VARIABLE RANKINGS IN COST-BENEFITS FROM · PDF filefraction and modify the intensity of the UHI ... ten cities from replacing 100 km2 of conventional roof areas with green roofs in

2014

VARIABLE RANKINGS IN COST-BENEFITS FROM INSTALLING

GREEN ROOFS IN TEN CITIES IN THE UNITED STATES

PRACTICAL EXAM

Gon Park

Shippensburg University

College of Arts and Sciences

Department of Geography/Earth Science

M.S. Geoenvironmental Studies

i

TABLE OF CONTENTS

List of Tables .....................................................................................................................................................ii

List of Figures.................................................................................................................................................. ii

Abstract ............................................................................................................................................................ iii

Introduction ........................................................................................................................................................ 1

Literature Review ............................................................................................................................................. 2

Impervious Area .......................................................................................................................... 2

Green Roof Basics ...................................................................................................................... 2

Green Roof Benefits ................................................................................................................... 3

Net Present Value ........................................................................................................................ 5

Study Area .......................................................................................................................................................... 6

Data and Method ............................................................................................................................................... 7

Geographic Information System Analysis ............................................................................ 7

Cost-Benefits of Green Roofs................................................................................................... 8

Calculations for Total Cost-Benefits of Green Roofs ........................................................12

Results and Discussion ..................................................................................................................................14

Developed Area and City Density Rankings .......................................................................14

Cost-Benefits of Green Roofs in One Year ..........................................................................16

Cost-Benefits after 50 Years ...................................................................................................17

A Relationship between Population Density and Cost-Benefits of Green Roofs ........19

Population Demographics and City Structures to Install Green Roofs ..........................21

Overall Rankings of Ten Cites ..............................................................................................22

Existing Green Roofs in Ten Cities ......................................................................................23

Conclusion........................................................................................................................................................25

References ........................................................................................................................................................25

ii

LIST OF TABLES

Table 1. Life cycle costs of conventional roofs and green roofs during 90 years .............................. 4

Table 2. Electricity prices and annual electric costs for cooling energy of ten cities ........................ 8

Table 3. Annual electric savings by reducing the UHI with green roof installation ........................10

Table 4. Annual average precipitation of ten cities ................................................................................. 11

Table 5. Population demographics and city structures in ten cities ......................................................13

Table 6. The percentage of multifamily over 5 units and effective areas to install green roofs .....14

Table 7. Rankings of city density ................................................................................................................15

Table 8. Comparison of three rankings ......................................................................................................20

Table 9. Rankings of cost-benefits ..............................................................................................................23

Table 10. Rankings of areas of existing green roofs, potential cost-benefits, and income ..............24

LIST OF FIGURES

Figure 1. Locations of ten cities that were used for study areas in this study ...................................... 6

Figure 2. One year cost-benefits of ten cities............................................................................................16

Figure 3. The NPVs of total cost-benefits after 50 years .......................................................................18

Figure 4. The year assessment of the NPVs ..............................................................................................18

Figure 5. The comparison of one-year cost-benefits and total cost-benefits ......................................21

Figure 6. The NPVs of total cost-benefits of green roofs.......................................................................22

iii

Abstract

The impervious surface area associated with urbanization causes many negative effects on

environment, health of people, and biodiversity. Green roofs can resolve these problems by

replacing impervious areas with vegetation cover by providing many remarkable benefits such as

energy savings, reducing the urban heat island effect and stormwater runoff, and increasing air

quality. Thus, this study calculated variable cost-benefits of green roofs in the ten cities in the

United States and analyzed the trend of green roof installations and potential possibilities of the

ten cities to install green roofs.

Spatial analyses were used to calculate developed areas in ten cities. Cost-benefits of green

roofs from energy savings and reducing the urban heat island effect, stormwater runoff, and air

pollution, were calculated with considerations of electricity prices and the climate. The net present

value assessment based on a 50-year period was used to suggest investment decisions to install

green roofs. This study recalculated cost-benefits of green roofs with considerations of specific

characteristics of cities such as population size and density, population demographics, and city

structures to assess effects of these factors on cost-benefits of green roofs. With these results, this

study estimated and compared variable rankings of the ten cities. Lastly, the areas of existing green

roofs and the calculated cost-benefits were compared to analyze the trend of green roof installation

of the ten cities and to suggest which cities need to install green roofs. The results show that cost-

benefits of green roofs were affected by variable factors such as electricity price, climate

characteristics, population density, population demographics, and city structures. This study is an

important step in suggesting green roof installation in cities in the United States.

