi ts still 

remain to be 

quantifi ed.

Scranton Urban Forest Summary

Feature Measure

Number of trees 1.2 million

Tree cover 22%

Most common species red maple, gray birch, black cherry, 

northern red oak, quaking aspen

Percentage of trees < 6-inches diameter 77.1%

Pollution removal 65 tons/year ($514,000/year)

Carbon storage 93,300 tons ($1.9 million)

Carbon sequestration 4,000 tons/year ($83,000/year)

Building energy reduction $628,000/year

Increased carbon emissions $16,700/year

Structural value $322 million

Ton – short ton (U.S.) (2,000 lbs)

Mark Burns. Used with permission.
2

Urban Forest Effects Model 

and Field Measurements

Th ough urban forests have many functions and values, currently only a few of these 

attributes can be assessed due to a limited ability to quantify all of these values through 

standard data analyses. To help assess the city’s urban forest, data from 182 fi eld plots 

located throughout the city were analyzed using the Forest Service’s Urban Forest 

Eff ects (UFORE) model.1

UFORE is designed to use standardized fi eld data from randomly located plots and 

local hourly air pollution and meteorological data to quantify urban forest structure 

and its numerous eff ects, including:

• Urban forest structure (e.g., species composition, tree density, tree health, leaf 

area, leaf and tree biomass, species diversity, etc.).

• Amount of pollution removed hourly by the urban forest, and its associated 

percent air quality improvement throughout a year. Pollution removal is 

calculated for ozone, sulfur dioxide, nitrogen dioxide, carbon monoxide and 

particulate matter (<10 microns).

• Total carbon stored and net carbon annually sequestered by the urban forest.

• Eff ects of trees on building energy use and consequent eff ects on carbon 

dioxide emissions from power plants.

• Compensatory value of the forest, as well as the value of air pollution removal 

and carbon storage and sequestration.

• Potential impact of infestations by Asian longhorned beetles, emerald ash 

borers, gypsy moth, or Dutch elm disease.

For more information go to http://www.ufore.org

In the fi eld, one-tenth acre plots were selected based on a randomized grid-based pattern 

at an average density of approximately 1 plot for every 57 acres. Th e study is broken into 

smaller areas based on fi eld land use classifi cations. Th e plots were divided among the 

following land 

uses: residential 

(91 plots, 

50.0% of area), 

commercial/

industrial (30 

plots, 16.5%), 

vacant (28 

plots, 15.4%), 

transportation/

utility (12 plots, 

6.6%), park/

cemetery (11 

plots, 6.0%), and 

institutional (10 

plots, 5.5%).

Benefi ts provided by 

urban trees include:

• Air pollution 

removal

• Air temperature 

reduction

• Reduced building 

energy use

• Absorption 

of ultraviolet 

radiation

• Improved water 

quality

• Reduced noise

• Improved 

human comfort

• Increased 

property value

• Improved 

physiological & 

psychological 

well-being

• Aesthetics

• Community 

cohesion

Carl Abraham. Used with permission.

“Urban” Scranton study area. Red dots represent fi eld plots, red shaded 

areas represent “non-urban” portion of Scranton that was not sampled.
3

Field data were collected by Northeast Pennsylvania Urban & Community Forestry 

Program staff , Keystone College interns, Penn State Extension Urban Forester, and 

DCNR Bureau of Forestry staff ; data collection took place during the leaf-on season 

to properly assess tree canopies. Within each plot, data included land-use, ground 

and tree cover, shrub characteristics, and individual tree attributes of species, stemdiameter at breast height (d.b.h.; measured at 4.5 ft.), tree height, height to base of live 

crown, crown width, percentage crown canopy missing and dieback, and distance and 

direction to residential buildings.2

To calculate current carbon storage, biomass for each tree was calculated using 

equations from the literature and measured tree data. Open-grown, maintained trees 

tend to have less biomass than predicted by forest-derived biomass equations3

. To 

adjust for this diff erence, biomass results for open-grown urban trees are multiplied by 

0.8.3

 No adjustment is made for trees found in natural stand conditions. Tree dryweight biomass was converted to stored carbon by multiplying by 0.5.3

To estimate the gross amount of carbon sequestered annually, average diameter growth 

from the appropriate genera and diameter class and tree condition was added to the 

existing tree diameter (year x) to estimate tree diameter and carbon storage in year x+1.

Air pollution removal estimates are derived from calculated hourly tree-canopy 

resistances for ozone, and sulfur and nitrogen dioxides based on a hybrid of big-leaf 

and multi-layer canopy deposition models.4, 5 As the removal of carbon monoxide 

and particulate matter by vegetation is not directly related to transpiration, removal 

rates (deposition velocities) for these pollutants were based on average measured values 

from the literature6, 7 that were adjusted depending on leaf phenology and leaf area. 

Particulate removal incorporated a 50 percent resuspension rate of particles back to the 

atmosphere.8

Seasonal eff ects of trees on residential building energy use was calculated based on 

procedures described in the literature9

 using distance and direction of trees from 

residential structures, tree height and tree condition data.

Compensatory values were based on valuation procedures of the Council of Tree 

and Landscape Appraisers, which uses tree species, diameter, condition and location 

information.10

To learn more about UFORE methods11 visit: 

www.nrs.fs.fed.us/tools/UFORE/ or www.ufore.org

Field Survey Data

Plot Information

• Land use type

• Percent tree 

cover

• Percent shrub 

cover

• Percent plantable

• Percent ground 

cover types

• Shrub species/

dimensions

Tree parameters

• Species

• Stem diameter

• Total height

• Height to crown 

base

• Crown width

• Percent foliage 

missing

• Percent dieback

• Crown light 

exposure

• Distance and 

direction to 

buildings from 

trees

Mark Burns. Used with permission.
4

Tree Characteristics of the Urban Forest

Th e urban forest of Scranton has an estimated 1.2 million trees with a standard 

error (SE) of 133,000. Tree cover in urban portion of Scranton is estimated at 22 

percent.12 Th e four most common species in the urban forest are red maple (10.6 

percent), gray birch (10.1 percent), black cherry (8.9 percent), and northern red oak 

(8.1 percent). Th e 10 most common species account for 65.4 percent of all trees; their 

relative abundance is illustrated below. In total, 80 tree species were sampled in urban 

Scranton; these species and their relative abundance are presented in Appendix IV.

