Center for

Urban Forest Research

City of Boise, Idaho

Municipal Forest Resource Analysis

By

Paula J. Peper

E. Gregory McPherson

James R. Simpson

Shelley L. Gardner

Kelaine E. Vargas

Qingfu Xiao

Center for Urban Forest Research

USDA Forest Service, Pacific Southwest Research Station

Technical Report to:

Jerry Stallsmith, Boise City Forester

Boise Parks and Recreation Department

City of Boise, Idaho

 —June 2007—

CITY OF BOISE, IDAHO

MUNICIPAL FOREST RESOURCE ANALYSIS

Technical report to:

Jerry Stallsmith, Boise City Forester

Boise Parks and Recreation Department

City of Boise, Idaho 

By

Paula J. Peper1

E. Gregory McPherson1

James R. Simpson1

Shelley L. Gardner1

Kelaine E. Vargas1

Qingfu Xiao2

—June 2007—

1

Center for Urban Forest Research

USDA Forest Service, Pacific Southwest Research Station

c/o Dept. of Plant Science, MS-6

University of California

One Shields Ave.

Davis, CA 95616-8587

2

Department of Land, Air, and Water Resources

University of California

Davis, CA
Acknowledgements

We greatly appreciate the support and assistance provided by Brian Jorgenson, Jerry Stallsmith, Eric Wing, 

Amy Stahl (City of Boise Department of Parks & Recreation); Les Bois Tree Task Force; Scott Maco, Jim 

Jenkins, Aren Dottenwhy (Davey Resource Group); Johanna Bell (Boise Department of Public Works); 

Kevin Allen (Pro Care Landscape Services); Eddie Brock (The Lawn Company Maintenance); Mary Jane 

Fields (DG Turf Farm & Nursery); John P. Franks (City of Payette); Gene Gray (Forestry Commission); 

R. Michael Bowman (City of Lewiston); Earl Moran (City of Nampa); Paul Nelson (City of Caldwell); 

Janet Davis & Ezekiel T. Willard (Idaho Tree Preservation); Stephanie Huang, Christine Yang, Aywon-Anh 

Nguyen, (CUFR).

Mark Buscaino, Larry Payne, Keith Cline, Barbara Hollenbeck (USDA Forest Service, State and Private 

Forestry, U&CF), Margie Ewing (USDA Forest Service, State & Private Forestry, U&CF, Northern and Intermountain Regions), David Stephenson (Idaho Department of Lands) and Susan Stead (Nevada Divi-sion 

of Forestry) provided invaluable support for this project.
Table of Contents

Executive Summary 1

Resource Structure 1

Resource Function and Value 2

Resource Management 3

Chapter One—Introduction 5

Chapter Two—Boise’s Municipal Tree Resource 7

Tree Numbers 7

Street Tree Stocking Level 8

Municipal ROW Trees Per Capita 9

Species Richness, Composition and Diversity 9

Species Importance 9

Age Structure 11

Tree Condition 13

Tree Canopy 15

Replacement Value 15

Chapter Three—Costs of Managing Boise’s Municipal Trees 17

Tree Planting and Establishment 17

Pruning, Removals, and General Tree Care 17

Administration 18

Other Tree-Related Expenditures 18

Chapter Four—Benefits of Boise’s Municipal Trees 19

Energy Savings 19

Electricity and Natural Gas Results 20

Atmospheric Carbon Dioxide Reduction 22

Avoided and Sequestered Carbon Dioxide 22

Air Quality Improvement 22

Deposition and Interception 22

Avoided Pollutants 24

BVOC Emissions 24

Net Air Quality Improvement 25

Stormwater Runoff Reductions 25

Aesthetic, Property Value, Social, Economic and Other Benefits 25

Total Annual Net Benefits and Benefit–Cost Ratio 27
Chapter Five—Management Implications 31

Resource Complexity 31

Resource Extent 32

ROW Canopy Cover 33

Maintenance 33

Chapter Six—Conclusion 35

Appendix A—Tree Distribution 37

Appendix B—Replacement Values 43

Appendix C—Methodology and Procedures 47

Growth Modeling 47

Replacement Value 48

Identifying and Calculating Benefits 49

Energy Savings 49

Atmospheric Carbon Dioxide Reduction 56

Improving Air Quality 56

Reducing Stormwater Runoff 57

Property Value and Other Benefits 58

Estimating Magnitude of Benefits 60

Categorizing Trees by DBH Class 60

Applying Resource Units to Each Tree 60

Matching Significant Species with Modeled Species 60

Calculating Net Benefits And Benefit–Cost Ratio 61

References 63
1

Executive Summary

Boise, the capital and largest city in the state of 

Idaho, maintains parks and street trees as an integral component of the urban infrastructure (Figure 1). Located along the Boise River and nestled 

against foothills of the Rocky Mountains, Boise 

is renowned for its unique blend of natural beauty 

and urban comforts.

Trees are a critical component of the city in general. Research indicates that healthy trees can lessen 

im-pacts associated with the built environment by 

reducing stormwater runoff, energy consumption, 

and air pollutants. Trees improve urban life, making Boise a more enjoyable place to live, work, 

and play, while mitigating the city’s environmental 

impact. Over the past century, Boise residents and 

the City have been developing their urban forest on 

public and private properties. This report evaluates 

Boise street and park trees on the street public rightof-way only. The primary question that this study 

asks is whether the accrued benefits from Boise’s 

right-of-way trees justify the annual expenditures? 

This analysis combines results of a citywide inventory with benefit–cost modeling data to produce 

four types of information on the city-managed 

ROW tree resource:

• Structure (species composition, diversity, 

age distribution, condition, etc.)

• Function (magnitude of annual environmental and aesthetic benefits)

• Value (dollar value of benefits minus management costs)

• Management needs (sustainability, planting, maintenance)

Resource Structure

Boise’s tree inventory includes 23,262 publicly 

managed trees along the right of way (ROW). 

Figure 1—Trees shade a Boise neighborhood. Street trees in Boise provide great benefits, improving air quality, 

sequestering carbon dioxide, reducing stormwater runoff and beautifying the city. The trees of Boise return $1.30 in 

benefits for every $1 spent on tree care
2

These include 179 tree species with Norway maple 

(Acer platanoides), silver maple (Acer saccharinum), honeylocust (Gleditsia triacanthos), green 

ash (Fraxinus pennsylvanica), and crabapple (Malus spp.) the predominant species. The managers 

of the city’s street trees can be commended for the 

overall diversity of the tree population in terms 

of the number of species and distribution of trees 

among the species.

There is approximately one ROW tree for every 

nine residents, and these trees shade approximately 

0.74% of the city or 6.52% of the city’s streets and 

sidewalks. 

Boise divides its tree population into three mature 

size classes. Class I trees are small species, growing to 30 ft tall. Class II represents shade trees that 

mature to a maximum height of 40–60 ft while 

Class III shade trees grow to 60–90 ft.

The age structure of Boise’s ROW tree population 

appears fairly close to the desired “ideal” distribution in having a high proportion (48%) of young 

trees (0–6 inch diameter at breast height [DBH] or 

4.5 ft above the ground) and fewer mature and old 

trees. The largest size classes are represented almost entirely by silver maple, American sycamore, 

London planetree (Platanus acerifolia), and elm 

species (Ulmus spp.), many of which are nearing 

the end of their natural life spans. Loss of these 

trees before the large young tree population matures could represent a sizeable impact on the flow 

of benefits the city currently receives from street 

trees. Conversely, if the young trees survive and 

grow to full maturity, Boise can look forward to 

greater benefits in the future.

Resource Function and Value

The ROW trees of Boise provide great benefits to 

the citizens. Their ability to moderate climate—

thereby reducing energy use—is substantial. Electricity saved annually in Boise from both shading 

and climate effects of the ROW trees totals 3,021 

MWh ($184,117), and annual natural gas saved totals 129,230 therms ($147,639) for a total energy 

cost savings of $331,756 or $14 per tree. 

Citywide, annual carbon dioxide (CO2) sequestration and emission reductions due to energy savings 

by ROW trees are 1,122 lbs and 1,743 lbs, respectively. CO2

 released during decomposition and 

tree-care activities is 1,440 lbs. Net CO2 reduction 

is 907 tons, valued at $6,060 or $0.26 per tree. 

Net annual air pollutants removed, released, and 

avoided average 0.20 lbs per tree and are valued 

at $6,292 or $0.27 per tree. Ozone (O3) is the most 

significant pollutant intercepted by trees, with 

6,939 lbs per year removed from the air ($3,539), 

while nitrogen dioxide (NO2) is the most economically significant air pollutant whose production is 

avoided at the power plant, due to reduced energy 

needs (1,353 lbs) per year ($690).

Boise’s ROW trees intercept rain, reducing stormwater runoff by 19,246,286 gallons annually, with 

an estimated value of $96,238. Citywide, the average tree intercepts 827 gallons of stormwater each 

year, valued at $4 per tree.

The estimated total annual benefits associated with 

aesthetics, property value increases, and other 

less tangible improvements are approximately 

$561,917 or $24 per tree on average.

The grand total for all annual benefits – environmental and aesthetic – provided by ROW trees is 

$1,002,263, an average of $43 per street tree. The 

city’s 1,926 silver maples produce the highest total level of benefits at $173,990, annually ($90 per 

tree, 17.4% of total benefits). On a per tree basis, 

American sycamore (Platanus occidentalis, $87 

per tree) and English elm (Ulmus procera, $85 per 

tree) also produce significant benefits. Small-stature species, such as the crabapple ($17 per tree), 

the Callery pear (Pyrus calleryana, $19 per tree), 

and the hawthorn (Crataegus spp., $19 per tree) 

provide the lowest benefits. 

Boise spends approximately $770,784 in a typical 

year maintaining its public ROW trees ($33.13/

tree). The highest single cost is tree removal 

($141,417), followed by administration ($124,494). 
3

Silver maple, due to age and structural problems, 

accounts for significant proportion of maintenance 

costs associated with tree removal, storm cleanup, 

and property and infrastructure damage.

Subtracting Boise’s total expenditures on ROW 

trees from total costs shows that Boise’s municipal 

ROW tree population is a valuable asset, providing 

approximately $231,479 or $9.95 per tree ($1.11 

per capita) in net annual benefits to the community. 

Over the years, the city has invested millions in its 

urban forest. Citizens are now receiving a return on 

that investment—ROW trees are providing $1.30 

in benefits for every $1 spent on tree care. Boise’s 

benefit-cost ratio of 1.30 is similar to those reported 

for Berkeley, CA (1.37), Charleston, SC (1.34), and 

Albuquerque (1.31), but is below those reported 

for Fort Collins, CO (2.18), Cheyenne, WY (2.09), 

and Bismarck, ND (3.09). A variety of factors can 

contribute to the benefit-cost ratio being lower than 

other communities, but key is the fact that the majority of the trees are still immature and have not 

yet reached full potential for producing benefits.

Another way of describing the worth of trees is their 

replacement value, which assumes that the value of 

a tree is equal to the cost of replacing it in its current condition. Replacement value is a function of 

the number, stature, placement and condition of the 

cities’ trees and reflects their value over a lifetime. 

As a major component of Boise’s green infrastructure, the 23,262 street trees are estimated to have 

a replacement value of $88,266,102 or $3,794 per 

tree. 

Resource Management

Boise’s ROW trees are a dynamic resource. Managers of the urban forest and the community alike 

can take pride in knowing that these trees greatly 

improve the quality of life in the city. However, the 

trees are also a fragile resource needing constant 

care to maximize and sustain production of benefits into the future while also protecting the public from potential hazard. The challenge as the city 

continues to grow will be to sustain and expand 

the existing canopy cover to take advantage of the 

increased environmental and aesthetic benefits the 

trees can provide to the community. 

In 2006, Boise Mayor David Bieter signed the U.S. 

Mayors Climate Protection Agreement making 

Boise the 1st city in Idaho to endorse the agreement. Street ROW trees contribute more to reducing heat island effects, energy consumption, and 

ground-level ozone by shading the gray infrastructure than trees in backyards and parks. By acting 

now to implement the recommendations in this report, Boise will be better able to meet its 7% emission reduction target by 2012 and generally benefit 

from a more functional and sustainable urban forest in the future. 

Management recommendations focused on sustaining existing benefits and increasing future benefits 

include the following: 

1. To help meet Boise’s Climate Protection 

Agreement goals to reduce greenhouse gases 

and emissions:

• Develop programs and policies to encourage and significantly increase shade tree 

planting along streets, in parking lots, and 

near buildings in and adjacent to public 

rights-of-way.

• Increase ROW stocking and canopy cover, 

setting an initial goal of planting one ROW 

tree for every five residents. This represents an increase of over 18,000 ROW 

trees (41,600 total compared to 23,262 currently) for a 25% stocking level and 10.5% 

canopy cover over streets and sidewalks. 

• Increase stocking level with larger-growing (Class II and III) shade tree species 

where conditions are suitable to maximize 

benefits. Consider adopting front-yard 

easements for new tree planting and maintenance where parkway planting strips are 

absent.

• Plan for adequate care and pruning to reduce tree mortality and insure survival. 
4

2. Develop a strong young-tree care program that 

emphasizes reducing mortality. Irrigation along 

with inspection and pruning at least twice during the initial five years after planting will provide a good foundation for the trees. Expansion 

of the Tree Steward program is a cost-effective 

approach to accomplishing this goal.

3. Track the success of the newly planted trees to 

determine those most adaptable to the difficult 

growing conditions in Boise. Continue planting 

a diverse mix of tree species to guard against 

catastrophic losses due to storms, pests or disease while concentrating the species choice on 

those that have proven most successful.

4. Sustain current benefits by investing in programmed maintenance of mature trees (e.g., 

silver maple, American basswood, elms, Norway maple, American sycamore) to prolong 

the functional life spans of these trees. This 

includes developing a planned replacement 

program designed to gradually and systematically replace senescent trees with trees that 

will grow to similar stature. 

5. Continue working with the Ada County Highway District to develop planting space and tree 

guidelines in new developments and when retrofitting existing streets. Adequate space for 

Class II and III trees should be a priority.

6. Implement Forestry Management Plan priority 

recommendations including the development 

and im-plementation of a public education and 

volunteer program to demonstrate the link between basic tree care – irrigation frequencies 

and appropriate maintenance levels – and the 

resultant benefits provided to the homeowner, 

neighborhood, and community. This should assist in increasing available planting space and 

improving tree longevity, functionality, and 

overall benefits. 

7. Develop, implement, and enforce a landscaping ordinance for Boise that creates specific 

public and private street and parking lot shade 

guidelines to promote tree canopy cover and 

associated benefits.

The challenge ahead is to better integrate Boise’s 

green infrastructure with its gray infrastructure. 

This can be achieved by including green space and 

trees in the planning phase of development projects, providing space for trees through adequate 

ROW design or property easements, planting that 

available space, and designing and maintaining 

plantings to maximize net benefits over the long 

term. 
5

Located along the Boise River where the desert 

plateau meets the Rocky Mountains, Boise is the 

capital and largest city in the State of Idaho. It is the 

hub of commerce, banking and government for the 

state. This “City of Trees” (Boise derives its name 

from the French ‘le bois’ or ‘the woods’) maintains 

its trees as an integral component of the urban infrastructure. Over the past century, Boise residents 

and the city have been planting trees on public and 

private properties. Long before Idaho received its 

statehood, early residents began planting trees to 

improve community appearance and provide cool 

shade in an otherwise harsh desert plateau environment. The city’s Community Forestry Unit of the 

Parks and Recreation Department actively manages more than 41,000 trees (Boise Parks & Recreation Dept. Community Forestry Unit 2003). It 

is estimated that there are over 200,000 trees on 

private properties within the city. The city believes 

that the public’s investment in stewardship of the 

urban forest produces benefits that far outweigh 

the costs to the community and that investing in 

Boise’s green infrastructure makes sense economically, environmentally, and socially.

Research indicates that healthy trees can mitgate 

impacts associated with urban environs: polluted 

stormwater runoff, poor air 

quality, high energy use for 

heating and cooling buildings, 

and heat islands. Healthy trees 

increase real estate values, 

provide neighborhood residents with a sense of place, 

and foster psychological, 

social, and physical health. 

Street and park trees are associated with other intangibles, too, such as increasing 

community attractiveness 

for tourism and business and 

providing wildlife habitat and 

corridors. The municipal forest makes Boise a more enjoyable place to visit, 

live, work, and play while mitigating the environmental impacts of the city (Figure 2).

In an era of decreasing public funds and rising 

costs, however, there is a need to scrutinize public expenditures that are often viewed as “nonessential,” such as planting and maintaining street 

trees. Some may question the need for the level 

of service presently provided. Hence, the primary 

question that this study asks is whether the accrued 

benefits from Boise’s ROW trees justify the annual 

expenditures?

In answering this question, information is provided 

to do the following:

• Assist decision-makers to assess and justify 

the degree of funding and type of management 

program appropriate for Boise’s urban forest.

• Provide critical baseline information for evaluating program cost-efficiency and alternative 

management structures.

• Highlight the relevance and relationship of 

Boise’s street trees to local quality of life issues such as environmental health, economic 

development, and psychological well-being.

Chapter One—Introduction

Figure 2—Stately trees shade a residential street in Boise
6

• Provide quantifiable data to assist in developing alternative funding sources through utility 

purveyors, air quality districts, federal or state 

agencies, legislative initiatives, or local assessment fees.

This report includes six chapters and three appendices: 

Chapter One—Introduction: Describes the purpose of the study.

Chapter Two—Boise’s Municipal Tree Resource: 

Describes the current structure of the street ROW 

tree resource.

Chapter Three—Costs of Managing Boise’s Municipal Trees: Details management expenditures 

for publicly managed right-of-way trees.

Chapter Four—Benefits of Boise’s Municipal 

Trees: Quantifies the estimated value of tangible 

benefits and calculates net benefits and a benefit–

cost ratio for street ROW trees.

Chapter Five—Management Implications: Evaluates relevancy of this analysis to current programs 

and describes management challenges for street 

ROW tree maintenance.

Chapter Six—Conclusions: Final word on the use 

of this analysis.

Appendix A—Tree Distribution: Lists species 

and tree numbers in the population of street ROW 

trees.

Appendix B—Replacement Values: Lists replacement values for the entire street ROW tree population.

Appendix C—Describes procedures and methodology for calculating structure, function, and value 

of the street ROW tree resource.

References—Lists publications cited in the study.
7

Chapter Two—Boise’s Municipal Tree Resource

Many of Boise’s citizens are passionate about their 

trees, believing that they add character, beauty, and 

serenity to the city (Figure 3). With increasing focus on species diversity, residents and city government have been planting trees on public and private 

property over the past century. Today thousands 

of trees grace Boise, earning the city recognition 

as a National Arbor Day Foundation “Tree City 

USA” for 29 years and receiving the Foundation’s 

Growth Award six times in the past 12 years. The 

Parks & Recreation Department Community Forestry Unit is an accredited urban forestry program 

with the Society of Municipal Arborists. Community Forestry is responsible for the preservation, 

protection and management of more than 41,000 

publicly owned trees in the City of Boise and sponsors a ReLeaf Boise tree-planting event, free tree 

steward classes and other educational opportunities 

for the community. Their published Tree Selection 

Guide for Street and Landscapes throughout Idaho

includes planting and maintenance information for 

residents as well as botanical drawings to assist 

residents and visitors with tree identification. In 

2006, the Community Forestry staff and members 

of the community reviewed and updated the Community Forestry Management Plan, assessing Boise’s urban forest and formalizing recommendations 

for the planting, care and maintenance of the tree 

resource. Additionally, in 2003, the Community 

Forestry Unit produced the Tree Statistics Report, 

evaluating the diversity, size, and age distribution 

of the urban forest. Cooperatively, citizens and the 

Community Forestry Unit are striving to monitor 

and improve all aspects of their urban forest, continuing to make Boise an enjoyable and healthy 

place to live.

Tree Numbers 

Begun in 1981, the Boise tree inventory is used as 

a management tool and continually updated. At the 

Figure 3—Boise’s substantial canopy cover provides citizens with many environmental and aesthetic benefits
8

time of this study the ROW trees along streets tallied 23,262 trees, distributed among eight management districts (Figure 4). 

The municipal ROW tree population is dominated 

by deciduous trees (91.75% of the total). There are 

no broadleaf evergreen street trees. Conifers account for the remaining 8.25% of the ROW tree 

population. Deciduous trees provide protection 

against the harsh summer sun, while still allowing 

the sun’s warming rays to reach buildings during 

winter months.

Street Tree Stocking Level

Although the inventory on which our study is 

based did not sample all potential public right-ofway planting sites in Boise, stocking level can be 

estimated based on total street miles and the city’s 

inventory of 23,262 street trees. Assuming there 

are 800 linear miles of streets in Boise (Jorgenson 2006), on average there are 29 street trees per 

mile. A fully stocked city would have one tree on 

each side of the street every 50 feet or 211 trees per 

mile. By this measure, Boise’s street tree stocking 

level is 14%, and there is room, theoretically, for as 

many as another 145,000 trees. The actual number 

of street tree plantings sites may be significantly 

less due to inadequate planting spaces, presence of 

privately owned trees, and utility conflicts. Boise’s 

current stocking level compares favorably with 

Cheyenne, WY (12%), Glendale, AZ (9%), and 

Charleston, SC (8%), but is significantly less than 

Bismarck, ND (37%), and the mean stocking level 

for 22 U.S. cities (38.4%) (McPherson et al. 2005; 

McPherson and Rowntree 1989). 

One significant challenge to increasing the street 

tree stocking levels in Boise is the lack of public 

right of way available for tree planting in areas developed after 1970. Historically, Boise’s street development always included parkway planting strips 

on public right of way between curbs and sidewalks. 

About midway through the 1900s, that changed as 

land prices increased and public coffers for purchasing street rights of way dwindled. Streets and 

sidewalks also became wider, leaving little space 

for landscaping of any kind, especially trees. In the 

1970s, public street trees all but disappeared from 

residential and commercial 

street planning. Those neighborhoods that chose to incorporate parkway strips were left 

with space enough for 3- to 5-

ft wide planters – inadequate 

for growth of Class II and III 

shade trees without disrupting 

sidewalks and curbs caused by 

roots and trunks. The result 

was several decades when Boise’s public tree canopy was not 

increased. Public tree planting 

will likely never be possible 

in these areas unless the city 

works with the community to 

develop policies establishing 

tree easements on properties 

adjacent to these rights of way. 

Of course, the private citizens 

in these areas may plant their 

Figure 4—Urban forest management districts in Boise. In District 8, public trees 

are limited to parks
9

trees adjacent to streets, but consistent planting 

of shade trees is not likely in these areas without 

established programs to encourage neighborhood 

planting.

Recently, citizens, planners and developers have 

started to swing the pendulum back toward reintroducing the public right of way tree parkways, 

at least in residential neighborhoods. The Ada 

County Highway District has recently approved a 

policy that will require minimum 8’ parkway strips 

in new developments in Ada County that choose 

to incorporate them into their street design. This 

will provide new potential for Boise and all Ada 

County communities to increase the street tree 

stocking levels. Wider parkway strips should allow 

for planting and growth of large shade trees, while 

decreasing damage to sidewalks and curbs as trees 

approach maturity.

Municipal ROW Trees Per Capita

Calculations of street trees per capita are important in determining how well-forested a city is. Assuming a human population of 208,000 (Jorgenson 

2006) and a street ROW tree population of 23,262, 

Boise’s number of ROW trees per capita is 0.11 

– approximately one tree for every nine people – 

significantly below the mean ratio of 0.37 reported 

for 22 U.S. cities (McPherson and Rowntree 1989). 

More recent research shows Boise’s ratio similar to 

Fort Collins, CO (0.12 or one tree per eight residents) but significantly lower than Cheyenne, WY 

(one tree per six residents) and Bismarck, ND (one 

tree per three residents) (McPherson et al. 2003; 

Peper et al. 2004a, b).

Species Richness, 

Composition and Diversity

The ROW tree population in Boise includes a mix 

of 179 different species—more than three times 

the mean of 53 species reported by McPherson 

and Rowntree (1989) in their nationwide survey 

of street tree populations in 22 U.S. cities. This is 

especially impressive considering the challenging 

growing conditions in a high desert plateau community. 

The predominant municipal ROW tree species are 

Norway maple (Acer platanoides, 10.4%), silver 

maple (Acer saccharinum, 8.3%), honeylocust 

(Gleditsia triacanthos, 6.9%), green ash (Fraxinus 

pennsylvanica, 4.5%), and crabapple (Malus spp., 

4.1%) (Table 1; see also Appendix A). 