1

Introduction

Green roofs have been increasingly installed in many metropolitan cities across the United

States since the 1990s. Between 2004 and 2013, green roof areas increased approximately

20,000,000 ft2 in the United States (GRHC 2014). Green roofs provide the private benefits of

reduced energy consumption and an increase in roof life span. Additionally, they contribute to

public benefits by reducing stormwater runoff, greenhouse gas emissions, and the urban heat island

effect (Getter and Rowe 2006).

Using GIS, this study extracted developed areas in the United States and calculated cost-

benefits of green roofs in the identified ten cities. Cost-benefits of green roofs were calculated with

considerations of many factors: (1) electricity prices, (2) climate, (3) population size and density,

(4) population demographics such as income and age, (5) city structures upon which to install

green roofs, and (6) existing green roofs in the ten cities. Electricity prices were used to calculate

cost-benefits of green roofs from energy savings and the UHI reduction. Climate impacts were

considered to calculate cost-benefits of green roofs from reducing stormwater runoff and the UHI

effect. Population size and density, population demographics, and city structures were considered

to estimate appropriate and potential areas to install green roofs. Lastly, a comparison of the

calculated cost-benefits, existing green roofs, and incomes in the ten cities was used to assess the

trend of green roof installation and suggest which cities need more green roof installation than

other cities.

2

Literature Review

Impervious Area

The impervious surface area is recognized as the main indicator to assess urban

environments. Impervious surface area associated with urbanization causes many negative effects

on environment, health of people, and biodiversity. The urban heat island (UHI) is occurred by

non-evaporating impervious materials covering with an increase in heat flux in urban areas. Thus,

an increase in the UHI effects has been a major problem of many urban areas (Yuana and Bauerb

2007). Urbanization also threatens native species, causing the extinction of native species

(Mckinney 2002). However, tree cover in urban areas in the United States decreases at a rate of

7900 ha/year and 4.0 million trees per year. City tree cover is also reduced at a rate of 0.27

percent/year, while impervious area increases at a rate of 0.31 percent/year (Nowak and Greenfield

2012). Green roofs can resolve these problems by replacing impervious areas with vegetation

cover (Getter and Rowe 2006).

Green Roof Basics

Green roofs are vegetated systems that provide benefits of cool-roof technologies as well

as environmental benefits. They can be installed during the construction of new buildings or

retrofitted onto existing conventional roofs (Gedge and Kadas 2005). Green roofs are classified

into three types: intensive, extensive, and semi-extensive systems depending on the type of plants,

overall soil depth, the weight of soil, and regular human access (Getter and Rowe 2006). Intensive

green roofs have similar conditions of plants and soil to conventional ground-level gardens. They

require a media depth of at least 6 inches and can use a wide range of plant species from trees to

shrubs. Extensive green roofs, unlike intensive green roofs, require shallower media depth,

3

between 0.8 and 6 inches, and a thinner layer of planting materials such as herbs, turf, and sedum.

Lastly, semi-extensive green roofs exhibit properties of both intensive and extensive systems. They

require an average depth between 4 and 8 inches and contain plant types of both intensive and

extensive green roof systems (Getter and Rowe 2006). In North America, the extensive green roof

system is the most popular type of green roofs. In 2013, green roof systems of North America

consisted of 63% extensive systems, 13% intensive systems, and 14% the semi - extensive systems

(GRHC 2014).

Green Roof Benefits

Green roofs provide many remarkable benefits, especially to dense city areas. Energy

savings are most effective benefit of green roof (Clark et al. 2008). Lower absorbance of vegetation

and low conductance in green roofs lead to lower roof temperature and reduce heat flux (Saiz et

al. 2006). Liu and Baskaran (2005) found that the temperature of a conventional roof peaked at

70°C in the summer, whereas a green roof maintained 25°C. The daily temperature fluctuation

through the conventional roof was 45°C but the green roof’s temperature fluctuation was 6°C.