There are an 

estimated 1.2 million 

trees in urban 

Scranton with 22.0 

percent tree cover.

The 10 most 

common species 

account for 65.4 

percent of the total 

number of trees.

Tree density is 

highest in vacant 

lands and lowest in 

institutional lands.

Overall, trees that have diameters less than 6 inches account for 77.1 percent of 

the population. Th e highest density of trees occurs in vacant lands (421 trees/acre), 

followed by park/cemetery (190 trees/acre) and residential land (65 trees/acre). Th e 

overall tree density in urban Scranton is 116.3 trees/acre, which is relatively high 

compared to other city tree densities that range between 14.4 and 119.2 trees/acre 

(Appendix I). Much of this relatively high tree density can be attributed to the high 

density of small trees (e.g., gray birch) on vacant lands and most likely are an artifact 

of forest regeneration on mine lands or harvested forests. About 46 percent of the 

David Nowak, U.S. Forest Service

red maple

10.6% gray birch

10.1% black cherry

8.9%

northern red 

oak

8.1%

quaking aspen

7.1%

black birch

5.6%

black locust

5.4%

Norway maple

3.6%

sumac

3.4%

black tupelo

2.6%

other species

34.6%

0

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0

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600,000

700,000

Trees per acre

Number of Trees

Total Trees

per acre
5

total tree population in urban Scranton is from trees less than 6 inches in diameter on 

vacant lands. Land uses that contain the most leaf area are residential (45.3 percent of 

total tree leaf area) and vacant (37.9 percent).

Urban forests are a mix of native tree species that existed prior to the development 

of the city and exotic species that were introduced by residents or other means. 

Th us, urban forests often have a tree diversity that is higher than surrounding native 

landscapes. Increased tree diversity can minimize the overall impact or destruction by 

a species-specifi c insect or disease, but the increase in the number of exotic plants can 

also pose a risk to native plants if some of the exotics species are invasive plants that 

can potentially out-compete and displace native species. In urban Scranton, about 75 

percent of the trees are from species native to Pennsylvania. Trees with a native origin 

outside of North America are mostly from Eurasia (6.3 percent of the species).

Nearly 74.6 percent 

of the tree species 

in urban Scranton 

are native to 

Pennsylvania.

Urban forests are 

a mix of native 

tree species that 

existed prior to the 

development of 

the city and exotic 

species that were 

introduced by 

residents or other 

means.

Carl Abraham. Used with permission.

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* native to North America and one other continent, excluding South America

** nativeto North America and South America, and one other continent

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Percent

d.b.h. class (in)
6

Urban Forest Cover and Leaf Area

Dominant ground cover types include herbaceous (35.3 percent), impervious surfaces 

(excluding buildings) (27.5 percent), and buildings (16.7 percent).

Healthy leaf area 

equates directly 

to tree benefi ts 

provided to the 

community.

Red maple is 

currently the most 

dominant species in 

the urban Scranton 

area based on 

relative leaf area and 

relative population.

Many tree benefi ts are linked directly to the amount of healthy leaf surface area of the 

plant. In urban Scranton trees that dominate in terms of leaf area are northern red oak, 

Norway maple and red maple.

Tree species with relatively large individuals contributing leaf area to the population 

(species with percent of leaf area much greater than percent of total population) are 

silver maple, Norway maple, and northern red oak. Smaller trees in the population 

are sumac, gray birch and witch hazel (species with percent of leaf area much less than 

percent of total population). A species must also constitute at least 1 percent of the 

total population to be considered as relatively large or small trees in the population.

Th e importance values (IV) are calculated using a formula that takes into account the 

relative leaf area and relative abundance. Th e most important species in Scranton’s 

urban forest, according to calculated IVs, are red maple, northern red oak, and black 

cherry. High importance values do not mean that these trees should necessarily be used 

in the future, rather that these species currently dominate the urban forest structure.

Common 

Name

%

Popa

%

LAb IVc

red maple 10.6 10.6 21.2

northern

 red oak 8.1 11.3 19.4

black

 cherry 8.9 7.3 16.2

Norway

 maple 3.6 10.8 14.4

gray birch 10.1 2.7 12.8

quaking

 aspen 7.1 2.5 9.6

black birch 5.6 3.2 8.8

silver maple 1.7 6.7 8.4

black locust 5.4 2.8 8.2

Norway

 spruce 0.9 6.2 7.1

a

 percent of population

b

 percent of leaf area

c

 Percent Pop + Percent LA 

Tom Dobb, wikimedia commons.

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Comm/Ind

Institutional

Park/Cemetery

Residential

Trans/Utility

Vacant

Urban Scranton

bare soil duff/mulch cover herbaceous water impervious surfaces (excluding buildings) buildings

0

2

4

6

8

10

12

Percent

% of total leaf area

% of all trees
7

Air Pollution Removal by Urban Trees

Poor air quality is a common problem in many urban areas. It can lead to human 

health problems, damage to landscape materials and ecosystem processes, and reduced 

visibility. Th e urban forest can help improve air quality by reducing air temperature, 

directly removing pollutants from the air, and reducing energy consumption in 

buildings, which consequently reduce air pollutant emissions from power plants. 

Trees also emit volatile organic compounds that can contribute to ozone formation. 

However, integrative studies have revealed that an increase in tree cover leads to 

reduced ozone formation.13

Pollution removal by trees in urban Scranton was estimated using the UFORE model 

in conjunction with fi eld data and hourly pollution and weather data for the year 

2000. Pollution removal was greatest for ozone (O3

), followed by nitrogen dioxide 

(NO2

), sulfur dioxide (SO2), particulate matter less than ten microns (PM10), and 

carbon monoxide (CO). It is estimated that trees remove 65 tons of air pollution 

(CO, NO2

, O3, PM10, SO2) per year with an associated value of $514,000 (based on 

estimated 2007 national median externality costs associated with pollutants14).

The urban forest of 

Scranton removes 

approximately 65 

tons of pollutants 

each year, with a 

societal value of 

$514,000 million/

year.

General urban 

forest management 

recommendations 

to improve air 

quality are given in 

Appendix II.