The Community Forestry Unit, focused on species 

diversification, is working to conform to the general idea that no single species should represent more 

than 10% of the population and no genus more than 

20% (Clark et al. 1997). Norway maple barely exceeds the 10% species level, and only one genus, 

maple (Acer spp.), surpasses the 20% threshold at 

23.6%. In 2000, maples constituted 29% of the forest, but when maples die or require removal, the 

Community Forestry staff encourages replacement 

with nonmaple species, thereby reducing the predominance of this genus. The Community Forestry 

Unit is clearly aware of the impact that drought, 

disease, pests, or other stressors can have on an urban forest dominated by one species or genus. Providing a wide variety of species will reduce the loss 

of canopy due to such catastrophic events. 

Although ROW tree species diversity at the city 

level is good, at the district level, there are some areas for concern (Table 2; see Figure 4 for districts). 

In District 4, for example, 18.8% of the street trees 

are Norway maple. In District 6, silver maples represent 13.6% of the population and Scotch pine 

(Pinus sylvestris) 13.7% in District 7. 

Species Importance

Importance values (IV) are particularly meaningful 

to managers because they indicate a community’s 

reliance on the functional capacity of particular 

species. For this study, IV takes into account not 

only total tree numbers, but canopy cover and leaf 

area, providing a useful comparison with the total 

population distribution. 

IV, a mean of three relative values, can in theory 
10

DBH class (in)

Species 0–3 3–6 6–12 12–18 18–24 24–30 30–36 36–42 >42 Total % of total

Broadleaf deciduous large (BDL)

Silver maple 52 46 98 173 347 577 381 160 92 1,926 8.3

Honeylocust 484 324 535 218 23 18 3 2 - 1,607 6.9

Green ash 307 351 202 92 46 32 12 3 1 1,046 4.5

White ash 499 267 86 17 - 2 4 - - 875 3.8

American sycamore 61 23 34 61 139 195 145 44 11 713 3.1

American basswood 209 122 141 96 67 26 9 1 1 672 2.9

Ash 58 134 128 131 98 55 15 1 1 621 2.7

Sweetgum 282 120 128 72 11 3 - - - 616 2.6

Black locust 36 74 113 108 116 110 29 13 2 601 2.6

Elm 53 61 64 18 34 76 108 68 18 500 2.1

Northern red oak 187 66 51 48 61 38 13 1 - 465 2.0

Black walnut 13 30 106 134 84 34 5 4 2 412 1.8

Sugar maple 248 38 26 22 10 7 1 - - 352 1.5

Northern catalpa 13 40 47 65 66 73 33 7 1 345 1.5

London planetree 140 55 85 27 13 15 4 2 2 343 1.5

Siberian elm 19 21 56 49 58 66 24 12 2 307 1.3

English elm - 1 4 1 28 67 110 69 23 303 1.3

Cottonwood 36 78 35 18 10 24 24 18 29 272 1.2

BDL other 294 150 144 154 142 166 87 38 26 1,201 5.2

Total 2,991 2,001 2,083 1,504 1,353 1,584 1,007 443 211 13,177 56.6

Broadleaf deciduous medium (BDM)

Norway maple 669 505 607 421 162 57 7 2 - 2,430 10.4

Maple 239 135 203 130 51 9 1 2 - 770 3.3

Callery pear 377 256 106 5 1 - - - - 745 3.2

Littleleaf linden 208 62 17 4 4 - - - - 295 1.3

BDM other 669 317 216 129 64 28 18 6 5 1,452 6.2

Total 2,162 1,275 1,149 689 282 94 26 10 5 5,692 24.5

Broadleaf deciduous small (BDS)

Crabapple 424 329 155 34 2 - - - - 944 4.1

Hawthorn 117 75 128 63 7 - - - - 390 1.7

Plum 51 141 88 7 3 - - - - 290 1.2

BDS other 419 172 168 82 7 2 - - - 850 3.7

Total 1,011 717 539 186 19 2 - - - 2,474 10.6

Conifer evergreen large (CEL)

Blue spruce 57 168 86 99 29 10 1 - 1 451 1.9

CEL other 41 87 121 91 47 22 8 - - 417 1.8

Total 98 255 207 190 76 32 9 - 1 868 3.7

Conifer evergreen medium (CEM)

Scotch pine 174 212 67 8 2 4 - - - 467 2.0

Austrian pine 28 135 139 47 7 5 - - - 361 1.6

CEM other 66 65 46 18 6 2 - - - 203 0.9

Total 268 412 252 73 15 11 - - - 1,031 4.4

Conifer evergreen small (CES)

CES other 4 2 10 3 1 - - - - 20 0.1

Citywide total 6,534 4,662 4,240 2,645 1,746 1,723 1,042 453 217 23,262 100.0

Table 1—Most abundant ROW tree species in order of predominance by DBH class and tree type
11

range between 0 and 100, where an IV of 100 implies total reliance on one species and an IV of 0 

suggests no reliance. Urban tree populations with 

one dominant species (IV>25%) may have low 

maintenance costs due to the efficiency of repetitive 

work, but may still incur large costs if decline, disease, or senescence of the dominant species results 

in large numbers of removals and replacements. 

When IVs are more evenly dispersed among five 

to ten leading species, the risks of a catastrophic 

loss of a single dominant species are reduced. Of 

course, suitability of the dominant species is an 

important consideration. Planting short-lived or 

poorly adapted trees can result in short rotations 

and increased long-term management costs. 

The 28 most abundant ROW tree species listed in 

Table 3 constitute 82% of the total population, 86% 

of the total leaf area, and 86% of total canopy cover, 

for an IV of 85. As Table 3 illustrates, Boise is relying most on the functional capacity of silver maple. 

Though the species accounts for just over 8% of all 

public ROW trees, because of the trees’ large size, 

the amount of leaf area and canopy cover provided 

is great, increasing their importance value to 18.62 

when all components are considered. This makes 

them 2.3 times more significant than Norway maple and 2.5 times more significant than American 

sycamore (Platanus occidentalis), the next closest 

species. 

The main reason why silver maple are highest in 

importance value is that the majority of the trees are 

old and fully mature; therefore, they have reached 

their full structural and functional capacity. Maple, 

as a genus, contributes 32% of the leaf area and 

34% of Boise’s canopy cover. Other large trees 

– honeylocust, green ash, and white ash (Fraxinus 

americana) – appear to have significantly lower 

importance values; however, more than half of 

their populations are young trees (<6 inches DBH) 

and will continue to grow in importance as they 

age. For example, white ash’s current importance 

value is only 1.87, but nearly 90% of its population 

is less than 6 inches DBH. If white ash increase in 

size and number they are likely to become as important as the older species. 

Age Structure

The distribution of ages within a tree population 

influences present and future costs as well as the 

flow of benefits. An uneven-aged population allows managers to allocate annual maintenance 

costs uniformly over many years and assures continuity in overall tree-canopy cover. A desirable distribution has a high proportion of new transplants 

to offset establishment-related mortality, while the 

percentage of older trees declines with age (Richards 1982/83). 

Citywide, the overall age structure, represented 

here in terms of DBH, for ROW trees in Boise appears fairly similar to the ideal with the exception 

of trees in the 6–12 inch DBH class where the proportion is 7% below the ideal (Figure 5) . Closer 

examination, however, shows that the results differ greatly by species. Norway maple comes closest to ideal distributions across DBH classes. Four 

District 1st (%) 2nd (%) 3rd (%) 4th (%) 5th (%) # of trees

Not assigned Honeylocust (10.2) Norway maple (9.7) Green ash (6.6) Crabapple (5.6) Silver maple (4.9) 1,054

1 Norway maple (10.7) Silver maple (9.3) Honeylocust (6.8) Maple (6.3) Green ash (5.2) 3,529

2 Silver maple (11) Norway maple (10.9) Honeylocust (4.3) Maple (3.9) Elm (3.8) 4,956

3 Green ash (7.1) Silver maple (6.7) Norway maple (6.4) Ash (5.9) White ash (4.3) 1,293

4 Norway maple (18.8) White ash (6.3) White ash (6.3) Green ash (6.3) Silver maple (5.2) 4,538

5 Norway maple (9.3) Honeylocust (8.4) Silver maple (7.4) Maple (5) White ash (4.8) 3,132

6 Silver maple (13.6) Black locust (9.7) Siberian elm (6) Green ash (5.2) Crabapple (4.9) 1,839

7 Scotch pine (13.7) Crabapple (7.4) Honeylocust (6.8) Silver maple (6.6) Austrian pine (6.3) 2,921

Citywide total Norway maple (10.4) Silver maple (8.3) Honeylocust (6.9) Green ash (4.5) Crabapple (4.1) 23,262

Table 2—Most abundant right-of-way tree species by district with percentage of totals in parenthesis
12

species are heavily represented in the smaller size 

classes, reflecting the city’s recent efforts to plant 

new Class I, II, and III species including green ash, 

white ash, Callery pear (Pyrus calleryana), and 

crabapple. As relatively new introductions to the 

planting palette, these species have 63-87% of their 

populations in the 0–6 inch DBH class and little or 

no representation as of yet in mature size classes. 

For mature size classes, Boise ROW trees exceed 

ideal numbers only because of two species – American sycamore and silver maple – with heavy representation in DBH classes greater than 24 inches. 

The middle size classes (18–30 inch DBH) are less 

well-represented, probably a result of fewer trees 

being planted during that time period, coupled with 

high mortality rates. Records maintained by Community Forestry indicate the tree mortality of new 

plantings in Boise at around 2% per year for the 

first five years and 0.8% per year thereafter, suggesting that 30% of all trees planted do not live 

beyond 30 years (Jorgenson 2006). Many trees 

simply do not live long enough to grow large. Poor 

maintenance through inadequate watering or overwatering by adjacent property owners is the main 

contributing factor to early mortality. Where growing conditions are good, removed trees are replaced 

with new trees.

It is interesting to note that Boise has a relatively 

high percentage of very old street ROW trees (2.9% 

Table 3—Importance values (IV) indicate which species dominate the population based on numbers and size

Species

% of total 

trees

Leaf area (ft2) % of total leaf 

area

Canopy cover 

(ft2)

% of total 

canopy cover

Importance 

value

Silver maple 8.3 13,300,478 23.8 3,602,907 23.8 18.62

Norway maple 10.4 3,427,328 6.1 1,110,360 7.3 7.97

American sycamore 3.1 5,668,569 10.1 1,438,316 9.5 7.57

Honeylocust 6.9 2,382,679 4.3 903,552 6.0 5.71

Elm 2.1 3,221,936 5.8 579,637 3.8 3.91

English elm 1.3 2,947,684 5.3 530,133 3.5 3.36

Green ash 4.5 1,438,364 2.6 437,499 2.9 3.32

Ash 2.7 1,781,251 3.2 539,219 3.6 3.14

Maple 3.3 1,201,394 2.1 369,241 2.4 2.63

Black walnut 1.8 1,430,379 2.6 463,421 3.1 2.46

Northern red oak 2.0 1,410,519 2.5 395,024 2.6 2.38

Black locust 2.6 1,224,755 2.2 356,163 2.4 2.38

American basswood 2.9 991,999 1.8 286,878 1.9 2.19

Siberian elm 1.3 1,634,050 2.9 295,987 2.0 2.07

Crabapple 4.1 335,031 0.6 147,613 1.0 1.88

White ash 3.8 468,885 0.8 150,796 1.0 1.87

Cottonwood 1.2 1,122,196 2.0 308,663 2.0 1.74

Northern catalpa 1.5 880,510 1.6 229,941 1.5 1.53

Sweetgum 2.6 544,285 1.0 137,392 0.9 1.51

Callery pear 3.2 290,363 0.5 82,894 0.5 1.42

London planetree 1.5 713,931 1.3 205,402 1.4 1.37

Blue spruce 1.9 509,810 0.9 106,098 0.7 1.18

Hawthorn 1.7 256,311 0.5 114,678 0.8 0.96

Austrian pine 1.6 328,771 0.6 82,594 0.5 0.90

Sugar maple 1.5 350,606 0.6 80,020 0.5 0.89

Scotch pine 2.0 165,596 0.3 44,537 0.3 0.87

Plum 1.2 148,401 0.3 65,711 0.4 0.65

Littleleaf linden 1.3 78,716 0.1 23,817 0.2 0.52
13

in DBH classes greater than 36 inches). These large 

trees are almost entirely elms (data not shown), silver maple and American sycamore. Most of the 

other large trees are cottonwoods or were heavily 

planted in the past. 

Figure 6 shows age distribution of ROW trees by 

district. The desired pattern (a high proportion of 

new trees and numbers that decline with age) holds 

true at the district level. Some small differences 

can be noted: Districts 3 and 5 have a markedly 

high proportion of trees in the youngest class while 

Districts 1, 2, and 6 surpass the ideal representation for the larger DBH classes. No district was assigned to 1,054 trees in the inventory and that is 

also shown in Figure 6.

Again, it is important to note that these findings are 

proportionate to the number of ROW trees present 

in each district, not total number of ROW trees. 

Districts undergoing expansion and development 

have significantly fewer trees than older, established districts due to exclusion of tree space from 

planned rights-of-way (Figure 4).

Tree Condition

Tree condition indicates both how well trees are 

managed and how well they perform given site-specific conditions. About 57% of ROW trees in Boise 

are in good or better condition, with over 16% in 

poor or worse condition (Figure 7). The relative 

performance index (RPI) of each species provides 

an indication of its suitability to local growing conditions, as well as its performance. A species whose 

trees are in average condition compared to all other 

species in the city has an RPI of 1.0. Species that 

perform above the average have an RPI greater 

than 1.0, and those species with below average 

performance have RPIs below 1.0. Part of Boise’s 

higher percentage of trees in good condition may 

be due to the fact that 66% of the ROW population 

is relatively young, under 12 inches DBH.

Condition varies greatly from species to species, 

however (Table 4). Looking at species with 100 or 

more trees planted, Boise species with the lowest 

RPI (highest percentage in poor or worse condition) 

0-6

6-12

12-18

18-24

24-30

30-36

>36Ideal

Citywide total

Norway maple

American sycamore

White ash

Callery pear

American basswood

Green ash

Crabapple

Maple

Honeylocust

Silver maple

0

10

20

30

40

50

60

70

80

90

100

DBH Class

(%)

Figure 5—Relative age distribution for Boise’s 10 most 

abundant street tree species citywide shown with an 

ideal distribution

Figure 6—Relative age distribution of all street trees by 

management district 0-6 6-12 12-18 18-24 24-30 30-36

36-42

>42No District

District 1

District 2

District 3

District 4

District 5

District 6

District 7

Citywide total

0

10

20

30

40

50

60

70

(%)

DBH Class

Tree Condition

Critical

0.5% Poor

15.2%

Fair

Good 26.7%

47.3%

Very Good

0.7%

Excellent

9.0%

Dead or Dying

0.6%

Figure 7­—Boise’s street trees are assigned one of seven condition ratings. The majority of the city’s trees are in 

fair or better condition
14

Species Dead Critical Poor Fair Good Very good Excellent RPI # of trees % of pop’n

Northern hackberry 0.9 0.0 1.9 8.3 59.3 0.0 29.6 1.24 108 0.5

White ash 0.3 0.5 2.7 17.1 52.5 2.2 24.7 1.19 875 3.8

London planetree 0.0 0.0 2.6 13.1 65.3 0.9 18.1 1.17 343 1.5

Callery pear 0.5 0.3 3.2 13.5 62.4 1.6 18.4 1.16 743 3.2

White oak 0.0 0.0 1.1 18.4 62.1 1.1 17.2 1.16 174 0.7

Northern red oak 0.6 0.0 3.2 9.5 71.0 1.1 14.6 1.15 465 2.0

American basswood 0.4 0.1 7.0 22.3 45.1 0.0 25.0 1.15 672 2.9

Crabapple 0.2 0.3 5.4 23.5 50.8 1.0 18.8 1.13 942 4.1

Green ash 0.6 0.5 4.4 21.8 54.7 1.0 17.0 1.12 1,045 4.5

Maple 1.4 0.4 7.6 21.5 48.6 0.0 20.6 1.11 768 3.3

Sugar maple 0.3 0.9 5.1 9.1 71.9 6.3 6.5 1.11 352 1.5

Honeylocust 0.3 0.2 4.5 20.5 60.8 1.3 12.4 1.10 1,607 6.9

Silver linden 0.0 0.0 2.2 8.1 86.8 0.0 2.9 1.10 136 0.6

Sweetgum 0.7 0.3 3.4 18.6 66.1 0.2 10.7 1.10 614 2.6

Japanese pagoda tree 0.0 0.7 9.9 19.2 55.0 0.0 15.2 1.09 151 0.7

Ginkgo 0.0 0.7 3.4 12.8 77.9 0.0 5.4 1.09 149 0.6

Austrian pine 0.0 0.0 1.9 12.7 83.9 0.0 1.4 1.08 361 1.6

Bur oak 0.8 0.0 2.5 12.6 79.8 1.7 2.5 1.08 119 0.5

Scotch pine 0.0 0.0 0.2 17.4 82.4 0.0 0.0 1.07 466 2.0

Plum 1.4 0.7 14.8 26.2 34.8 0.0 22.1 1.07 290 1.2

Blue spruce 0.4 0.0 1.8 21.1 73.8 0.0 2.9 1.06 451 1.9

Littleleaf linden 0.0 0.3 3.1 25.4 62.7 5.1 3.4 1.06 295 1.3

Hawthorn 0.5 0.8 5.4 39.7 40.3 0.0 13.3 1.04 390 1.7

Pine 0.9 0.0 3.6 17.9 77.7 0.0 0.0 1.04 112 0.5

Norway maple 0.6 0.7 9.4 25.0 56.9 0.5 7.0 1.02 2,428 10.5

Red maple 0.0 1.8 5.3 20.4 71.7 0.0 0.9 1.02 113 0.5

Basswood 0.7 1.4 9.5 19.0 63.3 2.0 4.1 1.02 147 0.6

Ash 0.3 0.0 19.3 35.7 41.2 0.0 3.4 0.93 621 2.7

American sycamore 0.3 0.4 21.2 45.4 23.3 0.4 8.9 0.92 711 3.1

Northern catalpa 0.0 0.3 15.1 47.8 34.8 0.6 1.4 0.91 345 1.5

Tree of heaven 0.0 0.0 13.5 64.3 22.2 0.0 0.0 0.86 126 0.5

American elm 0.0 0.6 27.5 60.5 9.6 0.0 1.8 0.80 167 0.7

Black walnut 2.0 1.5 28.1 46.9 21.0 0.0 0.5 0.80 409 1.8

Cottonwood 4.1 0.0 35.1 32.1 28.4 0.0 0.4 0.79 271 1.2

Elm 1.4 0.4 37.6 37.0 23.0 0.0 0.6 0.79 500 2.2

Silver maple 0.8 0.9 42.6 42.6 12.5 0.1 0.5 0.75 1,919 8.3

Siberian elm 0.0 0.0 55.4 34.5 6.5 0.0 3.6 0.73 307 1.3

English elm 0.0 1.0 49.5 38.9 10.6 0.0 0.0 0.72 303 1.3

Black locust 2.3 1.7 71.4 19.6 3.5 0.0 1.5 0.63 601 2.6

Citywide total 0.64 0.49 15.20 26.72 47.32 0.65 8.98 1.00 23,220 100.0

Table 4—Relative performance index (RPI) for Boise’s predominant right-of-way species
15

are black locust (Robinia pseudoacacia, 75%), Siberian elm (Ulmus pumila, 55%), and English elm 

(Ulmus procera, 50%). Species with the largest percentage of trees in good or better condition include 

northern hackberry (Celtis occidentalis, 90%), 

London planetree (Platanus acerifolia, 85%), and 

Callery pear (82%). Note that these values reflect 

condition as reported in the 2005 inventory and 

may not reflect current conditions for all species.

Care should be taken when analyzing tree condition to ensure that relevant factors such as age 

are taken into consideration. For example, 80% or 

more of Callery pear, white ash, and crabapple are 

young trees under 6 inches DBH. It is important to 

compare relative age (Figure 5) with RPI (Table 4) 

to determine whether various species have actually 

stood the test of time. Conclusions about their suitability to the region as ROW trees should be postponed until the trees have matured. 

Tree Canopy

Canopy cover, or more precisely, the amount and 

distribution of leaf surface area, is the driving 

force behind the urban forest’s ability to produce 

benefits for the community. As canopy cover increases, so do the benefits afforded by leaf area. 

It is important to remember that street and park 

trees throughout the United States—and those of 

Boise—likely represent less than 20% of the entire urban forest (Moll and Kollin 1993) within a 

city. Overall tree cover for the City of Boise (public 

and private trees) was reported as 28.6% (Dwyer et 

al. 2000). Public ROW trees canopy cover is only 

0.74%, given a city land area of 46,720 acres (73 

mi2) and our estimated ROW tree canopy of 347.6 

acres. The ROW trees cover 6.5% of city streets 

and sidewalks. Shading of streets and sidewalks 

decreases urban heat island effects as well as asphalt maintenance costs. Boise’s street and sidewalk canopy coverage exceeds that of Glendale, 

AZ (1.8%), Charleston, SC (2.5%), and Cheyenne, 

WY (5.2%), but is significantly less than Fort Collins, CO (11.8%), Bismarck, ND (23.8%), and Minneapolis, MN (35.2%). Although canopy coverage 

will increase in certain portions of the city as all the 

young trees planted in recent years mature, overall 

canopy cover may not increase unless space is created for street ROW trees in new developments and 

existing developments currently lacking tree ROW 

space or easements.

Replacement Value

Replacement value is a way of describing the value of trees at a given time, reflecting their current 

number, stature, placement, and condition. There 

are several methods that arborists employ to develop a fair and reasonable perception of a tree’s value 

(CTLA 1992, Watson 2002). The cost approach is 

widely used today and assumes that value equals 

the cost of production, or in other words, the cost of 

replacing a tree in its current state (Cullen 2002). 

Replacing Boise’s 23,262 municipal ROW trees 

with trees of similar size, species, and condition if, 

for example, all were destroyed by a catastrophic 

storm, would cost approximately $88 million (Table 5; see also Appendix B). Boise’s ROW trees are 

a valuable legacy, and as a central component of 

the city’s green infrastructure can be considered a 

public asset with a value of $88,266,102, with most 

of this value in the older and larger trees. The average replacement value per tree is $3,794. 