The UHI effect occurs when urban air temperature is higher than the temperature of the

surrounding countryside. An increase in impervious areas and the concentration of buildings

causes the UHI (Dunnett and Kingsbury 2008). The lack of vegetated areas in cities is a major

cause of the UHI. Planting trees in urban areas can reduce the UHI by altering the heat balance of

the entire city (Akbari and Konopacki 1998). Green roofs increase surface albedo and vegetative

fraction and modify the intensity of the UHI (Taha et al. 1999). Results from Taha et al. (1999)

illustrated that green roofs can reduce air temperature between 1°C to 2°C around 2 PM and lead

to a reduction of energy demand of 10%.

4

The reduction of storm water runoff is a significant benefit of green roofs given the large

amount of impervious area associated with cities. Green roofs absorb rainfall into pore spaces and

capture precipitation in the media and vegetation (Getter and Rowe 2006). The City of Portland

analyzed cost-benefits from replacing 40,000m2 of conventional roofs with green roofs. They

expected that green roofs of 40,000m2 would reduce 56% of annual stormwater runoff and 96% of

peak runoff, and create public benefits of $60,700 and private benefits of $70,330

(Portland 2008).

Green roofs increase air quality by absorbing air pollutants and removing dust particles.

They alleviate airborne contaminants and reduce sulfur dioxide and nitrous (Getter and Rowe

2006). Yang et al. (2008) found that air pollutant removal is highest in May and lowest in February

in Chicago. These results suggest that if all roofs in Chicago are installed with intensive green

roofs, air pollution of 2046.89 metric tons can be removed.

Table 1. Life cycle costs of conventional roofs and green roofs during 90 years

Source: Porsche and Köhler 2003

Roof Type Conventional Roofs Extensive Green Roofs

Construction Costs in $/m² 40 90

Repairs (Interval in Years) Every 10 years

Renovations Costs during 90 years ($/m²) 240 40

Reconstruction Costs ($/m²) 20 40

Disposal and Recycling Costs ($/m²) 20 0

Sum ($/m²) 320 170

Soil layers of green roofs lead to an increase in roof life by reducing energy consumption

(Saiz et al. 2006). Materials of green roofs protect roof membranes from solar radiation during the

day and decrease temperature fluctuations of roofs, which negatively influence the roof life

(Dunnett and Kingsbury 2008; Getter and Rowe 2006). Green roofs extend the roof membrane life

5

by more than 20 years in comparison with the conventional roof membrane (Oberndorfer et al.

2007). A roof life of conventional roofs is typically from 10 to 20 years, whereas green roofs can

continue over 50 years (Dunnett and Kingsbury 2008). Porsche and Köhler (2003) compared roof

life cycle costs of green roofs and conventional roofs during 90 years by calculating the sum of

construction, renovation, and reconstruction cost. Although installation costs of extensive green

roofs are more expensive than conventional roofs, extensive green roofs are more economical than

conventional roofs after 90 years (Table 1).

Net Present Value

The net present value (NPV) leads to a suboptimal investment decision with investment

costs and discretion about the timing of the project. The NPV is an easy-to-use metric for

investment decisions making (Doraszelski 2001). The NPV assessment shows the time to

undertake the project. The simplest statement of the NPV rule is that projects with negative NPVs

are discarded and projects with positive NPVs are undertaken (Ross 1995). For example, the

project is invested $100 million and is expected that $110 million will be generated one year later.

With interest rates at 10.3%, the invested $100 million grows to $110.3 million one year later. This

project should be rejected because expected $110 million is lower than grown $110.3 million one

year later (Ross 1995). The NPV is calculated by

NPV =∑Bt

(1+𝑟)𝑡

𝑇

𝑡=0 (3)

Where T is the life of the project; Bt is the net benefit at time t; and r is the internal rate (Oehmke

2000). The positive NPV is occurred when the NPV is higher than investment costs, and the

negative NPV is occurred when the NPV is lower than investment costs. The project that has the

6

positive NPV is undertaken, whereas the project that has the negative NPV is rejected.