Carl Abraham. Used with permission.

0

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100,000

150,000

200,000

250,000

300,000

350,000

400,000

0

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45

50

CO NO2 O3 PM10 SO2

Value ($ / year)

Pollution removed (tons/year)

Pollution Removed

Value (U.S. Dollars)
8

Carbon Storage and Sequestration

Climate change is an issue of global concern. Urban trees can help mitigate climate 

change by sequestering atmospheric carbon (from carbon dioxide) in tissue and by 

reducing energy use in buildings, and consequently reducing carbon dioxide emissions 

from fossil-fuel based power plants.15

Trees reduce the amount of carbon in the atmosphere by sequestering carbon in new 

tissue growth every year. Th e amount of carbon annually sequestered is increased with 

healthier trees and larger diameter trees. Gross sequestration by trees in urban Scranton 

is about 4,000 tons of carbon per year with an associated value of $83,000. Net carbon 

sequestration in the urban Scranton is estimated at about 3,000 tons and is lower than 

gross sequestration based on the estimated loss of carbon due to tree mortality.

Carbon storage:

Carbon currently 

held in tree tissue 

(roots, stems, and 

branches).

Carbon 

sequestration:

Estimated amount 

of carbon removed 

annually by 

trees. Net carbon 

sequestration 

can be negative if 

emission of carbon 

from decomposition 

is greater than 

amount sequestered 

by healthy trees.

Carbon storage by trees is another way trees can infl uence global climate change. As 

trees grow, they store more carbon by holding it in their accumulated tissue. As trees 

die and decay, they release much of the stored carbon back to the atmosphere. Th us, 

carbon storage is an indication of the amount of carbon that can be released if trees 

are allowed to die and decompose. Maintaining healthy trees will keep the carbon 

stored in trees and when trees die, utilizing the wood in long-term wood products or 

to help heat buildings or produce energy will help reduce carbon emissions from wood 

decomposition or from power plants. Trees in urban Scranton are estimated to store 

93,300 tons of carbon ($1.9 million). Of all the species sampled, northern red oak 

stores and sequesters the most carbon (approximately 13.2% of the total carbon stored 

and 10.2% of all sequestered carbon).

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12,000

14,000

Sequestration (tons/year)

Storage (tons)

d.b.h. class

Carbon Storage 

Carbon Sequestration

Mark Burns. Used with permission.

0

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Value ($/year)

Carbon sequestration (tons/year)

Carbon Sequestration

U.S. Dollars
9

Trees Affect Energy Use in Buildings

Trees aff ect energy consumption by shading buildings, providing evaporative cooling, 

and blocking winter winds. Trees tend to reduce building energy consumption in the 

summer months and can either increase or decrease building energy use in the winter 

months, depending on the location of trees around the building. Estimates of tree 

eff ects on energy use are based on fi eld measurements of tree distance and direction to 

space-conditioned residential buildings.9

Based on average state energy costs in 2007, trees in urban Scranton are estimated 

to reduce energy costs from residential buildings by $628,000 annually. Trees also 

provide an additional $16,700 in value per year by reducing amount of carbon released 

by fossil-fuel based power plants (a reduction of 700 tons of carbon emissions).

Trees affect energy 

consumption by 

shading buildings, 

providing 

evaporative cooling, 

and blocking winter 

winds. 

Interactions between 

buildings and trees 

are estimated to 

reduce residential 

heating and cooling 

costs by $628,000 

per year.

Annual energy savings due to trees near residential buildings

Heating Cooling Total

MBTUa 32,000 n/a 32,000

MWHb 300 1200 1,500

Carbon avoided (t) 500 200 700

a

Million British Th ermal Units

b

Megawatt-hour

Annual savingsc (U.S. $) in residential energy expenditures during heating 

and cooling seasons.

Heating Cooling Total

MBTUa 463,000 n/a 463,000

MWHb 30,000 135,000 165,000

Carbon avoided 12,200 4,500 16,700

a

Million British Th ermal Units

b

Megawatt-hour

c

Based on state-wide energy costs

Jeffrey from Dunmore PA. 

wikimedia commons.

Richard Webb, Self-employed horticulurist, Bugwood.org
10

Structural and Functional Values

Urban forests have a structural value based on the tree itself (e.g., the cost of having 

to replace the tree with a similar tree). Th e structural value10 of the trees and forests in 

urban Scranton is about $322 million. Th e structural value of an urban forest tends to 

increase with a rise in the number and size of healthy trees.

Urban forests also have functional values (either positive or negative) based on the 

functions the tree performs. Annual functional values also tend to increase with 

increased number and size of healthy trees, and are usually on the order of several 

million dollars per year. Th ere are many other functional values of the urban forest, 

though they are not quantifi ed here (e.g., reduction in air temperatures and ultra-violet 

radiation, improvements in water quality, etc.). Th rough proper management, urban 

forest values can be increased. However, the values and benefi ts also can decrease as the 

amount of healthy tree cover declines.

Structural values:

• Structural value: $322 million

• Carbon storage: $1.9 million

Annual functional values:

• Carbon sequestration: $83,000

• Pollution removal: $514,000

• Reduced energy costs: $628,000

More detailed information on the trees and forests in urban Scranton can be found at 

http://nrs.fs.fed.us/data/urban. Additionally, information on other urban forest values 

can be found in Appendix I and information comparing tree benefi ts to estimates 

of average carbon emissions in the city, average automobile emissions, and average 

household emissions can be found in Appendix III.

Urban forests have 

a structural value 

based on the tree 

itself.

Urban forests also 

have functional 

values based on the 

functions the tree 

performs.

Large, healthy, 

long-lived trees 

provide the greatest 

structural and 

functional values.

A map of priority 

planting locations 

for the City of 

Scranton is found in 

Appendix V.

A list of tree species 

found in the City 

of Scranton is in 

Appendix IV. 

Joseph O’brien, US Forest Service, 

forestryimages.com

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Structural value ($ millions)
11

Potential Insect and Disease Impacts

Various insects and diseases can infest urban forests, potentially killing trees and 

reducing the health, value and sustainability of the urban forest. As various pests have 

diff ering tree hosts, the potential damage or risk of each pest will diff er. Four exotic 

pests were analyzed for their potential impact: Asian longhorned beetle, gypsy moth, 

emerald ash borer, and Dutch elm disease.