Replacement value should be distinguished from 

the value of annual benefits produced by the ROW 

trees. The latter will be described in Chapter 4 as 

a “snapshot” of benefits during one year, while the 

former accounts for the historical investment in 

trees over their lifetimes. Hence, the replacement 

value of Boise’s municipal ROW tree population 

is many times greater than the value of annual benefits it produces.
16

Table 5—Replacement values, summed by DBH class, for the 40 most valuable species of street trees in 

Boise. See Appendix B for complete listing

Species 0–6 6–12 12–18 18–24 24–30 30–36 36–42 >42 Total

% of 

total

Silver maple 28,505 137,676 611,029 2,392,822 6,662,492 6,348,965 3,463,423 2,357,652 22,002,562 24.9

American sycamore 19,963 46,893 226,683 990,527 2,354,429 2,592,123 1,090,125 280,837 7,601,581 8.6

Norway maple 338,113 1,010,245 1,950,829 1,418,823 834,526 146,852 59,605 - 5,758,993 6.5

Elm 34,268 82,497 55,920 213,017 765,365 1,636,198 1,457,405 402,179 4,646,849 5.3

English elm 506 4,970 3,978 180,390 670,634 1,689,091 1,425,427 480,592 4,455,587 5.0

Northern catalpa 21,955 78,967 315,748 612,742 1,094,941 756,575 199,965 30,086 3,110,980 3.5

Black locust 36,068 133,021 334,423 666,072 1,051,526 406,209 231,586 42,580 2,901,485 3.3

Honeylocust 229,053 946,600 1,047,467 226,481 294,462 70,826 69,689 - 2,884,578 3.3

Ash 69,974 198,456 554,975 840,089 730,531 295,597 20,940 23,402 2,733,966 3.1

American elm - - 31,543 114,325 689,649 902,183 650,683 230,872 2,619,255 3.0

American basswood 86,210 245,752 485,897 662,020 428,405 199,650 34,845 32,075 2,174,852 2.5

Green ash 211,228 317,559 397,233 406,472 441,062 268,862 75,423 24,084 2,141,923 2.4

Black walnut 16,344 152,455 523,405 608,443 416,337 93,797 97,084 61,477 1,969,343 2.2

Northern red oak 55,393 94,037 242,873 598,861 596,881 301,450 26,120 - 1,915,614 2.2

Siberian elm 11,563 57,590 132,469 294,997 559,408 311,233 212,475 37,608 1,617,342 1.8

Maple 94,843 304,255 540,269 433,814 124,116 22,289 44,050 - 1,563,636 1.8

Cottonwood 39,135 40,285 53,705 52,477 205,188 244,860 285,801 515,505 1,436,956 1.6

White oak 8,990 44,747 109,480 315,767 512,863 215,904 55,670 - 1,263,421 1.4

Blue spruce 90,049 144,424 456,913 251,521 145,673 20,823 - 30,086 1,139,490 1.3

London planetree 44,579 156,598 131,454 129,354 264,027 104,359 67,476 68,126 965,972 1.1

Sweetgum 96,335 224,473 346,168 107,110 51,026 - - - 825,112 0.9

Crabapple 221,301 277,953 173,914 18,067 - - - - 691,235 0.8

Austrian pine 70,218 239,090 221,335 64,217 73,622 - - - 668,482 0.8

Hawthorn 52,756 221,326 297,376 58,840 - - - - 630,298 0.7

Tree of heaven 7,472 45,077 78,079 127,050 189,702 93,475 21,033 - 561,888 0.6

Norway spruce 5,940 22,208 75,524 151,364 159,454 143,415 - - 557,905 0.6

White ash 198,596 141,862 75,580 - 27,937 95,616 - - 539,591 0.6

Horsechestnut 2,677 17,704 124,300 101,215 64,854 90,572 65,373 - 466,694 0.5

Boxelder 4,859 11,939 49,574 85,621 161,182 68,273 17,321 35,933 434,701 0.5

Ponderosa pine 2,758 53,259 126,785 134,211 75,476 - - - 392,487 0.4

Eastern cottonwood - 1,084 31,119 20,672 85,450 37,253 - 213,421 388,999 0.4

Callery pear 174,986 179,743 22,730 7,461 - - - - 384,919 0.4

Sugar maple 52,813 39,112 95,440 76,622 87,374 18,904 - - 370,264 0.4

Scotch pine 127,773 111,696 39,664 18,788 61,861 - - - 359,781 0.4

Willow 3,080 8,817 6,445 40,886 113,827 81,632 48,196 53,844 356,727 0.4

Japanese pagoda tree 25,264 66,419 62,729 160,020 27,639 - - - 342,072 0.4

English oak 24,390 14,194 45,864 81,953 46,834 114,699 - - 327,933 0.4

Basswood 29,011 49,249 67,375 89,044 27,639 - - - 262,317 0.3

Bur oak 13,754 8,502 10,619 113,942 112,747 - - - 259,564 0.3

White fir 2,630 19,684 90,883 106,063 39,910 - - - 259,170 0.3

Other trees 586,407 940,371 1,148,534 584,988 324,036 376,202 161,841 159,188 4,281,578 5.0

Citywide total 3,139,759 6,890,789 11,396,330 13,557,148 20,573,085 17,747,887 9,881,556 5,079,547 88,266,102 100.0
17

Chapter Three—Costs of Managing Boise’s Municipal Trees

The benefits that Boise’s ROW trees provide come, 

of course, at a cost. This chapter presents a breakdown of annual expenditures for fiscal year 2005. 

Table 6 shows that total annual tree-related expenditures for Boise’s ROW trees are approximately 

$770,784 (Jorgenson 2007). This represents 0.35% 

of the City of Boise’s total operating budget ($222 

million) or $4 per person.

The city spends about $33 per street ROW tree on 

average during the year, 1.7 times the 1997 mean 

value of $19 per tree reported for 256 California 

cities after adjusting for inflation (Thompson and 

Ahern 2000). However, nonprogram expenditures 

(e.g., sidewalk repair, litter clean-up) were not 

included in the California survey. Boise’s annual 

expenditure is approximately equal to that of Fort 

Collins, CO ($32), far less than Santa Monica, CA 

($53), and Berkeley, CA ($65), and significantly 

more than Cheyenne WY ($19), Bismarck, ND 

($18) and Boulder, CO ($21) (McPherson et al. 

2005a, e). 

Forestry program expenditures fall into three general categories: tree planting and establishment, 

pruning and general tree care, and administration.

Tree Planting and Establishment

Quality nursery stock, careful planting, and followup care are critical to perpetuation of a healthy urban forest. By planting new trees that are relatively 

large, with DBH of 2 inches, the City of Boise is 

giving its urban forest a healthy start. In a typical 

year, the Community Forestry Unit plants about 

250 ROW trees (Figure 8). In an average year, 

street ROW tree planting activities including materials, labor, administration, and equipment costs, 

account for 9% of the program budget or approximately $68,000. 

Pruning, Removals, and General Tree Care

Pruning accounts for about 15% of the annual expenditures, at $115,000 ($4.96 per tree). New trees 

do not receive training pruning. Regardless of tree 

size, all Class I, II, and III trees are pruned on a 7-

year cycle. 

Although there is currently no schedule for pruning young trees, Boise Community Forestry has 

a group of trained volunteers – the Tree Stewards 

– who prune street and park trees every year for 

the city. Currently, a group of about 15 volunteers 

works about 30 weeks in a year pruning trees along 

Boise’s streets. City crews follow close behind 

chipping brush created by the pruning. Volunteers 

are currently pruning about 1,000 trees each year. 

The main focus of their work is structural pruning 

of juvenile trees and crown-raising over streets and 

sidewalks. Costs are low as this program only requires one staff person to direct the pruning on site 

Table 6—Boise’s annual municipal forestry-related expenditures

Street tree expenditures Total ($) $/Tree $/Capita % of total

Purchasing trees and planting 67,728 2.91 0.33 8.8

Pruning 115,356 4.96 0.55 15.0

Pest management 2,336 0.10 0.01 0.3

Removal 141,417 6.08 0.68 18.3

Administration 124,494 5.35 0.60 16.2

Inspection/service 70,831 3.04 0.34 9.2

Infrastructure repairs 87,780 3.77 0.42 11.4

Litter clean-up 45,270 1.95 0.22 5.9

Liability/claims 825 0.04 0.00 0.1

Other costs 114,747 4.93 0.55 14.9

Total expenditures 770,784 33.13 3.71 100.0
18

and two field staff and equipment for brush chipping. Without the Tree Stewards, Boise’s young 

tree pruning program would be nonexistent.

While the city does irrigate its park trees, there is 

no budget for street tree irrigation. 

Tree and stump removal accounts for about 18% 

of tree-related expenses ($141,000 or $6 per tree). 

About 290 street trees are removed each year, predominantly by contractors. Approximately 90% 

of the removed wood is chipped and dumped at 

city facilities for use as mulch in city parks and by 

citizens. Contractors also sell about 10% of the removed wood for use by citizens as firewood.

Administration

About $71,000 (9%) is spent annually on inspection and answering calls from the public on ROW 

trees. An additional $124,500 (16%) is spent on administrative expenses including administrative salary, meetings, continuing education, and in-house 

safety inspections. 

Other Tree-Related Expenditures

In a typical year, Boise spends about $45,000 (6%) 

on limb calls associated with storm and litter cleanup and $2,300 (0.3%) on pest management for 

ROW trees. Liability or claim costs attributed to 

trees were minimal in 2005 at $825 (0.1%). However, it was estimated that, annually, about $88,000 

(11%) is spent by on infrastructure repair related 

to ROW tree roots. The Ada County Highway 

District and adjacent residents pay the majority of 

these costs. Lastly, other costs – special projects, 

other equipment costs, payouts for employee leave, 

on-call costs, and miscellaneous tree care expenses 

– totaled $115,000 (15%).

Figure 8— A young sugar maple in full fall color
19

City trees work ceaselessly, providing ecosystem 

services that directly improve human health and 

quality of life. In this section, the benefits of Boise’s municipal ROW trees are described. It should 

be noted that this is not a full accounting because 

some benefits are intangible or difficult to quantify 

(e.g., impacts on psychological and physical health, 

crime, and violence). Also, our limited knowledge 

about the physical processes at work and their interactions makes these estimates imprecise (e.g., fate 

of air pollutants trapped by trees and then washed 

to the ground by rainfall). Tree growth and mortality rates are highly variable. 

A true and full accounting of 

benefits and costs must consider variability among sites 

throughout the city (e.g., tree 

species, growing conditions, 

maintenance practices), as 

well as variability in tree 

growth. 

For these reasons, the estimates given here provide 

first-order approximations of 

tree value. Our approach is 

a general accounting of the 

benefits produced by municipal ROW trees in Boise—an 

accounting with an accepted 

degree of uncertainty that 

can nonetheless provide a 

platform from which decisions can be made (Maco and 

McPherson 2003). Methods 

used to quantify and price 

these benefits are described in 

more detail in Appendix C.

Energy Savings

Trees modify climate and 

conserve energy in three principal ways (Figure 9):

• Shading reduces the amount of radiant energy 

absorbed and stored by built surfaces. 

• Transpiration converts moisture to water vapor 

and thus cools the air by using solar energy that 

would otherwise result in heating of the air.

• Wind-speed reduction reduces the movement 

of outside air into interior spaces and conductive heat loss where thermal conductivity is 

relatively high (e.g., glass windows) (Simpson 

1998). 

Chapter Four—Benefits of Boise’s Municipal Trees

Figure 9—Trees in Boise neighborhoods reduce energy use for cooling and 

cleaning the air
20

Trees and other vegetation within building sites 

may lower air temperatures 5°F (3°C) compared 

to outside the greenspace (Chandler 1965). At the 

larger scale of city-wide climate (6 miles or 10 km 

square), temperature differences of more than 9°F 

(5°C) have been observed between city centers and 

more vegetated suburban areas (Akbari et al. 1992). 

The relative importance of these effects depends on 

the size and configuration of trees and other landscape elements (McPherson 1993). Tree spacing, 

crown spread, and vertical distribution of leaf area 

influence the transport of warm air and pollutants 

along streets and out of urban canyons. 

Trees reduce air movement into buildings and conductive heat loss from buildings. Trees can reduce 

wind speed and resulting air infiltration by up to 

50%, translating into potential annual heating savings of 25% (Heisler 1986). Decreasing wind speed 

reduces heat transfer through conductive materials 

as well. Appendix C provides additional information on specific contributions that trees make toward energy savings.

Electricity and Natural Gas Results

Electricity and natural gas saved annually in Boise 

from both shading and climate effects equal 3,021 

Table 7—Net annual energy savings produced by Boise street trees

Species Electricity 

(MWh)

Electricity 

($)

Natural gas 

(therms)

Natural gas 

($)

Total ($) % of total 

trees

Avg. $/tree

Norway maple 241 14,714 11,736 13,407 28,121 10.4 11.57

Silver maple 675 41,136 25,784 29,456 70,593 8.3 36.65

Honeylocust 184 11,236 6,167 7,046 18,282 6.9 11.38

Green ash 91 5,572 4,572 5,224 10,796 4.5 10.32

Crabapple 31 1,907 2,204 2,518 4,425 4.1 4.69

White ash 32 1,980 1,868 2,134 4,114 3.8 4.70

Maple 81 4,946 4,292 4,903 9,849 3.3 12.79

Callery pear 19 1,142 1,014 1,158 2,300 3.2 3.09

American sycamore 263 16,002 8,559 9,778 25,779 3.1 36.16

American basswood 62 3,774 2,883 3,294 7,067 2.9 10.52

Ash 110 6,702 5,103 5,830 12,532 2.7 20.18

Sweetgum 32 1,946 1,588 1,814 3,760 2.7 6.10

Black locust 78 4,749 4,630 5,290 10,039 2.6 16.70

Elm 113 6,864 4,377 5,000 11,864 2.2 23.73

Scotch pine 9 566 488 558 1,124 2.0 2.41

Northern red oak 76 4,620 2,989 3,415 8,035 2.0 17.28

Blue spruce 23 1,431 1,056 1,206 2,637 1.9 5.85

Black walnut 96 5,860 4,130 4,718 10,578 1.8 25.67

Hawthorn 24 1,490 1,510 1,725 3,214 1.7 8.24

Austrian pine 18 1,084 824 942 2,026 1.5 5.61

Sugar maple 17 1,030 841 961 1,990 1.5 5.65

Northern catalpa 49 2,958 2,526 2,886 5,844 1.5 16.94

London planetree 41 2,513 1,697 1,939 4,452 1.5 12.98

Siberian elm 60 3,680 2,479 2,832 6,512 1.3 21.21

English elm 101 6,169 3,817 4,361 10,530 1.3 34.75

Littleleaf linden 5 326 293 335 661 1.3 2.24

Plum 14 849 972 1,110 1,959 1.3 6.76

Cottonwood 58 3,550 2,249 2,570 6,120 1.2 22.50

Other street trees 416 25,324 18,582 21,229 46,553 17.8 11.24

Citywide total 3,021 184,117 129,230 147,639 331,756 100.0 14.26
21

MWh ($184,117) and 129,230 therms ($147,639), 

respectively, for a total retail savings of $331,756 

or a citywide average of $14.26 per tree (Table 7). 

Silver maple provides 21.3% of the energy savings although it accounts for only 8.3% of total tree 

numbers, as expected for a tree species with such a 

high importance value. Norway maple (8.5%) and 

American sycamore (7. 8%) make the next greatest 

contributions to overall energy savings. On a per 

tree basis, silver maples again are the greatest contributors, reducing energy needs by approximately 

$36.65 per tree annually. American sycamore and 

English elm provide the next greatest savings on a 

per tree basis ($36.16 and $34.75). 

It should be noted again that this analysis describes 

the ROW tree population as it exists at the time of 

the inventory. This explains why the energy benefits of silver maple on a per tree basis ($36.65) are 

so much greater than, for instance, other large species like white ash ($4.70) or green ash ($10.32). 

Over 80% of Boise’s silver maples are greater than 

18 inches DBH, while the ashes have mostly been 

planted in recent years. As these younger species 

age and their size increases, the benefits that they 

provide will increase as well.

Table 8—CO2 reductions, releases, and net benefits produced by street trees

Species Sequestered (lb)Decomp. 

release (lb)

Maint. release (lb)Avoided 

(lb)

Net total 

(lb)

Total 

($)

% of 

trees

% of 

total $

Avg. 

$/tree

Norway maple 320,152 -21,942 -2,640 139 295,710 988 10.4 16.3 0.41

Silver maple 20,374 -144,704 -6,446 389 -130,386 -435 8.3 -7.2 -0.23

Honeylocust 120,533 -8,169 -1,484 106 110,987 371 6.9 6.1 0.23

Green ash 60,318 -5,833 -991 53 53,546 179 4.5 3.0 0.17

Crabapple 44,776 -1,610 -528 18 42,656 142 4.1 2.3 0.15

White ash 26,592 -1,038 -411 19 25,161 84 3.8 1.4 0.10

Maple 47,298 -6,534 -802 47 40,009 134 3.3 2.2 0.17

Callery pear 32,507 -811 -145 11 31,562 105 3.2 1.7 0.14

American sycamore 173,282 -31,471 -2,157 151 139,805 467 3.1 7.7 0.65

American basswood 45,173 -5,919 -787 36 38,503 129 2.9 2.1 0.19

Ash 70,858 -8,511 -1,031 63 61,380 205 2.7 3.4 0.33

Sweetgum 20,100 -1,244 -456 18 18,419 62 2.7 1.0 0.10

Black locust 155,069 -16,756 -1,298 45 137,060 458 2.6 7.6 0.76

Elm 161,776 -24,141 -1,429 65 136,270 455 2.2 7.5 0.91

Scotch pine 8,737 -320 -271 5 8,151 27 2.0 0.4 0.06

Northern red oak 58,330 -8,038 -589 44 49,746 166 2.0 2.7 0.36

Blue spruce 16,905 -1,434 -527 14 14,958 50 1.9 0.8 0.11

Black walnut 80,647 -8,829 -808 55 71,067 237 1.8 3.9 0.58

Hawthorn 26,614 -2,111 -358 14 24,159 81 1.7 1.3 0.21

Austrian pine 14,917 -710 -375 10 13,842 46 1.5 0.8 0.13

Sugar maple 18,012 -1,556 -200 10 16,265 54 1.5 0.9 0.15

Northern catalpa 34,143 -5,043 -827 28 28,302 95 1.5 1.6 0.27

London planetree 22,809 -2,141 -339 24 20,353 68 1.5 1.1 0.20

Siberian elm 85,187 -8,531 -743 35 75,948 254 1.3 4.2 0.83

English elm 148,099 -24,419 -1,275 58 122,464 409 1.3 6.8 1.35

Littleleaf linden 8,032 -371 -115 3 7,549 25 1.3 0.4 0.09

Plum 19,712 -714 -217 8 18,788 63 1.3 1.0 0.22

Cottonwood 22,513 -7,885 -604 34 14,057 47 1.2 0.8 0.17

Other street trees 380,362 -47,561 -4,896 240 328,145 1,096 17.8 18.1 0.26

Citywide total 2,243,828 -398,346 -32,751 1,743 1,814,474 6,060 100.0 100.0 0.26
22

Atmospheric Carbon Dioxide Reduction

Urban forests can reduce atmospheric carbon dioxide in two ways: 

• Trees directly sequester CO2

 as woody and foliar biomass as they grow.

• Trees near buildings can reduce the demand for 

heating and air conditioning, thereby reducing 

emissions associated with electric power production and consumption of natural gas. 

At the same time, however, CO2 is released by vehicles, chainsaws, chippers, and other equipment 

during the process of planting and maintaining 

trees. Also, eventually all trees die and most of the 

CO2 that has accumulated in their woody biomass 

is released into the atmosphere as they decompose 

unless it is recycled. These factors must be taken 

into consideration when calculating the carbon dioxide benefits of trees.

Avoided and Sequestered Carbon Dioxide 

Citywide, Boise’s municipal ROW trees reduce atmospheric CO2

 by a net of 907 tons annually (Table 8). This benefit was valued at 

$6,060 or $0.26 per tree and is equivalent to storing enough carbon in 2005 (year of the inventory) to counteract carbon production for over 

2 million (2,043,710) driving miles (assuming average vehicle fuel economy is 23 miles per gallon). 

Reduced CO2 emissions from power plants due to 

cooling energy savings totaled 1,743 lbs, while 

CO2 sequestered by trees was 1,122 tons. CO2

released through decomposition and tree care activities totaled 1,440 lbs, or 0.08% of the net total 

benefit. Net sequestration was over 1,000 times the 

avoided emissions. This is largely due to the relatively clean resource mix for electrical generation 

in Boise; over 50% of energy is provided by natural gas, geothermal, biomass, and hydro/renewable 

sources (US EPA 2003). 

On a per tree basis, English elm ($1.35), elm species (Ulmus spp., $0.91) and Siberian elm ($0.83) 

provide the greatest CO2 benefits (Table 8). Because 

of its predominance, the Norway maple population 

provides the greatest total CO2 benefits, accounting 

for over 16% of citywide CO2 reduction.

Air Quality Improvement

Urban trees improve air quality in five main ways:

• Absorbing gaseous pollutants (ozone, nitrogen oxides) through leaf surfaces

• Intercepting particulate matter (e.g., dust, 

ash, dirt, pollen, smoke)

• Reducing emissions from power generation by reducing energy consumption

• Releasing oxygen through photosynthesis

• Transpiring water and shading surfaces, 

resulting in lower local air temperatures, 

thereby reducing ozone levels

In the absence of the cooling effects of trees, higher 

temperatures contribute to ozone formation. On the 

other hand, most trees emit biogenic volatile organic compounds (BVOCs) such as isoprenes and 

monoterpenes that can also contribute to ozone formation. The ozone-forming potential of different 

species varies considerably (Benjamin and Winer 

1998). The contribution of BVOC emissions from 

trees to ozone formation depends on complex geographic and atmospheric interactions that have not 

been studied in most cities.

Deposition and Interception

Each year 4,752 lbs ($6,292) of nitrogen dioxide 

(NO2), small particulate matter (PM10), ozone (O3), 

and sulfur dioxide (SO2) are intercepted or absorbed 

by ROW trees (pollution deposition and particulate 

interception) in Boise (Table 9). The trees are most 

effective at removing O3 and PM10, with an implied 

annual value of $6,168. Due to their substantial 

leaf area and predominance, silver maple contributes the most to pollutant uptake, removing 2,231 

lbs each year. 
23

Table 9—Pollutant deposition, avoided and BVOC emissions, and net air-quality benefits produced by predominant street tree species

Deposition Avoided BVOC emissions Net total

% of 

trees Avg. $/tree Species

O3

(lb)

NO2

(lb)

PM10

(lb)

Total 

($)

NO2

(lb)

PM10

(lb)

VOC 

(lb)

SO2

(lb)

Total 

($) (lb) ($) (lb) ($)

Norway maple 453 40 170 408 122 9 6 1 72 −173 −24 629 455 10.45 0.19Silver maple 1,491 142 599 1,383 271 19 14 2 158 −846 −118 1691 1423 8.28 0.74Honeylocust 374 36 150 347 65 5 3 1 38 −296 −41 338 344 6.91 0.21Green ash 301 33 130 290 48 3 2 0 28 0 0 517 318 4.50 0.30Crabapple 61 6 25 57 23 2 1 0 13 −1 −0 117 70 4.06 0.07White ash 104 11 45 100 19 1 1 0 11 0 0 182 111 3.76 0.13Maple 153 15 61 142 45 3 2 0 26 −76 −11 203 157 3.31 0.20Callery pear 34 3 14 32 11 1 1 0 6 0 0 63 38 3.20 0.05American sycamore 522 55 233 509 90 6 5 1 53 −2334 −327 −1422 235 3.07 0.33American basswood 197 22 85 190 30 2 2 0 18 −78 −11 260 197 2.89 0.29Ash 370 41 160 357 53 4 3 0 31 0 0 632 388 2.67 0.63Sweetgum 57 5 23 53 17 1 1 0 10 −471 −66 −367 −4 2.65 -0.01Black locust 245 27 106 236 48 3 3 0 28 −250 −35 181 229 2.58 0.38Elm 240 23 96 223 46 3 2 0 27 0 0 411 249 2.15 0.50Scotch pine 31 3 13 29 5 0 0 0 3 −172 −24 −119 8 2.01 0.02Northern red oak 150 14 60 139 31 2 2 0 18 −377 −53 −118 104 2.00 0.22Blue spruce 73 8 32 70 11 1 1 0 6 −338 −47 −213 29 1.94 0.07Black walnut 192 18 77 178 43 3 2 0 25 −68 −10 267 194 1.77 0.47Hawthorn 43 4 17 40 16 1 1 0 9 −1 0 82 49 1.68 0.13

Austrian pine 57 6 25 55 9 1 0 0 5 −341 −48 −244 12 1.55 0.03Sugar maple 33 3 12 29 9 1 0 0 5 −27 −4 31 31 1.51 0.09

Northern catalpa 87 8 35 81 26 2 1 0 15 0 0 160 96 1.48 0.28London planetree 78 7 31 72 18 1 1 0 10 −237 −33 −101 49 1.47 0.14

Siberian elm 122 12 49 114 26 2 1 0 15 0 0 213 129 1.32 0.42

English elm 219 21 88 204 40 3 2 0 23 0 0 374 227 1.30 0.75Littleleaf linden 16 2 7 16 3 0 0 0 2 −6 −1 22 17 1.27 0.06

Plum 27 3 11 25 10 1 1 0 6 0 0 52 31 1.25 0.11Cottonwood 212 23 92 204 24 2 1 0 14 0 0 354 218 1.17 0.80

Other street trees 998 100 411 938 194 14 10 1 113 −1171 −164 558 887 17.81 0.21

Citywide total 6,939 689 2,858 6,519 1,353 97 71 10 790 −7,265 −1,017 4,752 6,292 100 0.27
24

Avoided Pollutants 

Energy savings result in reduced air pollutant emissions of NO2

, PM10, volatile organic compounds 

(VOCs), and SO2 (Table 9). Together, 1,531 lbs 

of pollutants are avoided annually with an implied value of $790. In terms of amount and dollar, 

avoided emissions of NO2 are greatest (1,353 lb, 

$690). Silver maples have the greatest impact on 

reducing energy needs; by moderating the climate 

they account for 306 lbs of pollutants whose production is avoided in power plants each year.

BVOC Emissions

Biogenic volatile organic compound (BVOC) 

emissions from trees must be considered. At a total 

of 3.6 tons, these emissions offset about one-quarter of air quality improvements and are calculated 

as a cost to the city of $1,017. American sycamore 

and silver maple are the higher emitters of BVOCs 

among Boise’s predominant tree species with sycamore accounting for about 32% of the urban forest’s emissions and silver maple 12%. 