Clark et al. (2008) used the 40-year NPV assessment to calculate cost-benefits of green

roofs of 2,000 m2 from energy savings, stormwater reduction, and an increase in air quality. They

assumed that conventional roofs need renovation after 20 years. An interest rate was estimated at

5% and inflation rate was estimated at 3%. With these data, the NPV assessment indicates that

green roofs have cost-benefits from 20% to 40% more than conventional roofs after 40 years.

Study Area

Ten cities in six states were selected to calculate and compare cost-benefits of green roofs:

New York, NY, Los Angeles, CA, Chicago, IL, Houston, TX, Philadelphia, PA, Phoenix, AZ, San

Antonio, TX, San Diego, CA, Dallas, TX, and San Jose, CA. These ten cities had variable

characteristics of climate, population density, and population demographics. Figure 1 shows

locations of these ten cities.

Figure 1. Locations of the ten cities.

7

Data and Method

Geographic Information System Analysis

Developed areas cause an increase in impervious areas because of the construction of

streets, buildings, and parking lots (Nowak and Greenfield 2012). Green roofs reduce impervious

areas in metropolitan cities with soil and plants (Dunnett and Kingsbury 2008). Thus, the most

fundamental process to find a relationship between green roof installation and population was the

calculation of population based on developed areas. In order to calculate population in developed

areas, developed areas in the ten cities were extracted by using GIS data.

Spatial analyses were needed to calculate developed areas of the ten cities. GIS data were

used to estimate city boundaries and developed areas. Shape files of city boundaries in the ten

cities were obtained from GIS departments of the cities and the U.S. Census Bureau. The “2011

Land Cover” GIS data set was obtained from the Multi-Resolution Land Characteristics

Consortium.

The Spatial Analyst Tools of ArcGIS were used to calculate developed areas within city

boundaries. The “2011 Land Cover” GIS data set consists of pixels of 30 × 30 meters and contained

17 land cover class: unclassified, open water, perennial snow/ice, developed (open space),

developed (low intensity), developed (medium intensity), developed (high intensity), barren land,

deciduous forest, evergreen forest, mixed forest, shrub and scrub, herbaceuous, hay and pasture,

cultivated crops, woody wetlands, and emergent herbaceuous wetlands. The four land cover

classes that include developed (open space), developed (low intensity), developed (medium

intensity), developed (high intensity) were reclassified into developed areas, while all others were

reclassified as undeveloped areas. After reclassifying land cover data, the “Zonal Statistics as Table”

8

and “Join” tools were used to calculate developed areas within a city.

Cost-Benefits of Green roofs

Electric costs for cooling energy per m2

Electric costs for cooling energy per m2 were required to calculate cost-benefits of green

roofs from energy savings and reducing the UHI effect. The method of the “Green Roofs in the

New York Metropolitan Region” research was used to calculate electric costs per m2. This method

uses data of annual total electric consumption and annual total cooling consumption from the

“Annual Energy Review 2011” (EIA 2012). Annual total electric consumption in the United States

was 1.043 trillion kWh, and annual total cooling consumption was 0.141 trillion kWh in 2011.

These data indicate that 13.5% of annual total electric consumption was used for cooling

consumption. This percentage was used to calculate annual cooling consumption per m2. Annual

total electric consumption per m2 was 159.94 kWh/m2. Thus, 13.5% of 159.94 kWh/m2 was

estimated as annual cooling consumption per m2.

Table 2. Electricity prices and annual electric costs for cooling energy of the ten cities.

Source: EIA 2014

City Electricity Price ($/m2) Electric Costs for Cooling ($/m2)

New York, NY 0.2031 4.4

Los Angeles, CA 0.1767 3.8

Chicago, IL 0.1162 2.5

Houston, TX 0.12 2.6

Philadelphia, PA 0.1396 3.0

Phoenix, AZ 0.1253 2.7

San Antonio, TX 0.12 2.6

San Diego, CA 0.1767 3.8

Dallas, TX 0.12 2.6

San Jose, CA 0.1767 3.8

9

Annual cooling consumption per m2 in the United Sates was calculated as 21.615 kWh/m2,

and this electric consumption and electricity prices of ten city were applied to calculate annual

cooling consumption per m2 in the cities. Electricity prices of the ten cities were offered from the

“Average Retail Price of Electricity to Ultimate Customers” data of U.S. Energy Information

Administration (EIA 2014). These data contained average price of electricity of 51 states and cities

in the United States in July 2014. Table 2 summarizes electricity prices and annual electric costs

for cooling energy of the ten cities. These different costs were applied to calculate cost-benefits of

green roofs from energy savings and UHI reduction.