Th e Asian longhorned beetle (ALB)16 is an insect that bores into and kills a wide range 

of hardwood species. Th is beetle was discovered in 1996 in Brooklyn, NY and has 

subsequently spread to Long Island, Queens and Manhattan. In 1998, the beetle was 

discovered in the suburbs of Chicago, IL. Beetles have also been found in Jersey City, NJ 

(2002), Toronto/Vaughan, Ontario (2003), and Middlesex/Union Counties, NJ (2004). 

In 2007, the beetle was found on Staten and Prall’s Island, NY. Most recently, beetles 

were detected in Worcester, MA (2008). In urban Scranton, this beetle represents a 

potential loss of $151 million in structural value (65.2 percent of live tree population).

Th e gypsy moth (GM)17 is a defoliator that feeds on many species causing widespread 

defoliation and tree death if outbreak conditions last several years. Th is pest could 

potentially result in damage to or a loss of $116 million in structural value of urban 

Scranton’s trees (39.7 percent of live tree population).

Since being discovered in Detroit in 2002, emerald ash borer (EAB)18 has killed 

millions of ash trees in Illinois, Indiana, Kentucky, Maryland, Michigan, Minnesota, 

Missouri, New York, Ohio, Ontario, Pennsylvania, Quebec, Virginia, West Virginia, 

and Wisconsin. EAB has the potential to aff ect 2.4 percent of urban Scranton’s live 

tree population ($5.8 million in structural value).

American elm, one of the most important street trees in the 20th century, has been 

devastated by Dutch elm disease (DED). Since fi rst reported in the 1930s, it has killed 

more than 50 percent of the native elm population in the United States.19 Although 

some elm species have shown varying degrees of resistance, urban Scranton possibly 

could lose 0.4 percent of its live trees to this disease ($2.8 million in structural value).

Asian longhorned 

beetle

Emerald ash borer

Gypsy moth

Kenneth R. Law 

USDA APHIS PPQ 

(www.invasive.org)

David Cappaert

Michigan State University

(www.invasive.org)

USDA Forest Service Archives 

(www.invasive.org)

Dutch elm disease

USDA Forest Service

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ALB GM EAB DED

Compensatory value ($ million)

Number of trees

Population at risk

Compensatory Value
12

Appendix I. Comparison of Urban Forests

A commonly asked question is, “How does this city compare to other cities?” Although comparison among 

cities should be made with caution as there are many attributes of a city that aff ect urban forest structure and 

functions, summary data are provided from other cities analyzed using the UFORE model.

I. City totals, trees only

City

% Tree 

cover

Number of 

trees

Carbon 

storage (tons)

Carbon 

sequestration 

(tons/yr)

Pollution 

removal 

(tons/yr)1

Pollution 

removal value

U.S. $2

Calgary, Albertaa 7.2 11,889,000 445,000 21,400 326 2,357,000

Toronto, Ontarioa

* 19.9 10,220,000 1,221,000 51,500 1,283 10,474,000

Atlanta, GAb 36.7 9,415,000 1,344,000 46,400 1,663 12,213,000

Sacramento, CAc

* 17.0 6,889,000 1,487,000 71,700 2,914 21,730,000

New York, NYb 20.9 5,212,000 1,350,000 42,300 1,677 11,834,000

Chicago, ILd 17.2 3,585,000 716,000 25,200 888 6,398,000

Baltimore, MDe 21.0 2,627,000 597,000 16,200 430 3,123,000

Philadelphia, PAb 15.7 2,113,000 530,000 16,100 576 4,150,000

Washington, DCf 28.6 1,928,000 526,000 16,200 418 2,858,000

Oakville, Ontariog 29.1 1,908,000 147,000 6,600 190 1,421,000

Scranton, PAh 22.0 1,198,000 93,000 4,000 72 514,000

Boston, MAb 22.3 1,183,000 319,000 10,500 284 2,092,000

Woodbridge, NJi 29.5 986,000 160,000 5,600 210 1,525,000

Minneapolis, MNj 26.4 979,000 250,000 8,900 306 2,242,000

Syracuse, NYe 23.1 876,000 173,000 5,400 109 836,000

San Francisco, CAa 11.9 668,000 194,000 5,100 141 1,018,000

Morgantown, WVk 35.5 658,000 93,000 2,900 72 485,000

Moorestown, NJi 28.0 583,000 117,000 3,800 118 841,000

Jersey City, NJi 11.5 136,000 21,000 890 41 292,000

Casper, WYa 8.9 123,000 37,000 1,200 37 275,000

Freehold, NJi 34.4 48,000 20,000 550 22 162,000

II. Per acre values of tree eff ects

City

No. of 

trees

Carbon Storage 

(tons)

Carbon 

sequestration 

(tons/yr)

Pollution

removal 

(lbs/yr)

Pollution

removal value 

U.S. $1

Calgary, Albertaa 66.7 2.5 0.12 3.7 13.2

Toronto, Ontarioa

* 64.9 7.8 0.33 16.3 66.5

Atlanta, GAb 111.6 15.9 0.55 39.4 144.8

Sacramento, CAc

* 21.3 4.6 0.22 18.0 67.3

New York, NYb 26.4 6.8 0.21 17.0 59.9

Chicago, ILd 24.3 4.8 0.17 12.0 43.3

Baltimore, MDe 50.8 11.6 0.31 16.6 60.4

Philadelphia, PAb 25.1 6.3 0.19 13.6 49.2

Washington, DCf 49.0 13.4 0.41 21.2 72.7

Oakville, Ontariog 55.6 4.3 0.19 11.0 41.4

Scranton, PAh 116.4 9.1 0.39 13.9 49.9

Boston, MAb 33.5 9.1 0.30 16.1 59.3

Woodbridge, NJi 66.5 10.8 0.38 28.4 102.9

continued
13

Minneapolis, MNj 26.2 6.7 0.24 16.3 60.1

Syracuse, NYe 54.5 10.8 0.34 13.6 52.0

San Francisco, CAa 22.5 6.6 0.17 9.5 34.4

Morgantown, WVk 119.2 16.8 0.52 25.9 87.8

Moorestown, NJi 62.1 12.4 0.40 25.1 89.5

Jersey City, NJi 14.4 2.2 0.09 8.6 30.8

Casper, WYa 9.1 2.8 0.09 5.5 20.4

Freehold, NJi 38.3 16.0 0.44 35.3 130.1 1

 Pollution removal and values are for carbon monoxide, sulfur and nitrogen dioxide, ozone, and particulate matter less than 10 

microns (PM10).