Table 10—Annual stormwater reduction benefits of Boise’s street trees by species

Species Rainfall interception (gal) Total ($) % of trees % of total $ Avg. $/tree

Silver maple 3,949,263 19,748 8.3 20.5 10.25

American sycamore 2,022,156 10,111 3.1 10.5 14.18

Norway maple 1,291,287 6,457 10.4 6.7 2.66

Honeylocust 1,104,942 5,525 6.9 5.7 3.44

Elm 1,025,054 5,126 2.2 5.3 10.25

English elm 937,647 4,689 1.3 4.9 15.47

Ash 590,787 2,954 2.7 3.1 4.76

Siberian elm 523,227 2,616 1.3 2.7 8.52

Black walnut 490,958 2,455 1.8 2.5 5.96

Blue spruce 488,669 2,444 1.9 2.5 5.42

Green ash 479,254 2,396 4.5 2.5 2.29

American basswood 410,149 2,051 2.9 2.1 3.05

Maple 404,768 2,024 3.3 2.1 2.63

Northern red oak 386,295 1,932 2.0 2.0 4.15

Cottonwood 338,295 1,692 1.2 1.8 6.22

Austrian pine 337,251 1,686 1.5 1.8 4.67

London planetree 288,615 1,443 1.5 1.5 4.21

Northern catalpa 274,489 1,373 1.5 1.4 3.98

Scotch pine 177,845 889 2.0 0.9 1.90

White ash 165,143 826 3.8 0.9 0.94

Sweetgum 140,220 701 2.7 0.7 1.14

Hawthorn 129,447 647 1.7 0.7 1.66

Black locust 117,980 590 2.6 0.6 0.98

Callery pear 116,255 581 3.2 0.6 0.78

Sugar maple 53,015 265 1.5 0.3 0.75

Crabapple 48,917 245 4.1 0.3 0.26

Littleleaf linden 34,031 170 1.3 0.2 0.58

Plum 21,774 109 1.3 0.1 0.38

Other street trees 2,898,557 14,494 17.8 15.1 3.50

Citywide total 19,246,286 96,238 100 100 4.14
25

Net Air Quality Improvement

Net air pollutants removed, released, and avoided 

are valued at $6,292 annually. The average benefit 

per ROW tree is $0.27 (0.2 lb). Trees vary dramatically in their ability to produce net air-quality benefits. Large-canopied trees with large leaf surface 

areas that are not high emitters generally produce 

the greatest benefits. Although silver maples are 

classified as moderate emitters for Boise, the large 

amount of leaf area associated with the numerous 

large, old silver maple population counteracts the 

overall effect. In this case, silver maple produces 

the greatest benefits ($1,423 total; $0.74 per tree) 

even as the second highest emitter among the predominant ROW species in Boise. 

Stormwater Runoff Reductions

According to Clean Water Act regulations, municipalities must obtain a permit for managing their 

stormwater discharges into water bodies. Each 

city must identify the Best Management Practices 

(BMPs) it will implement to reduce its pollutant 

discharge. Trees are minireservoirs, controlling 

runoff at the source. Healthy trees can reduce the 

amount of runoff and pollutant loading in receiving 

waters in three primary ways:

• Leaves and branch surfaces intercept and store 

rainfall, thereby reducing runoff volumes and 

delaying the onset of peak flows. 

• Root growth and decomposition increase the 

capacity and rate of soil infiltration by rainfall 

and reduce overland flow.

• Tree canopies reduce soil erosion and surface 

transport by diminishing the impact of raindrops on barren surfaces.

Boise’s municipal ROW trees intercept 19,246,286 

gal of stormwater annually, or 827 gal per tree on 

average (Table 10). The total value of this benefit 

to the city is $96,238 or $4.14 per tree. 

Certain species are much better at reducing stormwater runoff than others. Leaf type and area, 

branching pattern and bark, as well as tree size and 

shape all affect the amount of precipitation trees 

can intercept and hold to reduce runoff. Trees that 

perform well include English elm ($15.47 per tree), 

American sycamore ($14.18 per tree), and silver 

maple ($10.25 per tree). Interception by silver maple alone accounts for over 20% of the total dollar benefit from ROW trees. Comparatively poor 

performers are species with relatively small leaf 

and stem surface areas, such as crabapple, plum 

(Prunus spp.), littleleaf linden (Tilia cordata), and 

white ash. Small species like the plum and crabapple simply do not intercept as much due to less leaf 

and bark surface area. Although large-growing, the 

littleleaf linden and white ash currently are young 

and small. It is expected that the stormwater benefit 

value these species produce will increase as their 

populations mature.

Aesthetic, Property Value, 

Social, Economic and Other Benefits

Many benefits attributed to urban trees are difficult 

to translate into economic terms. Wildlife habitat, 

beautification, privacy, shade that increases human 

comfort, sense of place, and well-being are difficult to price. However, the value of some of these 

benefits may be captured in the property values of 

the land on which trees stand (Figure 10). To estimate the value of these “other” intangible benefits, 

research comparing differences in sales prices of 

houses was used to estimate the contribution associated with trees. The difference in sales price 

Figure 10—Trees add beauty and value to residential 

property
26

reflects the willingness of buyers to pay for the 

benefits and costs associated with trees. This approach has the virtue of capturing what buyers perceive as both the benefits and costs of trees in the 

sales price. One limitation of using this approach is 

the difficulty associated with extrapolating results 

from front-yard trees on residential properties to 

trees in other locations (e.g., commercial vs. residential) (see Appendix C for more details).

The estimated total annual benefit associated with 

property value increases and other less tangible benefits attributable to Boise ROW trees is $561,917 

or $24.16 per tree on average (Table 11). Generally, the larger the tree, the more benefits provided. 

Therefore, the Boise ROW species that produce 

the highest average annual benefits are among the 

largest trees currently in the population. These include silver maple ($42.92 per tree), London planetree ($42.18 per tree), and Siberian elm ($40.62). 

As species diversity increases, other large-growing 

tree species will no doubt provide similar benefits. 

Conversely, small trees like hawthorn (Crataegus 

spp., $9.14 per tree), crabapple ($11.53 per tree) 

and Scotch pine ($13.17 per tree) produce the least 

benefits. 

Table 11—Total annual increases in property value produced by street trees

Species Total ($) % of trees % of total $ Avg. $/tree

Silver maple 82,663 8.3 14.7 42.92

Norway maple 51,322 10.4 9.1 21.12

Honeylocust 48,432 6.9 8.6 30.14

American sycamore 25,418 3.1 4.5 35.65

Maple 25,086 3.3 4.5 32.58

White ash 22,004 3.8 3.9 25.15

Green ash 21,963 4.5 3.9 21.00

Elm 18,060 2.2 3.2 36.12

Ash 15,412 2.7 2.7 24.82

London planetree 14,467 1.5 2.6 42.18

Northern red oak 14,191 2.0 2.5 30.52

American basswood 13,770 2.9 2.5 20.49

Siberian elm 12,472 1.3 2.2 40.62

Black walnut 11,784 1.8 2.1 28.60

Callery pear 11,137 3.2 2.0 14.95

Sweetgum 10,960 2.7 2.0 17.79

Crabapple 10,881 4.1 1.9 11.53

English elm 9,775 1.3 1.7 32.26

Black locust 9,638 2.6 1.7 16.04

Austrian pine 7,280 1.5 1.3 20.17

Blue spruce 7,180 1.9 1.3 15.92

Littleleaf linden 6,812 1.3 1.2 23.09

Scotch pine 6,151 2.0 1.1 13.17

Northern catalpa 5,823 1.5 1.0 16.88

Sugar maple 5,289 1.5 0.9 15.03

Cottonwood 5,239 1.2 0.9 19.26

Plum 4,282 1.3 0.8 14.77

Hawthorn 3,563 1.7 0.6 9.14

Other street trees 80,863 17.8 14.4 19.52

Citywide total 561,917 100.0 100.0 24.16
27

Total Annual Net Benefits 

and Benefit–Cost Ratio

Total annual benefits produced by Boise’s municipal ROW trees are estimated at $1,002,263 ($43.09 

per tree, $4.78 per capita) (Table 12). Over the 

same period, tree-related expenditures are estimated to be $770,784 ($33.13 per tree, $3.71 per 

capita). Net annual benefits (benefits minus costs) 

are $231,479 or $9.95 per tree and $1.11 per capita. Boise’s ROW trees currently return $1.30 to the 

community for every $1 spent on their management. Boise’s benefit-cost ratio (BCR) of 1.30 is 

similar to those reported for Berkeley, CA (1.37), 

Charleston, SC (1.34), and Albuquerque (1.31), but 

is below those reported for Fort Collins, CO (2.18), 

Cheyenne, WY (2.09), and Bismarck, ND (3.09) 

(Maco et al. 2005; Vargas et al. 2006; McPherson 

et al. 2006, 2005a). 

Boise’s ROW trees have beneficial effects on the 

environment. Nearly half (44%) of the annual benefits provided to residents of the city are environmental services. Energy savings represents 75% 

of environmental benefits, with stormwater runoff 

reduction accounting for another 22%. Air quality 

improvement (1.4%) and carbon dioxide reduction 

(1.4%) provide the remaining environmental benefits. Annual increases in property value by street 

ROW trees provide the largest benefit, accounting 

for 56% of total annual benefits. 

Table 13 shows the distribution of total annual 

benefits in dollars for the predominant municipal 

ROW tree species in Boise. On a per tree basis, 

American sycamore ($87 per tree) and English elm 

($85 per tree) produced second and third largest 

benefits next to silver maple at $90. However, at 

the species level, four species account for nearly 

40% of all benefits – silver maple (17.4%), Norway 

maple (8.7%), honeylocust (7.3%), and American 

sycamore (6.2%). It should be noted once again 

that this analysis provides benefits for a snapshot in 

time. White and green ash are the third and fourth 

most predominant tree species, but they are young 

populations with over 80% of trees measuring less 

than 12 inches DBH. They are poised to become 

Table 12—Benefit–cost summary for all street trees

Benefits Total ($) $/tree $/capita

 Energy 331,756 14.26 1.59

 Carbon dioxide 6,060 0.26 0.03

 Air Quality 6,292 0.27 0.03

 Stormwater 96,238 4.14 0.46

 Aesthetic/other 561,917 24.16 2.70

Total Benefits 1,002,263 43.09 4.82

Costs

 Planting 67,728 2.91 0.33

 Pruning 115,356 4.96 0.55

 Pest management 2,336 0.10 0.01

 Removal 141,417 6.08 0.68

 Administration 124,494 5.35 0.60

 Inspection/service 70,831 3.04 0.34

 Infrastructure repairs 87,780 3.77 0.42

 Litter clean-up 45,270 1.95 0.22

 Liability/claims 825 0.04 0.00

 Other costs 114,747 4.93 0.55

Total costs 770,784 33.13 3.71

Net benefits 231,479 9.95 1.11

Benefit-cost ratio 1.30
28

the city’s most beneficial species in the future, with 

benefit production increasing every year. This is 

important because over 60% of the city’s current 

most beneficial trees (silver maple) are mature or 

senescent and smaller species, such as crabapple 

($17 per tree), Callery pear ($19 per tree), and hawthorn ($19 per tree) will provide correspondingly 

lower benefits despite increased new plantings.

This is not to insist that large trees are always the 

best option. Numerous considerations drive species choice, including planting site, potential conflicts with infrastructure, maintenance concerns, 

water use, and design considerations. In some cases, small trees (Class I) are the best or only option. 

Nonetheless, the results of this analysis emphasize 

that large trees (Class II and III) should be planted 

wherever possible to increase the benefits to the 

citizens of Boise.

Figure 11 illustrates the average annual benefits per 

tree by district and reflects differences in tree types 

and ages. The trees of District 2 provide $49.59 in 

benefits on average each year, which can be attributed to the predominant species (see Table 2) – all 

of which are large-stature trees – and the number of 

trees within the district. The trees of District 3, in 

contrast, provide only $33.69 in benefits on averTable 13—Average annual benefits ($ per tree) of street trees by species

Species Energy CO2 Air quality Stormwater Aesthetic/other $/tree Total ($) % of total $

Silver maple 36.65 −0.23 0.74 10.25 42.92 90.34 173,990 17.4

Norway maple 11.57 0.41 0.19 2.66 21.12 35.94 87,343 8.7

Honeylocust 11.38 0.23 0.21 3.44 30.14 45.40 72,954 7.3

American sycamore 36.16 0.65 0.33 14.18 35.65 86.97 62,011 6.2

Maple 12.79 0.17 0.20 2.63 32.58 48.38 37,250 3.7

Elm 23.73 0.91 0.50 10.25 36.12 71.51 35,754 3.6

Green ash 10.32 0.17 0.30 2.29 21.00 34.08 35,651 3.6

Ash 20.18 0.33 0.63 4.76 24.82 50.71 31,491 3.1

White ash 4.70 0.10 0.13 0.94 25.15 31.02 27,140 2.7

English elm 34.75 1.35 0.75 15.47 32.26 84.59 25,630 2.6

Black walnut 25.67 0.58 0.47 5.96 28.60 61.28 25,248 2.5

Northern red oak 17.28 0.36 0.22 4.15 30.52 52.53 24,428 2.4

American basswood 10.52 0.19 0.29 3.05 20.49 34.54 23,213 2.3

Siberian elm 21.21 0.83 0.42 8.52 40.62 71.60 21,982 2.2

Black locust 16.70 0.76 0.38 0.98 16.04 34.86 20,953 2.1

London planetree 12.98 0.20 0.14 4.21 42.18 59.71 20,480 2.0

Crabapple 4.69 0.15 0.07 0.26 11.53 16.70 15,763 1.6

Sweetgum 6.10 0.10 -0.01 1.14 17.79 25.13 15,479 1.5

Callery pear 3.09 0.14 0.05 0.78 14.95 19.01 14,161 1.4

Cottonwood 22.50 0.17 0.80 6.22 19.26 48.95 13,315 1.3

Northern catalpa 16.94 0.27 0.28 3.98 16.88 38.35 13,230 1.3

Blue spruce 5.85 0.11 0.07 5.42 15.92 27.36 12,340 1.2

Austrian pine 5.61 0.13 0.03 4.67 20.17 30.61 11,050 1.1

Scotch pine 2.41 0.06 0.02 1.90 13.17 17.56 8,200 0.8

Littleleaf linden 2.24 0.09 0.06 0.58 23.09 26.05 7,685 0.8

Sugar maple 5.65 0.15 0.09 0.75 15.03 21.68 7,630 0.8

Hawthorn 8.24 0.21 0.13 1.66 9.14 19.37 7,554 0.8

Plum 6.76 0.22 0.11 0.38 14.77 22.22 6,444 0.6

Other street trees 11.24 0.26 0.21 3.50 19.52 34.73 143,894 14.4
29

age, due to the low numbers of trees within the district (about 1,300 compared to 5,000 in District 2) 

and the fact that the majority are young – very few 

trees are within the largest size class (only 1.3% are 

larger than 30-inch DBH compared to over 10% in 

District 2).

Figure 11—Average annual street tree benefits per tree by management district

0.00

10.00

20.00

30.00

40.00

50.00

60.00

Not

Assigned

1 2 3 4 5 6 7 Citywide

total

District

$ per tree

Aesthetic/Other

Stormwater

Air Quality

CO2

Energy
30
31

Chapter Five—Management Implications

Boise’s urban forest reflects the values, lifestyles, 

preferences, and aspirations of current and past residents. It is a dynamic legacy whose character will 

change greatly over the next decades. Although 

this study provides a “snapshot” in time of the municipal ROW tree resource, it also serves as an opportunity to speculate about the future. Given the 

status of Boise’s ROW tree population, what future 

trends are likely and what management challenges 

will need to be met to sustain or increase this level 

of benefits? 

Focusing on three components—resource complexity, resource extent, and maintenance—will 

help refine broader municipal tree management 

goals. Achieving resource sustainability will produce long-term net benefits to the community while 

reducing the associated costs incurred in managing 

the resource. 

Resource Complexity

The Parks and Recreation Department, Community 

Forestry Unit of Boise is to be commended for its 

commitment to increasing the diversity of the urban 

forest. The number of ROW species (179) is excellent, particularly considering the desert plateau climate. It is evident that there has been increased effort to both diversify and improve the age structure 

of the public right of way trees. The distribution of 

trees across species, with no one species representing significantly more than 10% of the total is fairly 

unique among the cities we have studied. However, 

there is reason to remain concerned over the predominance of maples generally. As a genus, these 

trees represent 24% of the total ROW tree population and produce 30.1% of all benefits enjoyed by 

residents of Boise. Ash species represent another 

11% of the population and currently produce 9.5% 

of the benefits. As previously mentioned, with 80% 

of the ash under 12-inch DBH, these species are 

poised to become the next generation of major benefit producers within the city. Care must be taken to 

maintain and monitor these two genera to protect 

them from disease and pest infestations now occurring in other parts of the nation. New York City has 

removed over 5,000 maples in the past few years 

due to Asian longhorn beetle (ALB) infestation. 

Emerald ash borers (EAB) have killed more than 

20 million ash trees in Michigan, Ohio, and Indiana. Boise’s ash trees currently suffer infestations 

of ash-lilac borers and Pacific flathead borers.

Figure 12 displays large- and medium-growing 

trees in the smallest DBH size classes, indicatFigure 12—Predominant species in the smallest diameter classes (0-6” DBH) indicating relatively recent tree planting 

and survival trend

0

200

400

600

800

1,000

1,200

1,400

Norway

maple

Honeylocust White ash Green ash Callery pear Sweetgum Scotch pine Maple

species

Basswood Sugar

maple

No. of Trees 

0-3" DBH 3-6" DBH
32

ing trends in new and replacement trees. Norway 

maples predominate and the maple genus accounts 

for 23% of all relatively recent plantings (0–3 inch 

DBH). Ash, as a genus, composes another 17% of 

the 5,500 0–3 inch trees. These two genera account 

for 40% of the new tree population in the 2005 inventory, and their popularity could increase their 

susceptibility pests and diseases. Aware of the importance of reducing the number of potential host 

species for ash-lilac and Pacific flathead borers, as 

well as ALB and EAB, the Community Forestry 

staff is carefully monitoring further municipal 

plantings of these species. 

Increasing the planting of high benefit species like 

Northern red oak (Quercus rubra) and American 

basswood (Tilia americana) is possible. Both species had above-average relative performance indices. Both produced significant benefits although 

they remain relatively young populations. The performance of littleleaf linden should be followed 

as well, as it may become a high benefit tree. Of 

the 295 trees planted, over 91% are in the smallest 

DBH class, yet the species still produces nearly 1% 

of the city’s benefits. Expanding upon the planting 

of species with high relative performance and leaf 

area but low susceptibility to pests and disease will 

be vital to maintaining the flow of benefits through 

time as well as insuring the health of the urban 

forest.

Resource Extent

Canopy cover, or more precisely the amount and 

distribution of leaf surface area, is the driving force 

behind the urban forest’s ability to produce benefits for the community. As the number of trees, 

and therefore canopy cover increases, so do the 

benefits afforded by leaf area. Maximizing the return on investment is contingent upon maximizing 

and maintaining the quality and extent of Boise’s 

canopy cover. 

From 2000–2006, the Community Forestry Unit 

planted (including replacements) about 250 trees 

annually. Over the same 7-year period removals 

averaged 288 trees annually. Tree planting is expected to increase over the next few years to about 

270 new trees annually. Given that the current mortality rate is 30% over the first 40 years of growth, 

we would expect 81 of these trees to die before 

reaching maturity, leaving 189 to continue growing and producing benefits. This removal rate combined with mortality numbers indicates a net loss 

in Boise tree numbers and canopy cover over time 

unless the city acts to reverse the current trend.

Any tree added to a city adds benefits in terms of 

air quality improvement, climate moderation, reductions in energy use, stormwater management 

and aesthetic improvement—benefits that have 

been described in detail above. Planting trees along 

streets and in parking lots, however, offers additional benefits beyond those that come from planting trees in parks. Most importantly, trees located 

along streets and in parking lots are more likely 

to shade structures. By moderating the immediate 

climate around a building, energy use is reduced, 

lowering costs for building owners and simultaneously reducing air pollutants and CO2

. 

Trees along streets have also been shown to reduce the wear on asphalt by lowering surface temperatures and thereby reducing maintenance costs 

(McPherson and Muchnick 2005). A study comparing several blocks in Modesto, CA, demonstrated 

that streets shaded by large trees required fewer 

than half the number of slurry seals (2.5 vs. 6 on an 

unshaded street) over a 30-year period, with associated savings of $0.66/ft2. In areas with on-street 

parking, trees can have an additional benefit of 

reducing pollutant emissions from parked cars by 

lowering local air temperature (Scott et al. 1999). 

Evaporative emissions from parked vehicles account for 16% of total vehicular emissions; lowering the air temperature by increasing shade cover 

in Sacramento parking lots to 50% from 8% was 

estimated to reduce overall emissions by 2% (0.85 

tons per day). Although seemingly modest, many 

existing programs to improve air quality have similar goals. 
33

The importance of size in achieving high levels of 

benefits should also not be forgotten. Large-growing trees should be planted wherever possible. In 

Boise this translates into planting Class II and III 

trees wherever possible.

Currently trees are planted and replanted in the 

same ROW locations they have been in for years 

with few new plantings in new locations. Planting 

is limited for a variety of reasons, but predominantly due reduced space for trees in new developments 

and drought conditions. Trees have been struggling 

with recent droughts and warm, dry winters, and 

water availability has become a deciding factor 

in whether to replace dead and dying trees. More 

people are deciding not to water city right-of-way 

tree lawns in front of their homes either to conserve 

water or reduce their water expenditures. This reduction in watering is currently the largest factor in 

the decline of many existing public street trees and 

a huge challenge to increasing street tree presence. 

Additionally, as development has occurred in Boise, the local highway district has generally only 

purchased enough right-of-way to build a road and 

sidewalk, leaving few locations where new trees 

can be planted. A few local developers increasingly want to return to more classical neighborhoods 

and streetscapes with tree lawns, allowing for the 

planting of more trees. In 2005, at the urging of 

the Community Forestry Unit, the highway district 

created policies setting size standards for tree lawns 

in the right-of-way and these standards are now in 

place. This, coupled with pro-tree developers may 

assist the city in turning around a trend toward net 

losses in tree cover. Further education for citizens 

and local governmental entities in charge of development is recommended to increase awareness of 

the environmental value of street trees.

ROW Canopy Cover 

In 2006, Boise Mayor David Beiter signed the 

U.S. Mayors Climate Protection Agreement, making Boise the first city in Idaho and the 280th city 

in the nation to endorse the agreement. Boise will 

strive to meet or exceed a 7% reduction from the 

1990 greenhouse gas emission level through such 

measures as energy-efficient building practices, 

alternative fuels, improved transportation, and improved land-use planning. 

By shading the gray infrastructure, canopy cover 

over streets and sidewalks contributes directly to 

reducing urban heat island effects, reducing energy 

consumption, ground level ozone, and the formation of greenhouse gases. As cities grow, carbon 

emissions, and air and water pollution typically 

increase. However, the value of the benefits that 

trees provide typically also increase. The mayor 

of New York City has recently acknowledged this 

and increased funding for the planting and care of 

trees by $37 million per year, establishing a longterm commitment in using the green infrastructure 

to mitigate the impact of a vast gray infrastructure.

As Boise has grown in recent years, public rightof-way planning in new neighborhoods has not 

allocated adequate space for trees, a contributing 

factor to the 14% stocking level citywide (29 trees/

mile; one tree for every nine residents), one of the 

lowest levels among cities studied thus far. Boise’s 

tree canopy currently shades 6.5% of the city’s 

streets and sidewalks. We recommend that the city 

focus on increasing stocking and canopy cover, setting an initial goal of planting one ROW tree for 

every five residents. This represents an increase 

of over 18,000 ROW trees (41,600 total compared 

to 23,262 currently) for a 25% stocking level and 

10.5% canopy cover over streets and sidewalks. 

Planning for planting new trees must also include 

planning for adequate care and maintenance to reduce mortality rates and insure survival.

Maintenance

Boise’s maintenance challenges in the coming 

years will be to establish and care properly for the 

many new trees that have been planted and to maintain and eventually remove the old silver maples, 

American sycamores, and elms as they continue 

to decline. With 270 new trees planted each year, 
34

a strong young-tree care program is imperative to 

insure, first, that the trees survive, and second, that 

they transition into well-structured, healthy mature trees requiring minimal pruning. Investing in 

the young-tree care program will reduce costs for 

routine maintenance as trees mature and reduce removal and replacement costs for dead trees. 

It will be a significant challenge, but the Parks 

and Recreation Department should work to secure 

funding to increase the young tree maintenance 

cycle to at least two visits during the first 5 years 

of establishment. Funding for establishment irrigation should also be considered, and inspection and 

pruning of young trees should be a priority. Expansion and enhancement of the Tree Steward volunteer program to take advantage of citizens willing 

to give time to improve the community forest could 

significantly reduce potential increases in costs associated with improving tree care.

The older silver maples, American sycamores, and 

elms are reaching the end of their natural life spans 

and are in decline. Like people, older trees tend to 

develop problems that younger trees do not; Boise’s silver maples often develop significant internal 

decay, which can result in dangerous loss of large 

branches. Silver maples are also cause significant 

damage when planted too 

near gray infrastructure 

because they have shallow 

root systems and large root 

crowns. Boise’s silver maples will require increased 

maintenance as they age 

and eventually they will 

have to be removed. The 

future of these species, 

which provide an enormous share of the benefits 

of the urban forest, should 

be considered with special 

care. Additionally, because 

of their stature and grace 

they tend to be especially 

beloved by residents (Figure 13). For these reasons, a careful plan should be developed to begin 

planting similarly beneficial and beautiful trees before all of the older trees have to be removed. The 

trees in the worst condition should be replaced first, 

while treating the others to prolong their lifespan. 

Planned replacement involves assessing the tree 

population, particularly in those neighborhoods 

dominated by even-aged trees of the same species, 

and establishing a program of systematic removal 

and replacement so that the neighborhood will not 

suffer suddenly from a complete die-off or removal 

of hazardous trees. 

Additionally, if the city decides to increase canopy 

cover along public ROW, it is vital that an adequate 

maintenance program be funded to care for the 

increased number of trees and better insure their 

survival.

Figure 13—Old trees grace a residential neighborhood
35

Chapter Six—Conclusion

This analysis describes structural characteristics of 

the municipal tree population and uses tree growth 

and geographic data for Boise to model the ecosystem services trees provide the city and its residents. In addition, the benefit–cost ratio has been 

calculated and management needs identified. The 

approach is based on established tree sampling, 

numerical modeling, and statistical methods and 

provides a general accounting of the benefits produced by municipal trees in Boise that can be used 

to make informed decisions. 

Boise’s 23,262 ROW trees are a valuable asset, 

providing over $1 million ($43 per tree) in annual 

gross benefits. Benefits to the community are most 

pronounced for energy savings and aesthetic and 

other benefits. Thus, municipal street ROW trees 

play a particularly important role in maintaining 

the environmental and aesthetic qualities of the 

city (Figure 14). Boise spends approximately $771 

thousand maintaining these ROW trees or $33.13 

per tree.

After expenditures are taken into account, Boise’s 

ROW tree resource currently provides approximately $231,479 or $9.95 

per tree ($1.11 per capita) 

in net annual benefits to the 

community. Over the years, 

Boise has invested millions 

of dollars in these trees. Citizens are seeing a return on 

that investment—receiving 

$1.30 in benefits for every 

$1 spent on tree care. The 

fact that Boise’s benefit–cost 

ratio exceeds 1.0 indicates 

that the program is not only 

operationally efficient, but is 

capitalizing on the functional 

services its trees can produce. 

Over 66% of the tree population is relatively young – 

under 12 inches DBH – and nearly 75% of these 

trees are medium to large-growing (Class II and 

III) trees. The value of Boise’s ROW trees will increase if the many young trees planted by the Community Forestry Unit can survive and mature. As 

the resource grows, continued investment in management is critical, insuring that the trees are properly cared for so residents receive a high return on 

investment in the future.

Boise’s ROW trees are a dynamic resource. Managers of the urban forest and the community alike 

can take pride in knowing that these trees greatly 

improve the quality of life in the city. However, the 

trees are also a fragile resource needing constant 

care to maximize and sustain production of benefits into the future while also protecting the public from potential hazard. The challenge as the city 

continues to grow will be to sustain and expand 

the existing canopy cover to take advantage of the 

increased environmental and aesthetic benefits the 

trees can provide to the community. 

Management recommendations focused on sustaining existing benefits and increasing future benefits 

include the following: 

Figure 14—Norway maple leaves. Tree leaves help clean the air by absorbing pollutants, reduce stormwater runoff by intercepting rainfall, and reduce energy use by 

shading homes and businesses
36

1. To help meet Boise’s Climate Protection 

Agreement goals to reduce greenhouse gases 

and emissions:

•	 Develop programs and policies to encourage and significantly increase shade tree 

planting along streets, in parking lots, and 

near buildings in and adjacent to public 

rights-of-way.

•	 Increase ROW stocking and canopy cover, setting an initial goal of planting one 

ROW tree for every five residents. This 

represents an increase of over 18,000 

ROW trees (41,600 total compared to 

23,262 currently) for a 25% stocking level 

and 10.5% canopy cover over streets and 

sidewalks. 

•	 Increase stocking level with larger-growing (Class II and III) shade tree species 

where conditions are suitable to maximize 

benefits. Consider adopting front-yard 

easements for new tree planting and maintenance where parkway planting strips are 

absent.

•	 Plan for adequate care and pruning to reduce tree mortality rates and insure survival.

2. Develop a strong young-tree care program that 

emphasizes reducing mortality. Irrigation along 

with inspection and pruning at least twice during the initial five years after planting will provide a good foundation for the trees. Expansion 

of the Tree Steward program is a cost-effective 

approach to accomplishing this goal.

3. Track the success of the newly planted trees to 

determine those most adaptable to the difficult 

growing conditions in Boise. Continue planting 

a diverse mix of tree species to guard against 

catastrophic losses due to storms, pests or disease while concentrating the species choice on 

those that have proven most successful.

4. Sustain current benefits by investing in programmed maintenance of mature trees (e.g., 

silver maple, American basswood, elms, Norway maple, American sycamore) to prolong 

the functional life spans of these trees. This 

includes developing a planned replacement 

program designed to gradually and systematically replace senescent trees with trees that 

will grow to similar stature. 

5. Continue working with the Ada County Highway District to develop planting space and tree 

guidelines in new developments and when retrofitting existing streets. Adequate space for 

Class II and III trees should be a priority.

6. Implement Forestry Management Plan priority 

recommendations including the development 

and implementation of a public education and 

volunteer program to demonstrate the link between basic tree care – irrigation frequencies 

and appropriate maintenance levels – and the 

resultant benefits provided to the homeowner, 

neighborhood, and community. This should assist in increasing available planting space and 

improving tree longevity, functionality, and 

overall benefits. 

7. Develop, implement, and enforce a landscaping ordinance for Boise that creates specific 

public and private street and parking lot shade 

guidelines to promote tree canopy cover and 

associated benefits.

These recommendations build on a history of dedicated management and commitment to natural resource preservation that has put Boise on course to 

provide an urban forest resource that is both functional and sustainable.
37

Appendix A—Tree Distribution

Table A1—Tree numbers by size class (DBH in inches) for all street trees

Species 0–3 3–6 6–12 12–18 18–24 24–30 30–36 36–42 >42 Total

Broadleaf deciduous large (BDL)

Acer saccharinum 52 46 98 173 347 577 381 160 92 1,926

Gleditsia triacanthos 484 324 535 218 23 18 3 2 - 1,607

Fraxinus pennsylvanica 307 351 202 92 46 32 12 3 1 1,046

Fraxinus americana 499 267 86 17 - 2 4 - - 875

Platanus occidentalis 61 23 34 61 139 195 145 44 11 713

Tilia americana 209 122 141 96 67 26 9 1 1 672

Fraxinus species 58 134 128 131 98 55 15 1 1 621

Liquidambar styraciflua 282 120 128 72 11 3 - - - 616

Robinia pseudoacacia 36 74 113 108 116 110 29 13 2 601

Ulmus species 53 61 64 18 34 76 108 68 18 500

Quercus rubra 187 66 51 48 61 38 13 1 - 465

Juglans nigra 13 30 106 134 84 34 5 4 2 412

Acer saccharum 248 38 26 22 10 7 1 - - 352

Catalpa speciosa 13 40 47 65 66 73 33 7 1 345

Platanus acerifolia 140 55 85 27 13 15 4 2 2 343

Ulmus pumila 19 21 56 49 58 66 24 12 2 307

Ulmus procera - 1 4 1 28 67 110 69 23 303

Populus species 36 78 35 18 10 24 24 18 29 272

Quercus alba 18 14 28 25 38 38 11 2 - 174

Ulmus americana - - - 9 16 57 49 27 9 167

Ailanthus altissima 7 14 35 25 20 19 6 1 - 127

Quercus macrocarpa 86 5 5 2 13 8 - - - 119

Acer negundo 14 5 11 18 16 20 5 2 2 93

Quercus robur 17 42 8 9 8 3 5 - - 92

Quercus species 29 18 6 8 4 1 2 - - 68

Liriodendron tulipifera 16 14 8 6 2 1 2 - - 49

Quercus palustris 3 7 15 5 5 2 - - - 37

Gymnocladus dioicus 17 2 4 8 2 2 - - - 35

Ulmus parvifolia 21 11 1 - - - - - - 33

Populus deltoides - - 1 8 3 8 2 - 9 31

Fraxinus quadrangulata - 1 7 15 2 2 - - - 27

Populus × canadensis - 2 6 6 7 - 1 1 3 26

Taxodium distichum 8 5 2 3 2 - - - - 20

Betula nigra 19 - - - - - - - - 19

Quercus bicolor 13 2 - - 1 - 1 1 - 18

Populus trichocarpa 5 2 - - - - - 2 3 12

Quercus coccinea 1 4 4 - 1 1 1 - - 12

Betula papyrifera 8 - 1 - - - - - - 9

Populus alba 2 - - 1 - 2 1 1 - 7

Quercus velutina 6 - - - - - - - - 6

Acer nigrum - - 1 1 1 - - - - 3

Carya species 1 - - 2 - - - - - 3

Castanea dentata - 1 1 - - 1 - - - 3
38

Species 0–3 3–6 6–12 12–18 18–24 24–30 30–36 36–42 >42 Total

Populus nigra - - - 2 - 1 - - - 3

Quercus falcata 2 - - - - - - - - 2

Ulmus glabra - - - - 1 - 1 - - 2

Larix decidua 1 - - - - - - - - 1

Magnolia acuminata - - - 1 - - - - - 1

Populus balsamifera - 1 - - - - - - - 1

Quercus phellos - - - - - - - 1 - 1

Total 2,991 2,001 2,083 1,504 1,353 1,584 1,007 443 211 13,177

Broadleaf deciduous medium (BDM)

Acer platanoides 669 505 607 421 162 57 7 2 - 2,430

Acer species 239 135 203 130 51 9 1 2 - 770

Pyrus calleryana 377 256 106 5 1 - - - - 745

Tilia cordata 208 62 17 4 4 - - - - 295

Sophora japonica 40 41 38 13 17 2 - - - 151

Ginkgo biloba 65 14 39 28 3 - - - - 149

Tilia species 50 46 28 14 9 2 - - - 149

Tilia tomentosa 105 22 1 - 1 2 4 1 - 136

Acer rubrum 37 44 26 5 1 - - - - 113

Celtis occidentalis 56 46 3 - - 1 - - 2 108

Aesculus hippocastanum 3 5 11 28 11 4 4 2 - 68

Fraxinus oxycarpa 43 13 7 - - - - - - 63

Corylus colurna 51 7 - - - - - - - 58

Fraxinus excelsior - 2 21 23 6 1 - - - 53

Salix species 7 4 6 2 7 14 6 3 3 52

Carpinus betulus 48 3 - - - - - - - 51

Other species 21 10 9 4 2 - - - - 46

Populus tremuloides 19 18 1 - - - - - - 38

Phellodendron amurense 24 - - - - - - - - 24

Fraxinus mandshurica 20 - - - - - - - - 20

Juglans regia 2 4 9 2 - - 2 - - 19

Morus alba 1 - 6 5 3 1 1 - - 17

Betula pendula 4 1 6 4 1 - - - - 16

Catalpa bignonioides 9 4 - - 1 - - - - 14

Fagus species 10 2 1 - 1 - - - - 14

Corylus species - 12 - - - - - - - 12

Acer pseudoplatanus 5 3 1 1 - 1 - - - 11

Fagus sylvatica 9 - - - - - 1 - - 10

Betula species 6 3 - - - - - - - 9

Alnus glutinosa 3 4 - - 1 - - - - 8

Eucommia ulmoides 5 - 1 - - - - - - 6

Cercidiphyllum japonicum 4 - 1 - - - - - - 5

Gleditsia caspica 5 - - - - - - - - 5

Quercus aliena 4 - - - - - - - - 4

Quercus laurifolia - 4 - - - - - - - 4

Quercus glandulifera 3 1 - - - - - - - 4

Aesculus glabra 3 - - - - - - - - 3

Salix nigra 3 - - - - - - - - 3
39

Species 0–3 3–6 6–12 12–18 18–24 24–30 30–36 36–42 >42 Total

Fraxinus nigra - 2 - - - - - - - 2

Quercus acutissima 1 1 - - - - - - - 2

Alnus cordata 1 - - - - - - - - 1

Fraxinus ornus - 1 - - - - - - - 1

Morus rubra - - 1 - - - - - - 1

Quercus kelloggii 1 - - - - - - - - 1

Quercus muehlenbergii 1 - - - - - - - - 1

Total 2,162 1,275 1,149 689 282 94 26 10 5 5,692

Broadleaf deciduous small (BDS)

Malus species 424 329 155 34 2 - - - - 944

Crataegus species 117 75 128 63 7 - - - - 390

Prunus species 51 141 88 7 3 - - - - 290

Koelreuteria paniculata 18 25 30 17 1 1 - - - 92

Crataegus phaenopyrum 69 14 2 - - - - - - 85

Crataegus monogyna - 5 36 23 - - - - - 64

Cercis canadensis 24 6 14 5 - - - - - 49

Acer campestre 45 - - - - - - - - 45

Malus pumila 13 11 13 7 1 - - - - 45

Sorbus aucuparia 9 10 12 8 1 - - - - 40

Rhus species 16 17 2 - - 1 - - - 36

Acer truncatum 32 1 1 - - - - - - 34

Elaeagnus angustifolia 6 2 12 12 1 - - - - 33

Crataegus laevigata 3 9 13 2 - - - - - 27

Cornus florida 12 7 4 - - - - - - 23

Prunus cerasifera 14 8 1 - - - - - - 23

Prunus triloba 4 9 5 1 - - - - - 19

Pyrus fauriei 19 - - - - - - - - 19

Populus grandidentata 8 8 1 1 - - - - - 18

Prunus virginiana 13 1 - - - - - - - 14

Rhus typhina 6 7 1 - - - - - - 14

Acer ginnala 11 2 - - - - - - - 13

Magnolia species 6 1 4 1 - - - - - 12

Malus ioensis 1 10 1 - - - - - - 12

Crataegus viridis 8 3 - - - - - - - 11

Syringa reticulata 11 - - - - - - - - 11

Cladrastis lutea 10 - - - - - - - - 10

Maackia amurensis 10 - - - - - - - - 10

Prunus serrulata 6 2 2 - - - - - - 10

Carpinus caroliniana 5 4 - - - - - - - 9

Cornus species 5 1 3 - - - - - - 9

Prunus persica 6 3 - - - - - - - 9

Diospyros virginiana - - 1 4 2 - - - - 7

Sorbus americana - 2 5 - - - - - - 7

Albizia julibrissin 6 - - - - - - - - 6

Laburnum × watereri 3 1 1 - - - - - - 5

Pterostyrax corymbosa 4 - - - - - - - - 4

Acer griseum 3 - - - - - - - - 3
40

Species 0–3 3–6 6–12 12–18 18–24 24–30 30–36 36–42 >42 Total

Acer grandidentatum 3 - - - - - - - - 3

Cotinus coggygria - - 2 1 - - - - - 3

Prunus sargentii 3 - - - - - - - - 3

Ptelea trifoliata 3 - - - - - - - - 3

Pyrus communis 1 2 - - - - - - - 3

Salix matsudana 1 1 1 - - - - - - 3

Amelanchier arborea - - 1 - - - - - - 1

Cotinus obovatus 1 - - - - - - - - 1

Prunus armeniaca - - - - 1 - - - - 1

Prunus blieriana 1 - - - - - - - - 1

Total 1,011 717 539 186 19 2 - - - 2,474

Conifer evergreen large (CEL)

Picea pungens 57 168 86 99 29 10 1 - 1 451

Pinus species 17 43 40 12 2 - 1 - - 115

Picea abies 4 11 14 19 18 12 7 - - 85

Pinus ponderosa 5 4 31 26 14 5 - - - 85

Abies concolor 1 5 11 19 11 3 - - - 50

Pseudotsuga menziesii - 3 3 9 1 2 - - - 18

Picea species 1 5 7 2 - - - - - 15

Abies species 3 4 4 2 1 - - - - 14

Sequoiadendron giganteum 1 6 4 - - - - - - 11

Abies homolepis 5 - - - - - - - - 5

Pinus serotina - 2 2 1 - - - - - 5

Picea mariana - 1 2 - - - - - - 3

Thuja occidentalis 1 2 - - - - - - - 3

Thuja plicata 3 - - - - - - - - 3

Picea engelmannii - - 1 1 - - - - - 2

Cedrus deodara - - 1 - - - - - - 1

Pinus strobus - 1 - - - - - - - 1

Tsuga canadensis - - 1 - - - - - - 1

Total 98 255 207 190 76 32 9 - 1 868

Conifer evergreen medium (CEM)

Pinus sylvestris 174 212 67 8 2 4 - - - 467

Pinus nigra 28 135 139 47 7 5 - - - 361

Cedrus species 26 22 18 8 2 - - - - 76

Juniperus virginiana - 9 21 9 4 1 - - - 44

Juniperus species 13 14 5 - - - - - - 32

Pinus glabra 1 4 1 1 - 1 - - - 8

Pinus monticola - 7 1 - - - - - - 8

Picea asperata 7 - - - - - - - - 7

Pinus coulteri 1 5 - - - - - - - 6

Picea omorika 3 2 - - - - - - - 5

Picea orientalis 5 - - - - - - - - 5

Picea glauca 2 1 - - - - - - - 3

Sciadopitys verticillata 3 - - - - - - - - 3

Cedrus atlantica 1 1 - - - - - - - 2

Pinus attenuata 2 - - - - - - - - 2
41

Species 0–3 3–6 6–12 12–18 18–24 24–30 30–36 36–42 >42 Total

Pinus cembra 2 - - - - - - - - 2

Total 268 412 252 73 15 11 - - - 1,031

Conifer evergreen small (CES)

Pinus mugo 4 2 4 3 1 - - - - 14

Juniperus scopulorum - - 6 - - - - - - 6

Total 4 2 10 3 1 - - - - 20

Citywide total 6,534 4,662 4,240 2,645 1,746 1,723 1,042 453 217 23,262
42
43

Appendix B—Replacement Values

Species 0–6 6–12 12–18 18–24 24–30 30–36 36–42 >42 Total

% of 

Total

Silver maple 28,505 137,676 611,029 2,392,822 6,662,492 6,348,965 3,463,423 2,357,652 22,002,562 24.9

American sycamore 19,963 46,893 226,683 990,527 2,354,429 2,592,123 1,090,125 280,837 7,601,581 8.6

Norway maple 338,113 1,010,245 1,950,829 1,418,823 834,526 146,852 59,605 - 5,758,993 6.5

Elm 34,268 82,497 55,920 213,017 765,365 1,636,198 1,457,405 402,179 4,646,849 5.3

English elm 506 4,970 3,978 180,390 670,634 1,689,091 1,425,427 480,592 4,455,587 5.0

Northern catalpa 21,955 78,967 315,748 612,742 1,094,941 756,575 199,965 30,086 3,110,980 3.5

Black locust 36,068 133,021 334,423 666,072 1,051,526 406,209 231,586 42,580 2,901,485 3.3

Honeylocust 229,053 946,600 1,047,467 226,481 294,462 70,826 69,689 - 2,884,578 3.3

Ash 69,974 198,456 554,975 840,089 730,531 295,597 20,940 23,402 2,733,966 3.1

American elm - - 31,543 114,325 689,649 902,183 650,683 230,872 2,619,255 3.0

American basswood 86,210 245,752 485,897 662,020 428,405 199,650 34,845 32,075 2,174,852 2.5

Green ash 211,228 317,559 397,233 406,472 441,062 268,862 75,423 24,084 2,141,923 2.4

Black walnut 16,344 152,455 523,405 608,443 416,337 93,797 97,084 61,477 1,969,343 2.2

Northern red oak 55,393 94,037 242,873 598,861 596,881 301,450 26,120 - 1,915,614 2.2

Siberian elm 11,563 57,590 132,469 294,997 559,408 311,233 212,475 37,608 1,617,342 1.8

Maple 94,843 304,255 540,269 433,814 124,116 22,289 44,050 - 1,563,636 1.8

Cottonwood 39,135 40,285 53,705 52,477 205,188 244,860 285,801 515,505 1,436,956 1.6

White oak 8,990 44,747 109,480 315,767 512,863 215,904 55,670 - 1,263,421 1.4

Blue spruce 90,049 144,424 456,913 251,521 145,673 20,823 - 30,086 1,139,490 1.3

London planetree 44,579 156,598 131,454 129,354 264,027 104,359 67,476 68,126 965,972 1.1