2

 Pollution values updated to 2007 values.

* includes shrub cover in tree cover estimate based on photo-interpretation

Data collection group

a

 City personnel

b

 ACRT, Inc.

c

 Sacramento Tree Foundation

d

 Various Departments of the City of Chicago

e

 U.S. Forest Service

f

 Casey Trees Endowment Fund

g

 City personnel, urban boundary of city

h Northeast Pennsylvania Urban & Community Forestry Program staff , Keystone College interns, Penn State Extension 

Urban Forester, and DCNR Bureau of Forestry staff i

 New Jersey Department of Environmental Protection

j

 Davey Resource Group

k

 West Virginia University

Appendix I.—continued

City

% Tree 

cover

Number of 

trees

Carbon 

storage (tons)

Carbon 

sequestration 

(tons/yr)

Pollution 

removal 

(tons/yr)1

Pollution 

removal value

U.S. $1
14

Appendix II. General Recommendations for Air Quality Improvement

Urban vegetation can directly and indirectly aff ect local and regional air quality by altering the urban atmospheric 

environment. Four main ways that urban trees aff ect air quality are:

Temperature reduction and other microclimatic eff ects

Removal of air pollutants

Emission of volatile organic compounds (VOC) and tree maintenance emissions

Energy conservation in buildings and consequent power plant emissions

Th e cumulative and interactive eff ects of trees on climate, pollution removal, and VOC and power plant emissions 

determine the overall impact of trees on air pollution. Cumulative studies involving urban tree impacts on ozone 

have revealed that increased urban canopy cover, particularly with low VOC emitting species, leads to reduced 

ozone concentrations in cities. Local urban forest management decisions also can help improve air quality.

Urban forest management strategies to help improve air quality include:

Strategy Reason

Increase the number of healthy trees Increase pollution removal

Sustain existing tree cover Maintain pollution removal levels

Maximize use of low VOC-emitting trees Reduces ozone and carbon monoxide formation

Sustain large, healthy trees Large trees have greatest per-tree eff ects

Use long-lived trees Reduce long-term pollutant emissions from planting and removal

Use low maintenance trees Reduce pollutants emissions from maintenance activities

Reduce fossil fuel use in maintaining vegetation Reduce pollutant emissions

Plant trees in energy conserving locations Reduce pollutant emissions from power plants

Plant trees to shade parked cars Reduce vehicular VOC emissions

Supply ample water to vegetation Enhance pollution removal and temperature reduction

Plant trees in polluted or heavily populated areas Maximizes tree air quality benefi ts

Avoid pollutant-sensitive species Improve tree health

Utilize evergreen trees for particulate matter Year-round removal of particles
15

a

 miles = number of automobile miles driven that produces emissions equivalent to tree eff ect

Carbon sequestration

Pollution

Carbon storage removal

D.b.h.

Class (inch) (lbs) ($) (miles) a (lbs/yr) ($/yr) (miles)a (lbs) ($)

3-6 7 0.07 20 1.3 0.01 5 0.02 0.06

6-9 43 0.45 160 4.9 0.05 18 0.1 0.22

9-12 154 1.60 570 10.4 0.11 38 0.2 0.66

12-15 342 3.54 1,250 16.6 0.17 61 0.3 1.17

15-18 665 6.88 2,430 25.3 0.26 93 0.5 2.00

18-21 1,031 10.66 3,770 30.3 0.31 111 0.6 2.38

21-24 1,428 14.78 5,230 42.6 0.44 156 0.8 3.06

24-27 2,173 22.48 7,960 54.9 0.57 201 0.8 3.10

27-30 2,703 27.96 9,900 56.9 0.59 208 1.1 4.38

30+ 4,579 47.37 16,770 72.4 0.75 265 0.9 3.40

30+ 5,502 56.92 20,150 109.8 1.14 402 1.6 6.44

Appendix III. Relative Tree Effects

Th e urban forest in Scranton provides benefi ts that include carbon storage and sequestration, and air pollutant 

removal. To estimate a relative value of these benefi ts, tree benefi ts were compared to estimates of average carbon 

emissions in the city20, average passenger automobile emissions21, and average household emissions.22

General tree information:

Average tree diameter (d.b.h.) = 4.6 in.

Median tree diameter (d.b.h.) = 2.8 in.

Number of live trees sampled = 1,798

Number of species sampled = 80

Average tree eff ects by tree diameter:

Th e trees in urban Scranton provide:

Carbon storage equivalent to:

Amount of carbon (C) emitted in city in 78 days or

Annual carbon emissions from 56,000 automobiles or 

Annual C emissions from 28,100 single family houses

Carbon monoxide removal equivalent to:

Annual carbon monoxide emissions from 5 automobiles or

Annual carbon monoxide emissions from <1 single family 

houses

Nitrogen dioxide removal equivalent to:

Annual nitrogen dioxide emissions from 500 automobiles or

Annual nitrogen dioxide emissions from 300 single family 

houses

Sulfur dioxide removal equivalent to:

Annual sulfur dioxide emissions from 9,900 automobiles or

Annual sulfur dioxide emissions from 200 single family 

houses

Particulate matter less than 10 micron (PM10) removal 

equivalent to:

Annual PM10 emissions from 15,900 automobiles or

Annual PM10 emissions from 1,500 single family houses

Annual C sequestration equivalent to:

Amount of C emitted in city in 3.3 days or

Annual C emissions from 2,400 automobiles or

Annual C emissions from 1,200 single family homes
16

Continued

% %

Genus Species Common Name Population Leaf Area IVa

Abies concolor White fi r 0.1 0.2 0.3

Acer negundo Boxelder 0.4 0.4 0.8

Acer palmatum Japanese maple 0.2 0.2 0.4

Acer platanoides Norway maple 3.6 10.8 14.4

Acer rubrum red maple 10.6 10.6 21.2

Acer saccharinum Silver maple 1.7 6.7 8.4

Aesculus hippocastanum Horsechestnut 0 0 0

Ailanthus altissima Tree of heaven 1.5 2 3.5

Amelanchier species Serviceberry 0 0 0

Betula lenta Black birch 5.6 3.2 8.8

Betula nigra River birch 0.1 0.5 0.6

Betula papyrifera Paper birch 0.1 0.1 0.2

Betula pendula European white birch 0.2 0.1 0.3

Betula populifolia Gray birch 10.1 2.7 12.8

Betula species Birch 0 0 0

Carya species Hickory 1.6 0.9 2.5

Castanea dentata American chestnut 0 0 0

Catalpa species Catalpa 0.5 1 1.5

Chionanthus virginicus Fringe tree 0 0.1 0.1

Cornus alternifolia Alternateleaf dogwood 0 0 0

Cornus fl orida Flowering dogwood 0.2 0.4 0.6

Cornus kousa Kousa dogwood 0 0 0

Crataegus species Hawthorn 0.7 0.1 0.8

Elaeagnus umbellata Autumn olive 0.4 0.2 0.6

Euonymus alatus Winged burningbush 0 0 0

Fagus grandifolia American beech 0 0 0

Fagus sylvatica European beech 0 0 0

Fraxinus americana White ash 1.9 1.8 3.7

Fraxinus pennsylvanica Green ash 0.2 0 0.2

Gleditsia triacanthos Honeylocust 0.1 0.3 0.4

Hamamelis virginiana Witch hazel 1.2 0.4 1.6

Juglans nigra Black walnut 0.6 1.4 2

Juniperus chinensis Chinese juniper 0.1 0 0.1

Juniperus species Juniper 0 0 0

Kalmia latifolia Mountain laurel 0.3 0 0.3

Larix decidua European larch 0 0 0

Lonicera tatarica Tartarian honeysuckle 0 0 0

Appendix IV. List of Species Sampled in Scranton
17

Appendix IV continued.

Continued

% %

Genus Species Common Name Population Leaf Area IVa

Malus species Crabapple 1.3 0.9 2.2

Malus tschonoskii Tschonoskii crabapple 0.2 0.2 0.4

Morus alba White mulberry 0 0 0

Nyssa sylvatica Black tupelo 2.6 1 3.6

Philadelphus coronarius Sweet mock orange 0 0 0

Picea abies Norway spruce 0.9 6.2 7.1

Picea pungens Blue spruce 0.8 3.7 4.5

Pinus nigra Austrian pine 0 0 0

Pinus sylvestris Scotch pine 0.6 0.2 0.8

Platanus occidentalis American sycamore 0 0.7 0.7

Populus deltoides Eastern cottonwood 0.1 0.1 0.2

Populus grandidentata Bigtooth aspen 2.2 1.7 3.9

Populus tremuloides Quaking aspen 7.1 2.5 9.6

Prunus americana American plum 0 0 0

Prunus avium Sweet cherry 0.7 0.1 0.8

Prunus cerasifera Cherry plum 0.2 0.2 0.4

Prunus pensylvanica Pin cherry 1.9 0.8 2.7

Prunus serotina Black cherry 8.9 7.3 16.2

Prunus virginiana Common chokecherry 0.7 0.4 1.1

Prunus yedoensis Yoshino fl owering cherry 0 0 0

Pseudotsuga menziesii Douglas fi r 0.1 0.1 0.2

Pyrus calleryana Callery pear 0.2 0.6 0.8

Pyrus species Pear 0.1 0.2 0.3

Quercus alba White oak 2.3 3 5.3

Quercus coccinea Scarlet oak 1.2 1.5 2.7

Quercus ilicifolia Bear oak 1.4 0.6 2

Quercus palustris Pin oak 0.3 0.5 0.8

Quercus prinus Chestnut oak 2.6 2.9 5.5

Quercus rubra Northern red oak 8.1 11.3 19.4

Quercus velutina Black oak 0.8 0.6 1.4

Rhamnus species Buckthorn 0 0 0

Rhododendron azalea Azalea 0.1 0 0.1

Rhododendron species Rhododendron 0 0 0

Rhus species Sumac 3.4 0.2 3.6

Robinia pseudoacacia Black locust 5.4 2.8 8.2

Rosa banksiae Banksian rose 0 0 0

Salix nigra Black willow 0 0.3 0.3
18

Appendix IV continued.

% %

Genus Species Common Name Population Leaf Area IVa

Sassafras albidum Sassafras 0.8 0.2 1

Syringa species Lilac 0 0 0

Taxus baccata English yew 0.2 0.4 0.6

Taxus cuspidata Japanese yew 0.1 0.1 0.2

Th uja occidentalis Northern white cedar 0.1 0 0.1

Tsuga canadensis Eastern hemlock 0.8 3.1 3.9

Ulmus americana American elm 0.4 0.9 1.3

Ulmus pumila Siberian elm 0.1 0.4 0.5

Ulmus species Elm 0.1 0 0.1

a

 IV = importance value (% population + % leaf area)

Note: 0% indicates a value less than 0.05% but greater than 0
19

Appendix V. Tree Planting Index Map

To determine the best locations to plant trees, tree canopy 

and impervious cover maps from National Land Cover 

Data23 were used in conjunction with 2000 U.S. Census 

data to produce an index of priority planting areas. Index 

values were produced for each census block group with the 

higher the index value, the higher the priority of the area 

for tree planting. Th is index is a type of “environmental 

equity” index with areas with higher human population 

density and lower tree cover tending to get the higher 

index value. Th e criteria used to make the index were:

• Population density: the greater the population 

density, the greater the priority for tree planting

• Tree stocking levels: the lower the tree stocking 

level (the percent of available greenspace (tree, 

grass, and soil cover areas) that is occupied by tree 

canopies), the greater the priority for tree planting

Legend

PPI

0.0 - 10.0

10.1 - 20.0

20.1 - 30.0

30.1 - 40.0

40.1 - 50.0

50.1 - 60.0

60.1 - 70.0

70.1 - 80.0

80.1 - 90.0

90.1 - 100.0

• Tree cover per capita: the lower the amount of 

tree canopy cover per capita (m2

/capita), the 

greater the priority for tree planting

Each criteria was standardized24 on a scale of 0 to 1 with 

1 representing the census block group with the highest 

value in relation to priority of tree planting (i.e., the 

census block group with highest population density, 

lowest stocking density or lowest tree cover per capita 

were standardized to a rating of 1). Individual scores 

were combined and standardized based on the following 

formula to produce an overall priority planting index 

(PPI) value between 0 and 100:

PPI = (PD * 40) + (TS * 30) + (TPC * 30)

Where PPI = index value, PD is standardized population 

density, TS is standardized tree stocking, and TPC is 

standardized tree cover per capita.