Sweetgum 96,335 224,473 346,168 107,110 51,026 - - - 825,112 0.9

Crabapple 221,301 277,953 173,914 18,067 - - - - 691,235 0.8

Austrian pine 70,218 239,090 221,335 64,217 73,622 - - - 668,482 0.8

Hawthorn 52,756 221,326 297,376 58,840 - - - - 630,298 0.7

Tree of heaven 7,472 45,077 78,079 127,050 189,702 93,475 21,033 - 561,888 0.6

Norway spruce 5,940 22,208 75,524 151,364 159,454 143,415 - - 557,905 0.6

White ash 198,596 141,862 75,580 - 27,937 95,616 - - 539,591 0.6

Horsechestnut 2,677 17,704 124,300 101,215 64,854 90,572 65,373 - 466,694 0.5

Boxelder 4,859 11,939 49,574 85,621 161,182 68,273 17,321 35,933 434,701 0.5

Ponderosa pine 2,758 53,259 126,785 134,211 75,476 - - - 392,487 0.4

Eastern cottonwood - 1,084 31,119 20,672 85,450 37,253 - 213,421 388,999 0.4

Callery pear 174,986 179,743 22,730 7,461 - - - - 384,919 0.4

Sugar maple 52,813 39,112 95,440 76,622 87,374 18,904 - - 370,264 0.4

Scotch pine 127,773 111,696 39,664 18,788 61,861 - - - 359,781 0.4

Willow 3,080 8,817 6,445 40,886 113,827 81,632 48,196 53,844 356,727 0.4

Japanese pagoda tree 25,264 66,419 62,729 160,020 27,639 - - - 342,072 0.4

English oak 24,390 14,194 45,864 81,953 46,834 114,699 - - 327,933 0.4

Basswood 29,011 49,249 67,375 89,044 27,639 - - - 262,317 0.3

Bur oak 13,754 8,502 10,619 113,942 112,747 - - - 259,564 0.3

White fir 2,630 19,684 90,883 106,063 39,910 - - - 259,170 0.3

Ginkgo 14,349 74,219 138,835 28,745 - - - - 256,148 0.3

Plum 72,808 139,531 19,487 20,947 - - - - 252,773 0.3

Silver linden 23,523 1,554 - 8,213 29,336 92,085 37,003 - 191,714 0.2

Pine 22,931 64,953 54,586 20,340 - 20,076 - - 182,886 0.2

Oneseed hawthorn 2,544 66,749 109,165 - - - - - 178,458 0.2

Goldenrain tree 14,952 50,312 72,556 9,904 16,296 - - - 164,020 0.2

Table B1—Replacement value for Boise’s street trees
44

Species 0–6 6–12 12–18 18–24 24–30 30–36 36–42 >42 Total

% of 

Total

Oak 12,965 9,765 36,177 36,683 14,334 43,249 - - 153,173 0.2

Littleleaf linden 58,971 32,226 22,569 36,134 - - - - 149,898 0.2

European ash 700 26,192 68,312 37,479 11,857 - - - 144,540 0.2

Eastern red cedar 4,299 38,668 39,664 39,395 13,969 - - - 135,995 0.2

Tulip tree 8,956 14,093 26,547 16,970 13,969 45,526 - - 126,059 0.1

Blue ash 551 11,592 63,532 15,435 25,378 - - - 116,488 0.1

Northern hackberry 29,247 4,618 - - 13,055 - - 68,238 115,157 0.1

Beech 2,129 1,685 - 10,703 - - - - 14,517 0.0

Carolina poplar 776 6,725 16,948 36,364 - 1,800 2,322 46,686 111,622 0.1

Pin oak 3,423 22,862 19,943 38,587 23,566 - - - 108,379 0.1

Kentucky coffeetree 3,299 7,254 41,475 19,164 34,978 - - - 106,169 0.1

White mulberry 140 9,675 21,662 26,078 15,852 19,004 - - 92,411 0.1

Red maple 25,714 38,470 19,120 8,972 - - - - 92,276 0.1

Cedar 14,322 27,508 33,050 15,304 - - - - 90,185 0.1

Black cottonwood 1,727 - - - - - 39,619 44,265 85,610 0.1

Douglas fir 1,422 5,241 40,288 8,485 27,937 - - - 83,374 0.1

English walnut 2,293 14,792 9,682 - - 50,570 - - 77,337 0.1

Russian olive 1,879 18,060 49,191 5,863 - - - - 74,992 0.1

Paradise apple 7,269 22,509 34,650 9,033 - - - - 73,461 0.1

Swamp white oak 2,471 - - 9,582 - 27,954 30,473 - 70,480 0.1

Other species 7,701 14,675 19,363 14,788 - - - - 56,528 0.1

Scarlet oak 2,060 6,289 - 8,485 16,962 20,343 - - 54,140 0.1

Eastern redbud 6,025 24,575 23,111 - - - - - 53,712 0.1

White poplar 365 - 2,338 - 20,607 10,551 19,738 - 53,599 0.1

European mountain ash 5,406 15,387 26,223 5,963 - - - - 52,978 0.1

Baldcypress 3,520 3,960 14,054 16,970 - - - - 38,505 0.0

Common persimmon - 1,758 17,754 16,360 - - - - 35,872 0.0

Wych elm - - - 10,303 - 24,703 - - 35,006 0.0

European white birch 886 9,274 15,919 8,705 - - - - 34,784 0.0

Willow oak - - - - - - 32,687 - 32,687 0.0

Sweet mountain pine 1,493 7,257 13,940 9,033 - - - - 31,723 0.0

Smooth hawthorn 4,738 18,021 8,621 - - - - - 31,380 0.0

Sumac 10,331 3,003 - - 11,227 - - - 24,561 0.0

Fir 2,200 5,655 8,035 7,498 - - - - 23,388 0.0

Caucasian ash 13,299 8,855 - - - - - - 22,154 0.0

Spruce 2,033 10,972 8,953 - - - - - 21,959 0.0

European beech 1,031 - - - - 20,343 - - 21,374 0.0

Sycamore maple 1,908 1,758 4,643 - 12,141 - - - 20,450 0.0

Washington hawthorn 16,593 3,611 - - - - - - 20,204 0.0

Juniper 9,151 6,604 - - - - - - 15,755 0.0

Flowering plum 4,105 8,050 3,419 - - - - - 15,574 0.0

American chestnut 506 1,265 - - 12,523 - - - 14,294 0.0

Black maple - 1,825 3,989 7,717 - - - - 13,531 0.0

Black poplar - - 4,358 - 8,084 - - - 12,442 0.0

Quaking aspen 10,875 1,046 - - - - - - 11,921 0.0

Flowering dogwood 5,328 5,937 - - - - - - 11,265 0.0

Spruce pine 2,017 1,958 5,243 - 1,969 - - - 11,187 0.0

European alder 2,385 - - 8,306 - - - - 10,691 0.0

Giant sequoia 3,236 7,254 - - - - - - 10,490 0.0
45

Species 0–6 6–12 12–18 18–24 24–30 30–36 36–42 >42 Total

% of 

Total

Turkish hazelnut 10,118 - - - - - - - 10,118 0.0

Pond pine 976 3,570 5,243 - - - - - 9,789 0.0

Rocky mountain juniper - 9,784 - - - - - - 9,784 0.0

Chinese elm 8,268 1,448 - - - - - - 9,716 0.0

Bigtooth aspen 4,997 1,314 2,727 - - - - - 9,039 0.0

Southern catalpa 3,172 - - 5,863 - - - - 9,035 0.0

European hornbeam 7,936 - - - - - - - 7,936 0.0

Hickory 148 - 7,648 - - - - - 7,796 0.0

Smoke tree - 2,791 4,843 - - - - - 7,634 0.0

Prairie crabapple 5,458 2,091 - - - - - - 7,548 0.0

Cherry plum 6,137 1,241 - - - - - - 7,378 0.0

Magnolia 1,546 3,232 2,313 - - - - - 7,091 0.0

American mountain ash 681 5,936 - - - - - - 6,617 0.0

Apricot - - - 6,615 - - - - 6,615 0.0

Purple blow maple 4,810 1,631 - - - - - - 6,441 0.0

Hedge maple 5,969 - - - - - - - 5,969 0.0

Dogwood 1,176 4,502 - - - - - - 5,678 0.0

Engelmann spruce - 1,503 3,989 - - - - - 5,491 0.0

Western white pine 3,317 1,980 - - - - - - 5,297 0.0

Kwanzan cherry 1,856 3,408 - - - - - - 5,264 0.0

Staghorn sumac 3,964 1,265 - - - - - - 5,230 0.0

Hazelnut 4,831 - - - - - - - 4,831 0.0

Cucumber tree - - 4,372 - - - - - 4,372 0.0

Black spruce 555 3,694 - - - - - - 4,248 0.0

Amur corktree 3,564 - - - - - - - 3,564 0.0

Manchurian ash 2,843 - - - - - - - 2,843 0.0

Peach 2,640 - - - - - - - 2,640 0.0

Korean sun pear 2,610 - - - - - - - 2,610 0.0

Paper birch 1,233 1,356 - - - - - - 2,589 0.0

Coulter pine 2,589 - - - - - - - 2,589 0.0

Green hawthorn 2,556 - - - - - - - 2,555 0.0

American hornbeam 2,543 - - - - - - - 2,543 0.0

River birch 2,521 - - - - - - - 2,521 0.0

Amur maple 2,342 - - - - - - - 2,343 0.0

Hardy rubber tree 684 1,631 - - - - - - 2,315 0.0

Laurel oak 2,302 - - - - - - - 2,302 0.0

Birch 2,264 - - - - - - - 2,264 0.0

Common chokecherry 2,213 - - - - - - - 2,213 0.0

Corkscrew willow 618 1,536 - - - - - - 2,154 0.0

Katsura tree 529 1,539 - - - - - - 2,068 0.0

Downy serviceberry - 1,631 - - - - - - 1,631 0.0

Serbian spruce 1,545 - - - - - - - 1,544 0.0

Eastern hemlock - 1,539 - - - - - - 1,539 0.0

Yellowwood 1,535 - - - - - - - 1,535 0.0

Japanese tree lilac 1,503 - - - - - - - 1,503 0.0

Golden-chain tree 842 614 - - - - - - 1,456 0.0

Deodar cedar - 1,448 - - - - - - 1,448 0.0

Red mulberry - 1,448 - - - - - - 1,448 0.0

Amur maackia 1,314 - - - - - - - 1,314 0.0
46

Species 0–6 6–12 12–18 18–24 24–30 30–36 36–42 >42 Total

% of 

Total

Common pear 1,087 - - - - - - - 1,087 0.0

Northern white cedar 1,039 - - - - - - - 1,039 0.0

Mimosa 993 - - - - - - - 993 0.0

Chinese spruce 987 - - - - - - - 987 0.0

Black ash 976 - - - - - - - 976 0.0

Konara oak 952 - - - - - - - 952 0.0

White spruce 903 - - - - - - - 903 0.0

Japanese fir 890 - - - - - - - 890 0.0

Black oak 787 - - - - - - - 787 0.0

Caspian locust 718 - - - - - - - 718 0.0

Oriental spruce 707 - - - - - - - 707 0.0

Black willow 696 - - - - - - - 696 0.0

Sawtooth oak 605 - - - - - - - 605 0.0

Atlas cedar 588 - - - - - - - 588 0.0

Oriental white oak 538 - - - - - - - 538 0.0

Umbrella pine 509 - - - - - - - 509 0.0

Little epaulettetree 487 - - - - - - - 487 0.0

Eastern white pine 474 - - - - - - - 474 0.0

Balsam poplar 471 - - - - - - - 471 0.0

Ohio buckeye 449 - - - - - - - 449 0.0

Sargent cherry 419 - - - - - - - 419 0.0

Flowering ash 388 - - - - - - - 388 0.0

Common hoptree 384 - - - - - - - 384 0.0

Western redcedar 368 - - - - - - - 368 0.0

Knobcone pine 323 - - - - - - - 323 0.0

Southern red oak 319 - - - - - - - 319 0.0

Paperbark maple 315 - - - - - - - 315 0.0

Bigtooth maple 307 - - - - - - - 307 0.0

Swiss stone pine 294 - - - - - - - 294 0.0

Blierana plum 174 - - - - - - - 174 0.0

American smoketree 174 - - - - - - - 174 0.0

California black oak 159 - - - - - - - 159 0.0

Chinkapin oak 131 - - - - - - - 131 0.0

Italian alder 127 - - - - - - - 127 0.0

European larch 114 - - - - - - - 114 0.0

Citywide total 3,139,759 6,890,789 11,396,330 13,557,148 20,573,085 17,747,887 9,881,556 5,079,547 88,266,102 100
47

Appendix C—Methodology and Procedures

This analysis combines results of a citywide inventory with benefit–cost modeling data to produce 

four types of information: 

1. Resource structure (species composition, diversity, age distribution, condition, etc.)

2. Resource function (magnitude of environmental and aesthetic benefits)

3. Resource value (dollar value of benefits realized)

4. Resource management needs (sustainability, 

pruning, planting, and conflict mitigation)

This Appendix describes municipal tree sampling, 

tree growth modeling, and the model inputs and 

calculations used to derive the aforementioned outputs.

Growth Modeling

A stratified random sample of 930 street trees, 

drawn from Boise’s municipal tree database, was 

inventoried to establish relations between tree age, 

size, leaf area and biomass; subsequently, estimates 

for determining the magnitude of annual benefits 

in relation to predicted tree size were derived. The 

sample was composed of the 20 most abundant 

species; from these data, growth of all trees was 

inferred. The species were as follows:

•	 Norway maple (Acer platanoides)

•	 Silver maple (Acer saccharinum)

•	 Sugar maple (Acer saccharum)

•	 Northern catalpa (Catalpa speciosa)

•	 Hawthorn (Crataegus spp.)

•	 White ash (Fraxinus americana)

•	 Green ash (Fraxinus pennsylvanica)

•	 Honeylocust (Gleditsia triacanthos)

•	 Black walnut (Juglans nigra)

•	 Sweetgum (Liquidambar styraciflua)

•	 Apple (Malus spp.)

•	 Blue spruce (Picea pungens)

•	 Scotch pine (Pinus sylvestris)

•	 London planetree (Platanus acerifolia)

•	 American sycamore (Platanus occidentalis)

•	 Callery pear (Pyrus calleryana)

•	 Northern red oak (Quercus rubra)

•	 Black locust (Robinia pseudoacacia)

•	 American basswood (Tilia americana)

•	 Siberian elm (Ulmus pumila)

To obtain information spanning the life cycle of 

predominant tree species, the inventory was stratified into nine DBH classes: 

• 0–3 in (0–7.6 cm)

• 3–6 in (7.6–15.2 cm)

• 6–12 in (15.2–30.5 cm

• 12–18 in (30.5–45.7 cm)

• 18–24 in (45.7–61.0 cm)

• 24–30 in (61.0–76.2 cm)

• 30–36 in (76.2–91.4 cm)

• 36–42 in (91.4–106.7 cm)

• >42 in (>106.7 cm)

Thirty to sixty randomly selected trees of each species were selected to survey, along with an equal 

number of alternative trees. Tree measurements included DBH (to nearest 0.1 cm by sonar measuring 

device), tree crown and crown base (to nearest 0.5 

m by altimeter), crown diameter in two directions 

(parallel and perpendicular to nearest street to nearest 0.5 m by sonar measuring device), tree condition and location. Replacement trees were sampled 

when trees from the original sample population 

could not be located. Tree age was determined by 

municipal tree managers. Fieldwork was conducted 

in August and September 2005. 

Crown volume and leaf area were estimated from 

computer processing of tree crown images obtained using a digital camera. The method has 

shown greater accuracy than other techniques 

(±25% of actual leaf area) in estimating crown vol-
48

ume and leaf area of open-grown trees (Peper and 

McPherson 2003).

Linear and non-linear regression was used to fit 

predictive models—with DBH as a function of 

age—for each of the 20 sampled species. Predictions of leaf surface area (LSA), crown diameter, 

and height metrics were modeled as a function of 

DBH using best-fit models (Peper et al. 2003). 

Replacement Value

The monetary worth, or value, of a tree is based 

on people’s perception of it (Cullen 2000). There 

are several approaches that arborists use to develop 

a fair and reasonable perception of value (CTLA 

1992, Watson 2002). The cost approach is widely 

used today and assumes that the cost of production 

equals value (Cullen 2002).

The trunk formula method (CTLA 1992), also 

called depreciated replacement cost, is a common 

approach for estimating tree value in terms of cost. 

It assumes that the benefits inherent in a tree are 

reproduced by replacing the tree, and therefore, replacement cost is an indication of value. Replacement cost is depreciated to reflect differences in 

the benefits that would flow from an “idealized” 

replacement compared to the imperfect appraised 

tree. 

We regard the terms “replacement value” and “replacement cost” as synonymous indicators of the 

urban forest’s value. Replacement value is indicated by the cost of replacing existing trees with 

trees of similar size, species, and condition if all 

were destroyed, for example, by a catastrophic 

storm. Replacement cost should be distinguished 

from the value of annual benefits produced by the 

urban forest. The latter is a “snapshot” of benefits 

during one year, while the former accounts for the 

long-term investment in trees now reflected in their 

number, stature, placement, and condition. Hence, 

the replacement value of a street tree population 

is many times greater than the value of the annual 

benefits it produces.

The trunk formula method uses tree size, species, 

condition, and location factors to determine tree 

replacement value. Tree size is measured as trunk 

area (TA, cross-sectional area of the trunk based 

on DBH), while the other factors are assessed subjectively relative to a “high-quality” specimen and 

expressed as percentages. The trunk formula is

Replacement value = Basic value × Condition% 

 × Location%

Basic value = Replacement cost + (Basic price 

 × [TAA−TAR] × Species%)

where

Condition% = Rating of structural integrity and 

health; a higher percentage indicates better condition (CTLA 1992).

Location% = Rating of the site itself (relative market value), contribution of the tree in terms of its 

aesthetic and functional attributes, and placement, 

which reflects the effectiveness of realizing benefits; location is the sum of site, contribution, and 

placement divided by three (CTLA 1992). A higher 

percentage indicates better location.

Replacement cost = Sum of the cost of the replacement tree (of size TAR) and its installation.

Basic price = Cost of the largest available transplantable tree divided by TAR ($/in2

).

TAA = Trunk area of appraised tree (in2) or height of 

clear trunk (linear ft) for palms.

TAR = Trunk area of replacement tree (in2) or height 

of clear trunk (linear ft) for palms.

Species% = Rating of the species’s longevity, maintenance requirements, and adaptability to the local 

growing environment (CTLA 1992).

In this study, data from the “2006 Draft Species 

Evaluation Guide” were used to calculate replacement value (Pacific Northwest ISA Chapter 2006). 

Species rating percentages were the midpoint for 

the ranges reported. Tree condition ratings were 

based on the inventory (or set at 70% when no data 
49

were available) and location ratings were arbitrarily set at 70%, indicative of a tree located in a typical park. TAR is 7.065 inch2

 for a 3-inch caliper tree 

representing the largest tree that is normally available from wholesalers; TAA is calculated using the 

midpoint for each DBH class. The basic price was 

$66/in2 TA, based on the wholesale cost of a 3-inch 

caliper tree. Replacement costs equaled the cost for 

a 4-inch tree plus installation.

Replacement values were calculated using the trunk 

formula equation for each species by DBH class, 

then summed across DBH classes and species to 

derive total replacement value for the population. 

Identifying and Calculating Benefits

Annual benefits for Boise’s municipal trees were 

estimated for the fiscal year 2005. Growth rate 

modeling information was used to perform computer-simulated growth of the existing tree population for one year and account for the associated 

annual benefits. This “snapshot” analysis assumed 

that no trees were added to, or removed from, the 

existing population during the year. (Calculations 

of CO2 released due to decomposition of wood 

from removed trees did consider average annual 

mortality.) This approach directly connects benefits with tree-size variables such as DBH and LSA. 

Many functional benefits of trees are related to processes that involve interactions between leaves and 

the atmosphere (e.g., interception, transpiration, 

photosynthesis); therefore, benefits increase as tree 

canopy cover and leaf surface area increase.

For each of the modeled benefits, an annual resource unit was determined on a per-tree basis. 

Resource units are measured as MWh of electricity saved per tree; MBtu of natural gas conserved 

per tree; lbs of atmospheric CO2 reduced per tree; 

lbs of NO2, PM10, and VOCs reduced per tree; cubic feet of stormwater runoff reduced per tree; and 

square feet of leaf area added per tree to increase 

property values.

Prices were assigned to each resource unit (e.g., 

heating/cooling energy savings, air-pollution absorption, stormwater runoff reduction) using economic indicators of society’s willingness to pay for 

the environmental benefits trees provide. Estimates 

of benefits are initial approximations as some 

benefits are difficult to quantify (e.g., impacts on 

psychological health, crime, and violence). In addition, limited knowledge about the physical processes at work and their interactions makes estimates imprecise (e.g., fate of air pollutants trapped 

by trees and then washed to the ground by rainfall). 

Therefore, this method of quantification provides 

first-order approximations. It is meant to be a general accounting of the benefits produced by urban 

trees—an accounting with an accepted degree of 

uncertainty that can, nonetheless, provide a science-based platform for decision-making.

Energy Savings

Buildings and paving, along with little tree canopy 

cover and soil cover, increase the ambient temperatures within a city. Research shows that even 

in temperate climate zones temperatures in urban 

centers are steadily increasing by approximately 

0.5°F per decade. Winter benefits of this warming 

do not compensate for the detrimental effects of 

increased summertime temperatures. Because the 

electricity demand of cities increases about 1–2% 

per 1°F increase in temperature, approximately 

3–8% of the current electric demand for cooling is 

used to compensate for this urban heat island effect 

(Akbari et al. 1992). 

Warmer temperatures in cities have other implications. Increases in CO2

 emissions from fossil-fuel 

power plants, increased municipal water demand, 

unhealthy ozone levels, and human discomfort and 

disease are all symptoms associated with urban 

heat islands. In Boise, there are opportunities to 

ameliorate the problems associated with hardscape 

through strategic tree planting and stewardship 

of existing trees thereby creating street and park 

landscapes that reduce stormwater runoff, conserve 

energy and water, sequester CO2, attract wildlife, 

and provide other aesthetic, social, and economic 

benefits.
50

For individual buildings, street trees can increase 

energy efficiency in summer and increase or decrease energy efficiency in winter, depending on 

their location. During the summer, the sun is low in 

the eastern and western sky for several hours each 

day. Tree shade to protect east—and especially 

west—walls helps keep buildings cool. In the winter, allowing the sun to strike the southern side of 

buildings can warm interior spaces. 

Trees reduce air movement into buildings and conductive heat loss from buildings. The rates that outside air moves into a building can increase substantially with wind speed. In cold, windy weather, the 

entire volume of air, even in newer or tightly sealed 

homes, may change every two to three hours. Trees 

can reduce wind speed and resulting air infiltration by up to 50%, translating into potential annual 

heating savings of 25% (Heisler 1986). Decreasing 

wind speed reduces heat transfer through conductive materials as well. Cool winter winds, blowing 

against single-pane windows, can contribute significantly to the heating load of homes and buildings

Calculating Electricity and Natural Gas 

Benefits

Calculations of annual building energy use per 

residential unit (unit energy consumption [UEC]) 

were based on computer simulations that incorporated building, climate, and shading effects, following methods outlined by McPherson and Simpson (1999). Changes in UECs due to the effects of 

trees (ΔUECs) were calculated on a per-tree basis 

by comparing results before and after adding trees. 

Building characteristics (e.g., cooling and heating 

equipment saturations, floor area, number of stories, insulation, window area, etc.) are differentiated by a building’s vintage, or age of construction: 

pre-1950, 1950­­–1980, and post-1980. For example, 

all houses from 1950–1980 vintage are assumed to 

have the same floor area, and other construction 

characteristics. Shading effects for each of the 19 

tree species were simulated at three tree-to-building distances, for eight orientations and for nine 

tree sizes. 

The shading coefficients of the trees in leaf (gaps 

in the crown as a percentage of total crown silhouette) were estimated using a photographic method 

that has been shown to produce good estimates 

(Wilkinson 1991). Crown areas were obtained using the method of Peper and McPherson (2003) 

from digital photographs of trees from which background features were digitally removed. Values for 

tree species that were not sampled, and leaf-off values for use in calculating winter shade, were based 

on published values where available (McPherson 

1984; Hammond et al. 1980). Where published 

values were not available, visual densities were assigned based on taxonomic considerations (trees 

of the same genus were assigned the same value) 

or observed similarity to known species. Foliation 

periods for deciduous trees were obtained from the 

literature (McPherson 1984; Hammond et al. 1980) 

and adjusted for Boise’s climate based on consultation with foresters (Jorgenson 2006).

Average energy savings per tree were calculated 

as a function of distance and direction using tree 

location distribution data specific to Boise (i.e., frequency of trees located at different distances from 

buildings [setbacks] and tree orientation with respect to buildings). Setbacks were assigned to four 

distance classes: 0–20 ft, 20–40 ft, 40–60 ft and 

>60 ft. It was assumed that street trees within 60 

ft of buildings provided direct shade on walls and 

windows. Savings per tree at each location were 

multiplied by tree distribution to determine location-weighted savings per tree for each species 

and DBH class, independent of location. Location-weighted savings per tree were multiplied by 

the number of trees of each species and DBH class 

and then summed to find total savings for the city. 

Tree locations were based on the stratified random 

sample conducted in summer 2005.

Land use (single-family residential, multifamily 

residential, commercial/industrial, other) for rightof-way trees was based on the same tree sample. 

A constant tree distribution was used for all land 

uses. 
51

Three prototype buildings were used in the simulations to represent pre-1950, 1950–1980, and post1980 construction practices for Boise (Ritschard 

et al. 1992). Building footprints were modeled as 

square, which was found to be reflective of average 

impacts for a large number of buildings (Simpson 

2002). Buildings were simulated with 1.5-ft overhangs. Blinds had a visual density of 37%, and were 

assumed to be closed when the air conditioner was 

operating. Thermostat settings were 78°F for cooling and 68°F for heating, with a 60°F night setback 

in winter. Unit energy consumptions were adjusted 

to account for equipment saturations (percentage 

of structures with different types of heating and 

cooling equipment such as central air conditioners, 

room air conditioners, and evaporative coolers) 

(Table C1). 

Weather data for a typical meteorological year 

(TMY2) from Boise were used National Solar Radiation Data Base 2006). Dollar values for energy 

savings were based on electricity and natural gas 

prices of $0.061/kWh (Idaho Power 2006 ) and 

$1.14245/therm (Intermountain Gas 2006), respectively.

Single-Family Residence Adjustments

Unit energy consumptions for simulated singlefamily residences were adjusted for type and saturation of heating and cooling equipment, and for 

various factors (F) that modify the effects of shade 

and climate on heating and cooling loads:

ΔUECx=ΔUECsh

SFD × Fsh +ΔUECclSFD × Fcl 

 Equation 1

where

Fsh = Fequipment × APSF × Fadjacent shade × Fmultiple tree 

Fcl = Fequipment × PCF

Fequipment = SatCAC + Satwindow × 0.25 + Satevap × (0.33 

for cooling and 1.0 for heating).

Changes in energy use for higher density residential and commercial structures were calculated 

from single-family residential results adjusted by 

average potential shade factors (APSF) and potential climate factors (PCF); values were set to 1.0 for 

single-family residential buildings.

Total change in energy use for a particular land use 

was found by multiplying the change in UEC per 

tree by the number of trees (N):

Total change = N ×ΔUECx Equation 2

Subscript x refers to residential structures with 1, 

2–4 or ≥5 units, SFD to simulated single-family 

detached structures, sh to shade, and cl to climate 

effects. 