01234 0.5

Kilometers

.

Scranton
20

References

1 Nowak, D.J.; Crane, D.E. 2000. Th e Urban Forest 

Eff ects (UFORE) Model: quantifying urban 

forest structure and functions. In: Hansen, M.; 

Burk, T., eds. Integrated tools for natural resources 

inventories in the 21st century. Proceedings of 

IUFRO conference. Gen. Tech. Rep. NC-212. St. 

Paul, MN: U.S. Department of Agriculture, Forest 

Service, North Central Research Station: 714-720.

2 Nowak, D.J.; Crane, D.E.; Stevens, J.C.; Hoehn, R.E. 

2005. Th e urban forest eff ects (UFORE) model: 

fi eld data collection manual. V1b. [Newtown 

Square, PA]: U.S. Department of Agriculture, 

Forest Service, Northeastern Research Station. 34 p. 

http://www.fs.fed.us/ne/syracuse/Tools/downloads/

UFORE_Manual.pdf

3 Nowak, D.J. 1994. Atmospheric carbon dioxide 

reduction by Chicago’s urban forest. In: 

McPherson, E.G.; Nowak, D.J.; Rowntree, R.A., 

eds. Chicago’s urban forest ecosystem: results of 

the Chicago Urban Forest Climate Project. Gen. 

Tech. Rep. NE-186. Radnor, PA: U.S. Department 

of Agriculture, Forest Service, Northeastern Forest 

Experiment Station: 83-94.

4 Baldocchi, D. 1988. A multi-layer model for 

estimating sulfur dioxide deposition to a 

deciduous oak forest canopy. Atmospheric 

Environment. 22: 869-884.

5 Baldocchi, D.D.; Hicks, B.B.; Camara, P. 1987. A 

canopy stomatal resistance model for gaseous 

deposition to vegetated surfaces. Atmospheric 

Environment. 21: 91-101.

6 Bidwell, R.G.S.; Fraser, D.E. 1972. Carbon 

monoxide uptake and metabolism by leaves.

Canadian Journal of Botany. 50: 1435-1439. 

7 Lovett, G.M. 1994. Atmospheric deposition of 

nutrients and pollutants in North America: an 

ecological perspective. Ecological Applications. 4: 

629-650.

8 Zinke, P.J. 1967. Forest interception studies in the 

United States. In: Sopper, W.E.; Lull, H.W., eds. 

Forest hydrology. Oxford, UK: Pergamon Press: 

137-161.

9 McPherson, E.G.; Simpson, J.R. 1999. Carbon 

dioxide reduction through urban forestry: 

guidelines for professional and volunteer tree 

planters. Gen. Tech. Rep. PSW-171. Albany, CA: 

U.S. Department of Agriculture, Forest Service, 

Pacifi c Southwest Research Station. 237 p. Can 

be accessed through http://www.fs.fed.us/psw/

publications/gtrs.shtml

10 Nowak, D.J.; Crane, D.E.; Dwyer, J.F. 2002. 

Compensatory value of urban trees in the United 

States. Journal of Arboriculture. 28(4): 194-199.

11 Nowak, D.J.; Crane, D.E.; Stevens, J.C.; Ibarra, M. 

2002. Brooklyn’s urban forest. Gen. Tech. Rep. 

NE-290. Newtown Square, PA: U.S. Department 

of Agriculture, Forest Service, Northeastern 

Research Station. 107 p.

12 Based on photo-interpretation of Google Earth 

imagery (image date: April 2005) of 500 random 

points. Standard error of estimate is 1.9%.

13 Nowak D.J.; Dwyer, J.F. 2000. Understanding 

the benefi ts and costs of urban forest ecosystems.

In: Kuser, John E., ed. Handbook of urban and 

community forestry in the northeast. New York: 

Kluwer Academics/Plenum: 11-22.

14 Murray, F.J.; Marsh L.; Bradford, P.A. 1994. New 

York state energy plan, vol. II: issue reports.

Albany, NY: New York State Energy Offi ce. Th ese 

values were updated to 2007 dollars based on the 

producer price index from U.S Department of 

Labor, Bureau of Labor Statistics n.d. www.bls.gov/

ppi
21

15 Abdollahi, K.K.; Ning, Z.H.; Appeaning, A., eds. 

2000. Global climate change and the urban 

forest. Baton Rouge, LA: GCRCC and Franklin 

Press. 77 p.

16 USDA Animal and Plant Health Inspection Service. 

2010. Plant Health—Asian Longhorned Beetle.

http://www.aphis.usda.gov/plant_health/plant_

pest_info/asian_lhb/index.shtml; 

Natural Resources Canada. 2010. Sustainability 

Indicators—Asian Longhorned Beetle.

http://canadaforests.nrcan.gc.ca/indicator/

asianlonghornedbeetle

17 Northeastern Area State and Private Forestry. 

2005. Gypsy moth digest. Newtown Square, PA: 

U.S. Department of Agriculture, Forest Service, 

Northeastern Area State and Private Forestry. 

http://www.na.fs.fed.us/fhp/gm/

18 USDA Forest Service et al. 2010. Emerald ash 

borer. http://www.emeraldashborer.info/

19 Stack, R.W.; McBride, D.K.; Lamey, H.A. 1996. 

Dutch elm disease. PP-324 (revised). Fargo, 

ND: North Dakota State University, Cooperative 

Extension Service. http://www.ext.nodak.edu/

extpubs/plantsci/trees/pp324w.htm
22

Explanation of Calculations of Appendix III and IV

20 Total city carbon emissions were based on 2003 

U.S. per capita carbon emissions, calculated as 

total U.S. carbon emissions (Energy Information 

Administration, 2003, Emissions of Greenhouse 

Gases in the United States 2003. http://www.eia.

doe.gov/oiaf/1605/1605aold.html) divided by 

2003 total U.S. population (www.census.gov). Per 

capita emissions were multiplied by Minneapolis 

population to estimate total city carbon emissions.

21 Average passenger automobile emissions per 

mile were based on dividing total 2002 pollutant 

emissions from light-duty gas vehicles (National 

Emission Trends http://www.epa.gov/ttn/chief/

trends/index.html) by total miles driven in 2002 by 

passenger cars (National Transportation Statistics 

http://www.bts.gov/publications/national_

transportation_statistics/2004/).

Average annual passenger automobile emissions 

per vehicle were based on dividing total 2002 

pollutant emissions from light-duty gas vehicles 

by total number of passenger cars in 2002 

(National Transportation Statistics http://www.

bts.gov/publications/national_transportation_

statistics/2004/).

Carbon dioxide emissions from automobiles 

assumed 6 pounds of carbon per gallon of gasoline 

with energy costs of refi nement and transportation 

included (Graham, R.L.; Wright, L.L.; Turhollow, 

A.F. 1992. Th e potential for short-rotation woody 

crops to reduce U.S. CO2

 emissions. Climatic 

Change. 22: 223-238.)

22 Average household emissions based on average 

electricity kWh usage, natural gas Btu usage, fuel oil 

Btu usage, kerosene Btu usage, LPG Btu usage, and 

wood Btu usage per household from:

Energy Information Administration. Total Energy 

Consumption in U.S. Households by Type of 

Housing Unit, 2001 www.eia.doe.gov/emeu/recs/

recs2001/detailcetbls.html. 

CO2

, SO2, and NOx power plant emission per 

KWh from:

U.S. Environmental Protection Agency. U.S. 

power plant emissions total by year www.epa.gov/

cleanenergy/egrid/samples.htm.

CO emission per kWh assumes one-third of 1 

percent of C emissions is CO based on:

Energy Information Administration. 1994. 

Energy use and carbon emissions: non-OECD 

countries. DOE/EIA-0579(94). Washington, 

DC: Department of Energy, Energy Information 

Administration. http://tonto.eia.doe.gov/bookshelf

PM10 emission per kWh from: 

Layton, M. 2004. 2005 Electricity environmental 

performance report: electricity generation and air 

emissions. Sacramento, CA: California Energy 

Commission. 

http://www.energy.ca.gov/2005_energypolicy/

documents/2004-11-15_workshop/2004-11-

15_03-A_LAYTON.PDF

CO2

, NOx, SO2, PM10, and CO emission per Btu 

for natural gas, propane and butane (average used 

to represent LPG), Fuel #4 and #6 (average used to 

represent fuel oil and kerosene) from:

Abraxas energy consulting. http://www.

abraxasenergy.com/emissions/ 

CO2

 and fi ne particle emissions per Btu of wood 

from:

 Houck, J.E.; Tiegs, P.E.; McCrillis, R.C.; Keithley, 

C.; Crouch, J. 1998. Air emissions from residential 

heating: the wood heating option put into 
23

environmental perspective. In: Proceedings of U.S. 

EPA and Air and Waste Management Association 

conference: living in a global environment, V.1: 

373-384.

CO, NOx and SOx emission per Btu of wood based 

on total emissions from wood burning (tonnes) 

from: 

Residential Wood Burning Emissions in British 

Columbia. 2005. http://www.env.gov.bc.ca/air/

airquality/pdfs/wood_emissions.pdf.

Emissions per dry tonne of wood converted to 

emissions per Btu based on average dry weight per 

cord of wood and average Btu per cord from:

Kuhns, M.; Schmidt, T. 1988. Heating with wood: 

species characteristics and volumes I. NebGuide 

G-88-881-A. Lincoln, NE: University of Nebraska, 

Institute of Agriculture and Natural Resources, 

Cooperative Extension.

23 National Land Cover Data are available at: www.

epa.gov/mrlc/nlcd-2001.html

24 Standardized value for population density was 

calculated as PD = (n – m)/r, where PD is the 

value (0-1), n is the value for the census block 

(population / km2

), m is the minimum value for all 

census blocks, and r is the range of values among all 

census blocks (maximum value – minimum value). 

Standardized value for tree stocking was calculated 

as TS = 1 – [t/(t+g)], where TS is the value (0-1), 

t is percent tree cover, and g is percent grass cover. 

Standardized value for tree cover per capita was 

calculated as TPC = 1 – [(n – m)/r], where TPC is 

the value (0-1), n is the value for the census block 

(m2

/capita), m is the minimum value for all census 

blocks, and r is the range of values among all census 

blocks (maximum value – minimum value).
Printed on Recycled Paper

Nowak, David J.; Hoehn, Robert E. III; Crane, Daniel E.; Stevens, Jack C.; Cotrone, 

Vincent. 2010. Assessing urban forest effects and values, Scranton’s 

urban forest. Resour. Bull. NRS-43. Newtown Square, PA: U.S. Department of 

Agriculture, Forest Service, Northern Research Station. 23 p.

An analysis of trees in the urbanized portion of Scranton, PA, reveals that this 

area has about 1.2 million trees with canopies that cover 22.0 percent of the area. 

The most common tree species are red maple, gray birch, black cherry, northern 

red oak, and quaking aspen. Scranton’s urban forest currently store about 93,300 

tons of carbon valued at $1.9 million. In addition, these trees remove about 4,000 

tons of carbon per year ($83,000 per year) and about 65 tons of air pollution per 

year ($514,000 per year). Trees in urban Scranton are estimated to reduce annual 

residential energy costs by $628,000 per year. The structural, or compensatory, value 

is estimated at $322 million. Information on the structure and functions of the urban 

forest can be used to inform urban forest management programs and to integrate 

urban forests within plans to improve environmental quality in the Scranton area.

KEY WORDS: urban forestry, ecosystem services, air pollution removal, carbon 

sequestration, tree value
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