Estimated shade savings for all residential structures were adjusted to account for shading of 

neighboring buildings and for overlapping shade 

from trees adjacent to one another. Homes adjacent 

to those with shade trees may benefit from the trees 

on the neighboring properties. For example, 23% 

of the trees planted for the Sacramento Shade program shaded neighboring homes, resulting in an additional estimated energy savings equal to 15% of 

that found for program participants; this value was 

used here (Fadjacent shade = 1.15). In addition, shade 

from multiple trees may overlap, resulting in less 

building shade from an added tree than would result if there were no existing trees. Simpson (2002) 

estimated that the fractional reductions in average cooling and heating energy use were approximately 6% and 5% percent per tree, respectively, 

for each tree added after the first. Simpson (1998) 

also found an average of 2.5–3.4 existing trees per 

residence in Sacramento. A multiple tree reduction 

factor of 85% was used here, equivalent to approximately three existing trees per residence.

In addition to localized shade effects, which were 

assumed to accrue only to street trees within 18–60 

ft of buildings, lowered air temperatures and wind 

speeds due to neighborhood tree cover (referred to 

as climate effects) produce a net decrease in demand for summer cooling and winter heating. Reduced wind speeds by themselves may increase or 

decrease cooling demand, depending on the circumstances. To estimate climate effects on energy use, 

air-temperature and wind-speed reductions were 
52

estimated as a function of neighborhood canopy 

cover from published values following McPherson 

and Simpson (1999), then used as input for the 

building-energy-use simulations described earlier. 

Peak summer air temperatures were assumed to be 

reduced by 0.2°F for each percentage increase in 

canopy cover. Wind-speed reductions were based 

on the change in total tree plus building canopy 

cover resulting from the addition of the particular 

tree being simulated (Heisler 1990). A lot size of 

10,000 ft2 was assumed.

Cooling and heating effects were reduced based on 

the type and saturation of air conditioning (Table 

C2) or heating (Table C3) equipment by vintage. 

Equipment factors of 33 and 25% were assigned to 

homes with evaporative coolers and room air conditioners, respectively. These factors were combined 

with equipment saturations to account for reduced 

energy use and savings compared to those simulated for homes with central air conditioning (Fequipment). Building vintage distribution was combined 

with adjusted saturations to compute combined 

vintage/saturation factors for air conditioning (Table C2). Heating loads were converted to fuel use 

based on efficiencies in Table C2. The “other” and 

“fuel oil” heating equipment types were assumed 

to be natural gas for the purpose of this analysis. 

Building vintage distributions were combined with 

adjusted saturations to compute combined vintage/

saturation factors for natural gas and electric heating (Table C3). 

Multi-Family Residence Analysis

Unit energy consumptions (UECs) from single-family residential UECs were adjusted for multi-family residences (MFRs) to account for reduced shade 

resulting from common walls and multi-story construction. To do this, potential shade factors (PSFs) 

were calculated as ratios of exposed wall or roof 

(ceiling) surface area to total surface area, where 

total surface area includes common walls and ceilings between attached units in addition to exposed 

surfaces (Simpson 1998). A PSF of 1 indicates that 

all exterior walls and roofs are exposed and could 

be shaded by a tree, while a PSF of 0 indicates that 

no shading is possible (e.g., the common wall between duplex units). Potential shade factors were 

estimated separately for walls and roofs for both 

single- and multi-story structures. Average potential shade factors were 0.74 for multi-family residences of 2–4 units and 0.41 for ≥5 units.

Unit energy consumptions were also adjusted to 

account for the reduced sensitivity of multi-family 

buildings with common walls to outdoor temperature changes. Since estimates for these PCFs were 

unavailable for multi-family structures, a multifamily PCF value of 0.80 was selected (less than 

single-family detached PCF of 1.0 and greater than 

small commercial PCF of 0.40; see next section).

Commercial and Other Buildings

Reductions in unit energy consumptions for commercial/industrial (C/I) and industrial/transportation (I/T) land uses due to the presence of trees 

were determined in a manner similar to that used 

for multi-family land uses. Potential shade factors 

of 0.40 were assumed for small C/I, and 0.0 for 

large C/I. No energy impacts were ascribed to large 

C/I structures since they are expected to have surface-to-volume ratios an order of magnitude larger 

than smaller buildings and less extensive window 

area. Average potential shade factors for I/T structures were estimated to lie between these extremes; 

a value of 0.15 was used here. However, data relating I/T land use to building-space conditioning 

were not readily available, so no energy impacts 

were ascribed to I/T structures. A multiple-tree reduction factor of 0.85 was used, and no benefit was 

assigned for shading of buildings on adjacent lots. 

Potential climate-effect factors of 0.40, 0.25 and 

0.20 were used for small C/I, large C/I, and I/T, 

respectively. These values are based on estimates 

by Akbari (1992) and others who observed that 

commercial buildings are less sensitive to outdoor 

temperatures than houses.

The beneficial effects of shade on UECs tend to increase with conditioned floor area (CFA) for typi-
53

Single family 

detached

Mobile homes Single-family attached Multi-family 2-4 units Multi-family 5+ units Commercial/

industrial

Instit./

Transportation pre- 1950 1950- 1980 post- 1980 pre- 1950 1950- 1980 post- 1980 pre- 1950 1950- 1980 post- 1980 pre- 1950 1950- 1980 post- 1980 pre- 1950 1950- 1980 post- 1980 Small Large

Cooling equipment factors

Central air/

heat pump

100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100

Evaporative 

cooler

33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33

Wall/window unit 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25

None 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

Cooling saturations

Central air/

heat pump

13 35 69 13 35 69 13 35 69 13 35 69 13 35 69 86 86 86

Evaporative 

cooler

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

Wall/window unit 37 23 25 34 23 25 37 23 25 37 23 25 37 23 25 9 9 9

None 51 42 6 0 0 0 0 0 0 0 0 0 0 0 0 5 5 5

Adjusted cooling 

saturation

22 41 75 22 41 75 22 41 75 22 41 75 22 41 75 88 88 88

Table C1—Saturation adjustments for cooling (%)
54

Single family detached Mobile homes Single-family attached Multi-family 2-4 units Multi-family 5+ units Commercial/

industrial

Institutional/

Transportation

pre1950 1950- 1980 post- 1980 pre- 1950 1950- 1980 post- 1980 pre- 1950 1950- 1980 post- 1980 pre- 1950 1950- 1980 post- 1980 pre- 1950 1950- 1980 post- 1980 Small Large

Equipment efficiencies

AFUE 0.75 0.78 0.78 0.75 0.78 0.78 0.75 0.78 0.78 0.75 0.78 0.78 0.75 0.78 0.78 0.78 0.78 0.78

HSPF 6.8 6.8 8 6.8 6.8 8 6.8 6.8 8 6.8 6.8 8 6.8 6.8 8 8 8 8

HSPF 3.412 3.412 3.412 3.412 3.412 3.412 3.412 3.412 3.412 3.412 3.412 3.412 3.412 3.412 3.412 3.412 3.412 3.412

Electric heat saturations

Electric resistance 2.4 10.9 21.4 2.4 10.9 21.4 2.4 10.9 21.4 2.4 10.9 21.4 2.4 10.9 21.4 4.9 4.9 4.9

Heat pump 0.4 1.8 3.6 0.4 1.8 3.6 0.4 1.8 3.6 0.4 1.8 3.6 0.4 1.8 3.6 5.4 5.4 5.4

Adjusted electric heat saturations

0.4 1.7 2.9 0.4 1.7 2.9 0.4 1.7 2.9 0.4 1.7 2.9 0.4 1.7 2.9 1.7 1.7 1.7

Natural gas and other heating saturations

Natural gas 69.0 60.8 50.0 69.0 60.8 50.0 69.0 60.8 50.0 69.0 60.8 50.0 69.0 60.8 50.0 89.7 89.7 89.7

Oil 18.3 19.0 0.0 18.3 19.0 0.0 18.3 19.0 0.0 18.3 19.0 0.0 18.3 19.0 0.0 0.0 0.0 0.0

Other 9.9 7.6 25.0 9.9 7.6 25.0 9.9 7.6 25.0 9.9 7.6 25.0 9.9 7.6 25.0 0 0 0

NG heat saturations 97 87 75 97 87 75 97 87 75 97 87 75 97 87 75 90 90 90

Table C2—Saturation adjustments for heating (%, except AFUE [fraction] and HSPF [kBtu/kWh)
55

Single family detached Mobile homes Single-family attached Multi-family 2-4 units Multi-family 5+ units Commercial/ industrial Institutional/ Transportation

pre1950 1950- 1980 post- 1980 pre- 1950 1950- 1980 post- 1980 pre- 1950 1950- 1980 post- 1980 pre- 1950 1950- 1980 post- 1980 pre- 1950 1950- 1980 post- 1980 Small Large

Vintage distribution by building type

10.8 33.4 55.8 5.0 17.2 77.8 2.3 58.3 39.4 9.4 27.3 63.3 3.6 23.2 73.2 100 100 100

Tree distribution by vintage and building type

9.4 29.0 48.4 0.3 0.9 4.1 0.2 4.7 3.2 4.0 11.7 27.2 2.0 13.2 41.8 63.0 37.0 100

Combined vintage, equipment saturation factors for cooling

Cooling factor: shade 2.00 11.65 35.45 0.06 0.37 3.02 0.03 1.66 2.04 0.64 3.49 14.75 0.18 2.18 12.55 19.4 5.7 0.0

Cooling factor: climate

2.04 11.92 36.27 0.06 0.36 2.95 0.03 1.56 1.92 0.40 2.17 9.17 0.21 2.51 14.42 17.4 34.1 0.0

Combined vintage, equipment saturation for heating

Heating factor, natural gas: shade

8.89 24.73 35.45 0.25 0.78 3.02 0.15 3.52 2.04 2.84 7.41 14.75 0.80 4.63 12.55 19.7 5.8 0.0

Heating factor, electric: shade 0.03 0.47 1.37 0.00 0.01 0.12 0.00 0.07 0.08 0.01 0.14 0.57 0.00 0.09 0.49 0.38 0.11 0.0

Heating factor, natural gas: climate

9.09 25.30 36.27 0.14 0.44 1.69 0.17 3.90 2.27 1.68 4.38 8.72 0.97 5.64 15.27 68.0 133.1 0.0

Heating factor, electric: climate 0.03 0.48 1.40 0.00 0.01 0.07 0.00 0.07 0.09 0.01 0.08 0.34 0.00 0.11 0.59 1.30 2.55 0.0

Table C3—Building vintage distribution and combined vintage/saturation factors for heating and air conditioning
56

cal residential structures. As building surface area 

increases so does the area shaded. This occurs up 

to a certain point because the projected crown area 

of a mature tree (approximately 700–3,500 ft2) is 

often larger than the building surface areas being 

shaded. A point is reached, however, at which no 

additional area is shaded as surface area increases. 

At this point, ΔUECs will tend to level off as CFA 

increases. Since information on the precise relationships between change in UEC, CFA, and tree 

size is not available, it was conservatively assumed 

that ΔUECs in Equation 1 did not change for C/I 

and I/T land uses.

Atmospheric Carbon Dioxide Reduction

Sequestration (the net rate of CO2 storage in aboveand below-ground biomass over the course of one 

growing season) is calculated for each species using the tree-growth equations for DBH and height, 

described above, to calculate either tree volume or 

biomass. Equations from Pillsbury et al. (1998) are 

used when calculating volume. Fresh weight (kg/

m3) and specific gravity ratios from Alden (1995, 

1997) are then applied to convert volume to biomass. When volumetric equations for urban trees 

are unavailable, biomass equations derived from 

data collected in rural forests are applied (Tritton 

and Hornbeck 1982; Ter-Mikaelian and Korzukhin 

1997).

Carbon dioxide released through decomposition of 

dead woody biomass varies with characteristics of 

the wood itself, the fate of the wood (e.g., amount 

left standing, chipped, or burned), and local soil 

and climatic conditions. Recycling of urban waste 

is now prevalent, and we assume here that most material is chipped and applied as landscape mulch. 

Calculations were conservative because they assumed that dead trees are removed and mulched 

in the year that death occurs, and that 80% of their 

stored carbon is released to the atmosphere as CO2

in the same year. Total annual decomposition is 

based on the number of trees in each species and 

age class that die in a given year and their biomass. 

Tree survival rate is the principal factor influencing decomposition. Tree mortality for Boise was 

2.0% per year for the first five years after planting for street trees and 0.6% every year thereafter 

(Jorgenson 2006). Finally, CO2 released during tree 

maintenance was estimated to be 0.89 lb CO2 per 

inch DBH based on gasoline (~2,400 gal) and diesel fuel use (~8,000 gal) (Jorgenson 2006). 

Calculating Avoided CO2 Emissions 

Reducing building energy use reduces emissions of 

CO2. Emissions were calculated as the product of 

energy use and CO2 emission factors for electricity and heating. Heating fuel is largely natural gas 

and electricity in Boise. The fuel mix for electrical generation included coal (45.5%), hydroelectric 

(39.3%) and natural gas (14%) (U.S. EPA 2003). 

Emissions factors for electricity (lb/MWh) and natural gas (lb/MBtu) fuel mixes are given in Table C4. 

The monetary value of avoided CO2 was $6.68/ton 

based on the average value in Pearce (2003).

Improving Air Quality

Calculating Avoided Emissions 

Reductions in building energy use also result in 

reduced emissions of criteria air pollutants (those 

for which a national standard has been set by the 

EPA) from power plants and space-heating equipment. This analysis considered volatile organic hydrocarbons (VOCs) and nitrogen dioxide (NO2

)—

both precursors of ozone (O3) formation—as well 

Emission factor Implied 

valuec

($/lb) Electricity 

(lb/MWh)a

Natural gas 

(lb/MBtu)b

CO2 746 118 0.00334

NO2 1.443 0.1020 0.51

SO2 0.988 0.0006 0.06

PM10 0.500 0.0075 0.92

VOCs 0.460 0.0054 0.14

Table C4—Emissions factors and monetary implied values for CO2 and criteria air pollutants

a

USEPA 1998, 2003, except Ottinger et al. 1990 for VOCs

b

USEPA 1998

c

CO2 from Pearce (2003), values for all other pollutants are based 

on methods of Wang and Santini (1995) using emissions concentrations from U.S. EPA (2005) and population estimates from the U.S. 

Census Bureau (2003)
57

as sulfur dioxide (SO2) and particulate matter of 

<10 micron diameter (PM10). Changes in average 

annual emissions and their monetary values were 

calculated in the same way as for CO2, again using utility specific emission factors for electricity 

and heating fuels (US EPA 2003). The prices of 

emissions savings were derived from models that 

calculate the marginal cost of controlling different 

pollutants to meet air quality standards (Wang and 

Santini 1995). Emissions concentrations were obtained from the EPA (2005, Table C4), and population estimates from the US Census Bureau (2003).

Calculating Deposition and Interception 

Trees also remove pollutants from the atmosphere. 

The hourly pollutant dry deposition per tree is expressed as the product of the deposition velocity Vd 

=1/(Ra+Rb+Rc), pollutant concentration (C), canopy 

projection (CP) area, and time step. Hourly deposition velocities for each pollutant were calculated 

using estimates for the resistances Ra, Rb, and Rc

estimated for each hour over a year using formulations described by Scott et al. (1998). Hourly concentrations for NO2

, SO2, O3 and PM10 for Boise 

were obtained from the Environmental Protection 

Agency (EPA 2006 and hourly meteorological data 

(i.e., air temperature, wind speed, solar radiation) 

were obtained from the Pacific Northwest Cooperative Agricultural Weather Network (U.S Department of Interior 2006). The year 2003 was chosen 

because data were available and it closely approximated long-term, regional climate records.

Deposition was determined for deciduous species 

only when trees were in-leaf. A 50% re-suspension 

rate was applied to PM10 deposition. Methods described in the section “Calculating Avoided Emissions” were used to value emissions reductions; 

NO2 prices were used for ozone since ozone control measures typically aim at reducing NO2

. 

Calculating BVOC Emissions 

Emissions of biogenic volatile organic carbon 

(sometimes called biogenic hydrocarbons or 

BVOCs) associated with increased ozone formation 

were estimated for the tree canopy using methods 

described by Scott et al. (1998). In this approach, 

the hourly emissions of carbon in the form of isoprene and monoterpene are expressed as products 

of base emission factors and leaf biomass factors 

adjusted for sunlight and temperature (isoprene) or 

simply temperature (monoterpene). Annual dry foliar biomass was derived from field data collected 

in Boise during August 2006. The amount of foliar biomass present for each year of the simulated 

tree’s life was unique for each species. Hourly air 

temperature and solar radiation data for 2003 described in the pollutant uptake section were used as 

model inputs. Hourly emissions were summed to 

get annual totals (Table C4).

The ozone-reduction benefit from lowering summertime air temperatures, thereby reducing hydrocarbon emissions from biogenic sources, was 

estimated as a function of canopy cover following 

McPherson and Simpson (1999). Peak summer air 

temperatures were reduced by 0.2°F for each percentage increase in canopy cover. Hourly changes 

in air temperature were calculated by reducing this 

peak air temperature at every hour based on the 

hourly maximum and minimum temperature for 

that day, the maximum and minimum values of total global solar radiation for the year. 

Simulation results from Los Angeles indicate that 

ozone reduction benefits of tree planting with 

“low-emitting” species exceeded costs associated 

with their BVOC emissions (Taha 1996). This is a 

conservative approach, since the benefit associated 

with lowered summertime air temperatures and the 

resulting reduced hydrocarbon emissions from anthropogenic sources were not accounted for.

Reducing Stormwater Runoff

The social benefits that result from reduced peak 

runoff include reduced property damage from 

flooding and reduced loss of soil and habitat due 

to erosion and sediment flow. Reduced runoff also 

results in improved water quality in streams, lakes, 

and rivers. This can translate into improved aquatic 
58

habitats, less human disease and illness due to contact with contaminated water and reduced stormwater treatment costs. 

Calculating Stormwater Runoff Reductions

A numerical simulation model was used to estimate 

annual rainfall interception (Xiao et al. 1998). The 

interception model accounts for rainwater intercepted by the tree, as well as throughfall and stem 

flow. Intercepted water is stored on canopy leaf and 

bark surfaces. Once the storage capacity of the tree 

canopy is exceeded, rainwater temporarily stored 

on the tree surface will drip from the leaf surface 

and flow down the stem surface to the ground. 

Some of the stored water will evaporate. Tree canopy parameters related to stormwater runoff reductions include species, leaf and stem surface area, 

shade coefficient (visual density of the crown), tree 

height, crown diameter, and foliation period. Wind 

speeds were estimated for different heights above 

the ground; from this, rates of evaporation were estimated.

The volume of water stored in the tree crown was 

calculated from crown-projection area (area under tree dripline), leaf area indices (LAI, the ratio 

of leaf surface area to crown projection area), the 

depth of water captured by the canopy surface, 

and the water storage capacity of the tree crown. 

Tree surface saturation was 0.04 in. Species-specific shading coefficient, foliation period, and tree 

surface saturation storage capacity influence the 

amount of projected throughfall. 

Hourly meteorological and rainfall data for 2004 at 

the AgriMet Station (BOII) (The Pacific Northwest 

Cooperative Agricultural Weather Network, station‘s latitude: 43° 36’ 01” N, longitude: 116° 10’ 

37” W, elevation: 2,720 feet) in Boise, Idaho, were 

used in this simulation. The year 2004 was chosen 

because it most closely approximated the long time 

average rainfall of 12.3 in (312.4 mm). Annual precipitation at BOII during 2004 was 16.4 in (417.5 

mm). Storm events less than 0.1 in (2.5 mm) were 

assumed not to produce runoff and were dropped 

from the analysis. More complete descriptions of 

the interception model can be found in Xiao et al. 

(1998, 2000).

According to Johanna Bell, PE, Stormwater Program Coordinator, the City of Boise spends approximately $1.97 million annually on operations, 

maintenance, and improvements to its stormwater 

management system (Bell 2006). To calculate annual runoff we assigned curve numbers for each 

land use (USDA SCS 1986). Land use percentages 

were obtained from the city GIS database (Wing 

2006). We calculated runoff depth for each land use 

and found the citywide total to be 1,138 acre-feet. 

Given Boise’s area of 68.1 sq miles (176.4 km2), 

the total annual runoff was 419.9 million gallons 

(1,589,486.18 m3). The annual stormwater control 

cost ($1.97 million ÷ 419.9 million gal) was estimated to be $0.005 per gallon of runoff. 

Property Value and Other Benefits

Trees provide a host of aesthetic, social, economic, 

and health benefits that should be included in any 

benefit–cost analysis. One of the most frequently 

cited reasons for planting trees is beautification. 

Trees add color, texture, line, and form to the landscape softening the hard geometry that dominates 

built environments. Research on the aesthetic quality of residential streets has shown that street trees 

are the single strongest positive influence on scenic 

quality (Schroeder and Cannon 1983). Consumer 

surveys have shown that preference ratings increase with the presence of trees in the commercial streetscape. In contrast to areas without trees, 

shoppers indicated that they shopped more often 

and longer in well-landscaped business districts, 

and were willing to pay more for goods and services (Wolf 1999). Research in public-housing complexes found that outdoor spaces with trees were 

used significantly more often than spaces without 

trees. By facilitating interactions among residents, 

trees can contribute to reduced levels of violence, 

as well as foster safer and more sociable neighborhood environments (Sullivan and Kuo 1996).

Well-maintained trees increase the “curb appeal” 
59

of properties. Research comparing sales prices of 

residential properties with different numbers and 

sizes of trees suggests that people are willing to 

pay 3–7% more for properties with ample trees 

versus few or no trees. One of the most comprehensive studies on the influence of trees on residential property values was based on actual sales 

prices and found that each large front-yard tree was 

associated with about a 1% increase in sales price 

(Anderson and Cordell 1988). Depending on average home sale prices, the value of this benefit can 

contribute significantly to property tax revenues.

Scientific studies confirm our intuition that trees 

in cities provide social and psychological benefits. 

Humans derive substantial pleasure from trees, 

whether it is inspiration from their beauty, a spiritual connection, or a sense of meaning (Dwyer et 

al. 1992; Lewis 1996). Following natural disasters, 

people often report a sense of loss if the urban forest in their community has been damaged (Hull 

1992). Views of trees and nature from homes and 

offices provide restorative experiences that ease 

mental fatigue and help people to concentrate (Kaplan and Kaplan 1989). Desk-workers with a view 

of nature report lower rates of sickness and greater 

satisfaction with their jobs compared to those having no visual connection to nature (Kaplan 1992). 

Trees provide important settings for recreation and 

relaxation in and near cities. The act of planting 

trees can have social value, for community bonds 

between people and local groups often result.

The presence of trees in cities provides public 

health benefits and improves the well being of 

those who live, work and play in cities. Physical 

and emotional stress has both short-term and longterm effects. Prolonged stress can compromise the 

human immune system. A series of studies on human stress caused by general urban conditions and 

city driving showed that views of nature reduce the 

stress response of both body and mind (Parsons et 

al. 1998). City nature also appears to have an “immunization effect,” in that people show less stress 

response if they have had a recent view of trees 

and vegetation. Hospitalized patients with views 

of nature and time spent outdoors need less medication, sleep better, have a better outlook, and recover quicker than patients without connections to 

nature (Ulrich 1985). Trees reduce exposure to ultraviolet light, thereby lowering the risk of harmful 

effects from skin cancer and cataracts (Tretheway 

and Manthe 1999).

Certain environmental benefits from trees are 

more difficult to quantify than those previously 

described, but can be just as important. Noise can 

reach unhealthy levels in cities. Trucks, trains, and 

planes can produce noise that exceeds 100 decibels, 

twice the level at which noise becomes a health 

risk. Thick strips of vegetation in conjunction with 

landforms or solid barriers can reduce highway 

noise by 6–15 decibels. Plants absorb more high 

frequency noise than low frequency, which is advantageous to humans since higher frequencies are 

most distressing to people (Miller 1997). 

Urban forests can be oases, sometimes containing 

more vegetative diversity than surrounding rural 

areas. Numerous types of wildlife inhabit cities and 

are generally highly valued by residents. For example, older parks, cemeteries, and botanical gardens often contain a rich assemblage of wildlife. 

Street-tree corridors can connect a city to surrounding wetlands, parks, and other greenspace resources that provide habitats that conserve biodiversity 

(Platt et al.1994).

Urban and community forestry can provide jobs 

for both skilled and unskilled labor. Public service 

programs and grassroots-led urban and community 

forestry programs provide horticultural training to 

volunteers across the United States. Also, urban and 

community forestry provides educational opportunities for residents who want to learn about nature 

through first-hand experience (McPherson and 

Mathis 1999). Local nonprofit tree groups, along 

with municipal volunteer programs, often provide 

educational material, work with area schools, and 

offer hands-on training in the care of trees.
60

Calculating Changes in Property Values 

and Other Benefits 

In an Athens, GA, study (Anderson and Cordell 

1988), a large front-yard tree was found to be associated with a 0.88% increase in average home 

resale values. In our study, the annual increase in 

leaf surface area of a typical mature large tree (30-

year-old white ash, average leaf surface area 5,179 

ft2) was the basis for valuing the capacity of trees to 

increase property value. 

Assuming the 0.88% increase in property value 

held true for the city of Boise, each large tree 

would be worth $1,329 based on the 3rd quarter, 

2005, median single-family-home resale price in 

Boise ($151,000) (National Association of Realtors 2006). However, not all trees are as effective 

as front-yard trees in increasing property values. 

For example, trees adjacent to multifamily housing units will not increase the property value at the 

same rate as trees in front of single-family homes. 

Therefore, a citywide reduction factor (0.93) was 

applied to prorate trees’ value based on the assumption that trees adjacent to different land uses make 

different contributions to property sales prices. For 

this analysis, the reduction factor reflects the distribution of municipal trees in Boise by land use. The 

overall reduction factor for street trees reflects tree 

distribution by land use. Reduction factors were 

single-home residential (100%), multi-home residential (75%), small commercial (66%), industrial/

institutional/large commercial (50%), vacant/other 

(50%) (McPherson et al. 2001). Trees in parks were 

assigned a reduction factor of 0.50.

Estimating Magnitude of Benefits

Resource units describe the absolute value of the 

benefits of Boise’s street trees on a per-tree basis. 

They include kWh of electricity saved per tree, 

kBtu of natural gas conserved per tree, lbs of atmospheric CO2

 reduced per tree, lbs of NO2, PM10, and 

VOCs reduced per tree, cubic feet of stormwater 

runoff reduced per tree, and square feet of leaf area 

added per tree to increase property values. A dollar 

value was assigned to each resource unit based on 

local costs.

Estimating the magnitude of the resource units produced by all street and park trees in Boise required 

four steps: (1) categorizing street trees by species 

and DBH based on the city’s street-tree inventory, 

(2) matching other significant species with those 

that were modeled, (3) grouping the remaining 

“other” trees by type, and (4) applying resource 

units to each tree.

Categorizing Trees by DBH Class 

The first step in accomplishing this task involved 

categorizing the total number of street trees by relative age (as a function of DBH class). The inventory was used to group trees into the DBH classes 

described at the beginning of this chapter. 

Next, the median value for each DBH class was 

determined and subsequently used as a single value 

to represent all trees in each class. For each DBH 

value and species, resource units were estimated 

using linear interpolation. 

Applying Resource Units to Each Tree

The interpolated resource-unit values were used to 

calculate the total magnitude of benefits for each 

DBH class and species. For example, assume that 

there are 300 London planetrees citywide in the 

30–36 in DBH class. The interpolated electricity 

and natural gas resource unit values for the class 

midpoint (33 in) were 199.3 kWh and 6,487.9 kBtu 

per tree, respectively. Therefore, multiplying the 

resource units for the class by 300 trees equals the 

magnitude of annual heating and cooling benefits 

produced by this segment of the population: 59,790 

kWh of electricity saved and 1,946,370 kBtu of 

natural gas saved. 

Matching Significant Species 

with Modeled Species

To extrapolate from the 20 municipal species modeled for growth to the entire inventoried tree population, each species representing over 1% of the 
61

population was matched with the modeled species 

that it most closely resembled. Less abundant species that were not matched were then grouped into 

the “Other” categories described below. Grouping 

Remaining “Other” Trees by Type

The species that were less than 1% of the population were labeled “other” and were categorized according into classes based on tree type (one of four 

life forms and three mature sizes): 

• Broadleaf deciduous: large (BDL), medium 

(BDM), and small (BDS).

• Broadleaf evergreen: large (BEL), medium 

(BEM), and small (BES).

• Coniferous evergreen: large (CEL), medium 

(CEM), and small (CES).

• Palm: large (PEL), medium (PEM), and small 

(PES).

Large, medium, and small trees were >40 ft, 25–40 

ft, and <25 ft in mature height, respectively. A typical tree was chosen to represent each of the above 

15 categories to obtain growth curves for “other” 

trees falling into each of the categories:

BDL Other = Green ash (Fraxinus pennsylvanica)

BDM Other = Norway maple (Acer platanoides)

BDS Other = Crabapple (Malus spp.)

BEL Other = Not applicable

BEM Other = Not applicable

BES Other = American holly (Ilex opaca)

CEL Other = Blue spruce (Picea pungens)

CEM Other = Scotch pine (Pinus sylvestris)

CES Other = Pinyon pine (Pinus edulis)

PEL Other = Not applicable

PEM Other = Not applicable 

PES Other = Not applicable

When local data were not measured for certain categories (e.g., CES), growth data from similar-sized 

species in a different region were used.

Calculating Net Benefits 

And Benefit–Cost Ratio

It is impossible to quantify all the benefits and 

costs produced by trees. For example, owners of 

property with large street trees can receive benefits from increased property values, but they may 

also benefit directly from improved health (e.g., 

reduced exposure to cancer-causing UV radiation) 

and greater psychological well-being through visual and direct contact with trees. On the cost side, increased health-care costs may be incurred because 

of nearby trees, due to allergies and respiratory ailments related to pollen. The values of many of these 

benefits and costs are difficult to determine. We 

assume that some of these intangible benefits and 

costs are reflected in what we term “property value 

and other benefits.” Other types of benefits we can 

only describe, such as the social, educational, and 

employment/training benefits associated with the 

city’s street tree resource. To some extent connecting people with their city trees reduces costs for 

health care, welfare, crime prevention, and other 

social service programs. 

Boise residents can obtain additional economic 

benefits from street trees depending on tree location and condition. For example, street trees can 

provide energy savings by lowering wind velocities and subsequent building infiltration, thereby 

reducing heating costs. This benefit can extend to 

the neighborhood, as the aggregate effect of many 

street trees reduces wind speed and reduces citywide winter energy use. Neighborhood property 

values can be influenced by the extent of tree canopy cover on streets. The community benefits from 

cleaner air and water. Reductions in atmospheric 

CO2 concentrations due to trees can have global 

benefits.

To assess the total value of annual benefits (B) for 

each park and street tree (i) in each management 
62

area (j) benefits were summed:

Equation 3

where

e = price of net annual energy savings = annual 

natural gas savings + annual electricity savings

a = price of annual net air quality improvement = 

PM10 interception + NO2 and O3 absorption + 

avoided power plant emissions – BVOC emissions

c = price of annual carbon dioxide reductions = 

CO2 sequestered – releases + CO2 avoided from 

reduced energy use

h = price of annual stormwater runoff reductions = 

effective rainfall interception

p = price of aesthetics = annual increase in property 

value 

Total net expenditures were calculated based on all 

identifiable internal and external costs associated 

with the annual management of municipal trees 

citywide (Koch 2004). Annual costs for the municipality (C) were summed:

C = p + t + r + d + e + s + cl + l + a + q

p = annual planting expenditure

t = annual pruning expenditure

r = annual tree and stump removal and disposal expenditure

d = annual pest and disease control expenditure

e = annual establishment/irrigation expenditure

s = annual price of repair/mitigation of infrastructure damage

cl = annual price of litter/storm clean-up

l = average annual litigation and settlements 

expenditures due to tree-related claims

a = annual expenditure for program administration 

q = annual expenditures for inspection/answer 

service requests 

Total citywide annual net benefits as well as the 

benefit–cost ratio (BCR) were calculated using the 

sums of benefits and costs: 

Citywide Net Benefits = B – C Equation 4

BCR = B / C Equation 5

( )





 



 = ∑ ∑ ij + ij + ij + ij + ij

n n

B j i e a c h p

1 1
63

References

Akbari, H.; Davis, S.; Dorsano, S.; Huang, J.; Winnett, S., eds. 1992. Cooling Our Communities: A 

Guidebook on Tree Planting and Light-Colored 

Surfacing. Washington, DC: U.S. Environmental 

Protection Agency. 26 p.

Alden, H.A. 1995. Hardwoods of North America. 

General Technical Report No. 83. Madison, WI: 

U.S. Department of Agriculture, Forest Service.

Alden, H.A. 1997. Softwoods of North America. General Technical Report No. 102. Madison, WI: U.S. 

Department of Agriculture, Forest Service. 151 p.

Anderson, L.M.; Cordell, H.K. 1988. Residential 

property values improve by landscaping with trees.

Southern Journal of Applied Forestry 9: 162–166.

Bell, J. 2006. Personal communication. PE, City of Boise Stormwater Program, Boise, ID.

Benjamin, M.T.; Winer, A.J. 1998. Estimating the 

ozone-forming potential of urban trees and shrubs. 

Atmospheric Environment. 32: 53–68

Boise Parks & Recreation Dept. Urban Forestry. 

1995. Tree selection guide for street and landscapes 

throughout Idaho. Boise: Parks & Recreation Dept., 

Urban Forestry Division. 51 p.

Boise Parks & Recreation Dept. Community Forestry Unit. 2003. Tree statistics report for Boise, 

Idaho. Boise: Parks & Recreation Dept., Urban Forestry Division. 

Chandler, T.J. 1965. The Climate of London. London: 

Hutchinson.

Clark, J.R.; Matheny, N.P.; Cross, G.; Wake, V. 1997. 

A model of urban forest sustainability. Journal of 

Arboriculture. 23(1): 17–30.

Council of Landscape & Tree Appraisers [CTLA]. 

1992. Guide for plant appraisal, 8th ed. Champaign, 

IL: International Society of Arboriculture. 103 p. 

Cullen, S. 2000. Tree appraisal: what is the trunk formula method (9th edn.)? Arboricultural Consultant. 

30(5): 3. 

Cullen, S. 2002. Tree appraisal: Can depreciation factors be rated greater than 100%? Journal of Arboriculture. 28(3): 153–158.

Dwyer, J.F.; McPherson, E.G.; Schroeder, H.W.; 

Rowntree., R.A. 1992. Assessing the benefits and 

costs of the urban forest. Journal of Arboriculture 

18(5): 227–234.

Dwyer, J.F.; Nowak, D.J.; Noble, M.H.; Sisinni, S.M. 

2000. Connecting people with ecosystems in the 

21st century: an assessment of our nation’s urban 

forests. General Technical Report No. 490. Portland, OR: U.S. Department of Agriculture, Forest 

Service, PNW Research Station.

Hammond J.; Zanetto, J.; Adams, C. 1980. Planning 

solar neighborhoods. Sacramento, CA: California 

Energy Commission. 179 p.

Heisler, G.M. 1986. Energy savings with trees. Journal 

of Arboriculture. 12(5): 113–125.

Heisler, G. M. 1990. Mean wind speed below building 

height in residential neighborhoods with different 

tree densities. ASHRAE Transactions. 96:1: 1389–

1396.

Hull, R.B. 1992. How the public values urban forests. 

Journal of Arboriculture. 18(2): 98–101.

Idaho Power. 2006. Tariffs: schedule 1 residential service. http://www.idahopower.com/aboutus/regulatoryinfo/tariffPdf.asp?id=156&.pdf. (15 March 

2006).

Intermountain Gas. 2006. Residential gas prices. 

http://www.intgas.com/aboutbilling/prices.html. 

(13 March 2006).

Jorgenson, Brian. 2006. Personal communication. Forestry Specialist, Community Forestry Unit, Parks & 

Recreation Department, City of Boise, Boise, ID.

Jorgenson, Brian. 2007. Forestry Specialist, Community Forestry Unit, Parks & Recreation Department, 

City of Boise, Boise, ID.
64

Kaplan, R. 1992. Urban forestry and the workplace. 

In: Gobster, P.H., ed. Managing urban and high-use 

recreation settings. Gen. Tech. Rep. NC-163. St. 

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

Service, North Central Research Station: 41–45.

Kaplan, R.; Kaplan, S. 1989. The experience of nature: 

a psychological perspective. Cambridge, United 

Kingdom: Cambridge University Press. 360 p.

Lewis, C.A. 1996. Green nature/human nature: the 

meaning of plants in our lives. Chicago: University 

of Illinois Press. 176 p.

Maco, S.E.; McPherson, E.G. 2003. A practical approach to assessing structure, function, and value of 

street tree populations in small communities. Journal of Arboriculture. 29(2): 84–97.

Maco, S.E., McPherson, E.G.; Simpson, J.R.; Peper, 

P.J.; Xiao., Q. 2005. City of Berkeley, California, 

Municipal Tree Resource Analysis. Internal Tech. 

Rep. Davis, CA: U.S. Department of Agriculture, 

Forest Service, Pacific Southwest Research Station, 

Center for Urban Forest Research. 

McPherson, E.G. 1984. Planting design for solar control. In: McPherson, E.G., ed. Energy-conserving 

site design. Washington, DC: American Society of 

Landscape Architects: 141–164. Chapter 8.

McPherson, E.G. 1993. Evaluating the cost effectiveness of shade trees for demand-side management. 

The Electricity Journal 6(9): 57–65.

McPherson, E.G.; Mathis, S., eds. 1999. Proceedings 

of the Best of the West summit. Sacramento, CA: 

Western Chapter, International Society of Arboriculture. 93 p.

McPherson, E.G.; Muchnick, J. 2005. Effects of tree 

shade on asphalt concrete pavement performance. 

Journal of Arboriculture. 31(6): 303–309.

McPherson, E.G.; Rowntree, R.A. 1989. Using structural measures to compare twenty-two U.S. street 

tree populations. Landscape Journal. 8:13–23. 

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, Pacific Southwest Research Station. 237 p.

McPherson, E.G.; Simpson, J.R.; Peper, P.J.; Xiao, 

Q. 2001. Tree Guidelines for Inland Empire Communities. Davis, CA: U.S. Department of Agriculture, Forest Service, Western Center for Urban Forest Research and Education.

McPherson, E.G., J.R. Simpson, Q. Xiao, P.J. Peper, 

S.E. Maco. 2003. Benefit–cost analysis of Fort Collins’ municipal forest. Internal Technical Report. 

Davis, CA: U.S. Department of Agriculture, Forest 

Service, Center for Urban Forest Research.

McPherson, E.G., Simpson, J.R.; Peper, P.J.; Maco, 

S.E.; Xiao., Q. 2005a. Municipal forest benefits 

and costs in five U.S. cities. Journal of Forestry 

103(8): 411–416.

McPherson, E.G.; Simpson, J.R.; Peper, P.J.; Maco, 

S.; Gardner, S.L.; Cozad, S.K.; Xiao, Q. 2005b. 

City of Minneapolis, MN, Municipal Tree Resource 

Analysis. Internal Technical Report. Davis, CA: 

U.S. Department of Agriculture, Forest Service, 

Center for Urban Forest Research.

McPherson, E.G.; Simpson, J.R.; Peper, P.J.; Maco, 

S.; Xiao, Q. 2005c. City of Glendale, AZ, municipal forest resource analysis. Internal Technical Report. Davis, CA: U.S. Department of Agriculture, 

Forest Service, Center for Urban Forest Research.

McPherson, E.G.; Simpson, J.R.; Peper, P.J.; Gardner, S.L.; Vargas, K.E.; Ho, J.; Maco, S.; Xiao, 

Q. 2005d. City of Charlotte, North Carolina, Municipal Forest Resource Analysis. Internal Technical Report. Davis, CA: U.S. Department of Agriculture, Forest Service, Center for Urban Forest 

Research.

McPherson, E.G.; Simpson, J.R.; Peper, P.J.; Gardner, S.L.; Vargas, K.E.; Ho, J.; Maco, S.; Xiao, 

Q. 2005e. City of Boulder, Colorado, municipal 

forest resource analysis. Internal Technical Report. 

Davis, CA: U.S. Department of Agriculture, Forest 

Service, Center for Urban Forest Research.
65

McPherson, E.G.; Simpson, J.R.; Peper, P.J.; Gardner, S.L.; Vargas, K.E.; Ho, J.; Maco, S., Xiao, Q. 

2006. City of Charleston, South Carolina, municipal 

tree resource analysis. Internal Technical Report. 

Davis, CA: U.S. Department of Agriculture, Forest 

Service, Center for Urban Forest Research.

Miller, R.W. 1997. Urban forestry: planning and managing urban greenspaces. 2d ed. Upper Saddle River, 

NJ: Prentice-Hall. 502 p.

Moll, G.; Kollin, C. 1993. A new way to see our city 

forests. American Forests 99(9-10): 29–31.

National Association of Realtors. 2005. 2005 Singlefamily housing prices. http http://www.realtor.

org/Research.nsf/files/REL05Q3T.pdf. (February 

2006).

National Solar Radiation Data Base (1961–1990). 

2006. Typical meterological year data. http://rredc.

nrel.gov/solar/old_data/nsrdb/tmy2/. (15 June 

2006). 

Pacific Northwest Chapter International Society of 

Arboriculture. 2006. 2006 Draft Species Evaluation Guide. http://www.pnwisa.org/DraftSpeciesRating06.xls. (10 Oct 2006).

Parsons, R.; Tassinary, L.G.; Ulrich, R.S.; Hebl, 

M.R.; Grossman-Alexander, M. 1998. The view 

from the road: implications for stress recovery and 

immunization. Journal of Environmental Psychology. 18(2): 113–140.

Pearce, D. 2003. The social cost of carbon and its policy 

implications. Oxford Review of Economic Policy. 

19(3): 362–384.

Peper, P.J.; McPherson, E.G. 2003. Evaluation of four 

methods for estimating leaf area of isolated trees. 

Urban Forestry and Urban Greening. 2(1): 19–29.

Peper, P.J., McPherson, E.G.; and Mori, S.M. 2003. 

Equations for predicting diameter, height, crown 

width, and leaf area of San Joaquin Valley street 

trees. Journal of Arboriculture (27(6): 306–317.

Peper, P.J., McPherson, E.G., Simpson, J.R.; Maco, 

S.E.; Xiao, Q. 2004a. City of Cheyenne, Wyoming 

Municipal Tree Resource Analysis. Internal Technical Report. Davis, CA: U.S. Department of Agriculture, Forest Service, Center for Urban Forest 

Research.

Peper, P.J., McPherson, E.G., Simpson, J.R.; Maco, 

S.E.; Xiao, Q. 2004b. City of Bismarck, North Dakota Municipal Tree Resource Analysis. Internal 

Technical Report. Davis, CA: U.S. Department of 

Agriculture, Forest Service, Center for Urban Forest Research.

Pillsbury, N.H.; Reimer, J.L.; Thompson, R.P. 1998.

Tree volume equations for fifteen urban species in 

California. Tech. Rep. 7. San Luis Obispo, CA: Urban Forest Ecosystems Institute, California Polytechnic State University. 56 p.

Platt, R.H.; Rowntree, R.A.; Muick, P.C., eds. 1994.

The ecological city. Boston, MA: University of 

Massachusetts. 292 p.

Richards, N.A. 1982/83. Diversity and stability in a 

street tree population. Urban Ecology 7: 159–171.

Ritschard, R.L., Hanford, J.W.; Sezgen, A.O. 1992. 

Single-family heating and cooling requirements: 

assumptions, methods, and summary results. Topical Report. LBL-30377. GRI-91/023697. Berkeley, 

CA: Lawrence Berkeley Laboratory. 97 p.

Schroeder, H.W.; Cannon, N. 1983. The esthetic contribution of trees to residential streets in Ohio towns. 

Journal of Arboriculture. 9: 237–243.

Scott, K.I.; McPherson, E.G.; Simpson, J.R. 1998.

Air pollutant uptake by Sacramento’s urban forest. 

Journal of Arboriculture 24(4): 224–234.

Scott, K.I.; Simpson, J.R.; McPherson, E.G. 1999. 

Effects of tree cover on parking lot microclimate 

and vehicle emissions. Journal of Arboriculture. 

25(3): 129–142.

Simpson, J.R. 1998. Urban forest impacts on regional 

space conditioning energy use: Sacramento County 

case study. Journal of Arboriculture 24(4): 201-

214.

Simpson, J.R. 2002. Improved estimates of tree shade 

effects on residential energy use. Energy and Buildings. 34(10): 173–182.
66

Sullivan, W.C.; Kuo, E.E. 1996. Do trees strengthen 

urban communities, reduce domestic violence? Arborist News. 5(2): 33–34.

Taha, H. 1996. Modeling impacts of increased urban 

vegetation on ozone air quality in the South Coast 

Air Basin. Atmospheric Environment 30: 3423–

3430.

Ter-Mikaelian, M.T.; Korzukhin, M.D. 1997. Biomass 

equations for sixty-five North American tree species. Forest Ecol ogy and Management 97: 1–24.

Thompson, R.P.; Ahern, J.J. 2000. The state of urban 

and community forestry in California. San Luis 

Obispo, CA: Urban Forest Ecosystems Institute, 

California Polytechnic State University. 48 p.

Tretheway, R.; Manthe, A. 1999. Skin cancer prevention: another good reason to plant trees. In: 

McPherson, E.G.; Mathis, S., eds. Proceedings of 

the Best of the West summit. Davis, CA: University 

of California: 72–75.

Tritton, L.M.; Hornbeck, J.W. 1982. Biomass equations for major tree species of the Northeast. Gen. 

Tech. Rep. No. 69. Broomall, PA: U.S. Department 

of Agriculture, Forest Service, Northeast. 46 p.

U.S. Census Bureau. 2003. 2003 Population Estimates, 

Census 2000, 1990. http://www.census.gov. (25 

July 2006).

U.S. Department of Agriculture, Soil Conservation 

Service. 1986. Urban hydrology for small watersheds. Technical Release No. 55, 2nd ed. Washington, D.C.: U.S. Department of Agriculture, Soil 

Conservation Service 

U.S. Department of Interior. 2006. Agrimet. Pacific 

Northwest Cooperative Agricultural Weather Network data. http://www.usbr.gov/pn/agrimet/agrimetmap/boiida.html. (1 August 2006).

U.S. Environmental Protection Agency. 1998. Ap-42 

compilation of air pollutant emission factors. 5th ed. 

Research Triangle Park, NC. Volume I. 

U.S. Environmental Protection Agency. 2003. EGRID 2002, v.2.01. http://www.epa.gov/cleanenergy/egrid/index.htm. (1 May 2006). 

U.S. Environmental Protection Agency. 2006. Air 

quality data. http://www.epa.gov/ttn/airs/airsaqs/

detaildata/datarequest.html. (1 June 2006).

Ulrich, R. S. 1985. Human responses to vegetation and 

landscapes. Landscape and Urban Planning. 13: 

29–44.

Wang, M.Q.; Santini, D.J. 1995. Monetary values of 

air pollutant emissions in various U.S. regions. 

Transportation Research Record 1475: 33–41.

Watson, G. 2002. Comparing formula methods of tree 

appraisal. Journal of Arboriculture. 28(1): 11–18.

Wing, Eric. 2006. Personal communication. GIS Analyst, Information and Technology Department, City 

of Boise, ID.

WC-ISA. 2004. Species Classification and Group Assignment. Cohasset, CA: Western Chapter, International Society of Arboriculture. 

Wilkinson, D. 1991. Can photographic methods be used 

for measuring the light attenuation characteristics 

of trees in leaf? Landscape and Urban Planning. 20: 

347–349.

Wolf, K.L. 1999. Nature and commerce: human ecology in business districts. In: Kollin, C., ed. Building cities of green: proceedings of the 1999 National Urban Forest Conference. Washington, D.C.: 

American Forests: 56–59.

Vargas, K.E., McPherson, E.G.; Simpson, J.R.; 

Peper, P.J.; Gardner, S.L.; Ho, J.; Xiao, Q. 2006.

City of Albuquerque, New Mexico, municipal forest 

resource analysis. Internal Tech. Rep. Davis, CA: 

U.S. Department of Agriculture, Forest Service.

Xiao, Q.; McPherson, E.G.; Simpson, J.R.; Ustin, 

S.L. 1998. Rainfall interception by Sacramento’s 

urban forest. Journal of Arboriculture. 24(4): 235–

244.

Xiao, Q.; McPherson, E.G.; Simpson, J.R.; Ustin, 

S.L. 2000. Winter rainfall interception by two mature open grown trees in Davis, California. Hydrological Processes. 14(4): 763–784.

Mission Statement

We conduct research that demonstrates new ways in which trees

add value to your community, converting results into financial terms

to assist you in stimulating more investment in trees.

Center for Urban Forest Research

Pacific Southwest Research Station, USDA Forest Service

One Shields Avenue, UC Davis, Davis, California 95616

(530) 752-7636 Fax (530) 752-6634 

http://www.fs.fed.us/psw/programs/cufr/

Areas of Research:

Investment Value

Energy Conservation

Air Quality

Water Quality

Firewise Landscapes