Energy Savings

Green roofs save 25% of cooling energy in comparison with conventional roofs (Dunnett

and Kingsbury 2008). This general description was used to calculate effects of green roofs on

energy savings. Different electric costs for cooling consumption of the ten cities were used to

calculate cost-benefits of green roofs from energy savings with the following equation.

Energy saving cost for annual cooling consumption

= Roof area (m2) × Electric costs for cooling energy per m2 of ten cities × 25% (1)

Urban Heat Island Reduction

The study of Taha et al. (1999) calculated annual electric savings of the ten cities by

reducing the urban heat island (UHI) effect. Their data of annual electric savings were used to

calculate cost-benefits of green roofs from reducing the UHI effect (Table 3). Ten cities in the

study of Taha et al. (1999) contained six cities that were used in this study: New York, Los Angeles,

10

Chicago, Houston, Philadelphia, and Phoenix. However, the other four cities of this study, San

Antonio, San Diego, Dallas, and San Jose, were not contained in the study of Taha et al. (1999).

In order to estimate annual electric savings of these four cities, this study assumed that cities in the

same state have the same annual electric savings. Although this assumption had limitations of

different energy uses of cities in California and Texas because of variable climate conditions in

two states (CEC 2014; NAST 2001), this study analyzed electricity consumptions based on states.

Annual electric savings of San Antonio and Dallas were estimated by data of Houston, and annual

electric savings of San Diego and San Jose were estimated by data of Los Angeles (Table 3).

Table 3. Annual electric savings (kWH/m2) from reducing the UHI by installing green roofs.

Source: Taha et al. 1999

City Annual Electric Savings

(kWH/m2) City

Annual Electric Savings (kWH/m2)

New York, NY 2.09 Phoenix, AZ 6.13

Los Angeles, CA 6.10 San Antonio, TX 4.13 Chicago, IL 2.22 San Diego, CA 6.10

Houston, TX 4.13 Dallas, TX 4.13

Philadelphia, PA 2.30 San Jose, CA 6.10

Based on these data, cost-benefits from reducing the UHI by installing green roofs were calculated

with the following equation.

Cost-benefits from reducing the UHI =

Roof area (m2) × Annual Electric Savings (kWH/m2) × Electricity Price ($/kWH) (2)

Stormwater Runoff Reduction

Stormwater runoff is related to climate characteristics of cities. The method from the study

of Blackhurst et al. (2010) was used to calculate cost-benefits of green roof in relation to reducing

stormwater runoff. Blackhurst et al. (2010) calculated the cost-benefits from reducing stormwater

11

runoff by using the market values of stormwater from Fisher et al. (2008). They estimated that the

market values of stormwater is $2.27 per Kilogallon. Their calculation for cost-benefits of green

roofs from reducing stormwater runoff can be summarized with the following equation.

Cost-benefits from reducing stormwater runoff

= Roof area × Annual average precipitation (m) in the cities × $2.27/Kgal × (1 Kgal / 3.7854m³)

(3)

In this equation, 3.7854m³ indicates the coefficient to transfer unit of Kgallon to m³. Annual

average precipitation data of ten cities were collected from “The Weather Channel” (Table 4).

Table 4. Annual average precipitation of ten cities. Source: The Weather Channel 2014

City Annual Precipitation (m) City Annual Precipitation (m)

New York, NY 1.18 Phoenix, AZ 0.21

Los Angeles, CA 0.38 San Antonio, TX 0.74

Chicago, IL 0.97 San Diego, CA 0.26

Houston, TX 1.39 Dallas, TX 0.96

Philadelphia, PA 1.17 San Jose, CA 0.40

Air Pollution Reduction

Reducing air pollution increases health of people living in metropolitan cities. The “Green

Roofs in the New York Metropolitan Region” (GRNY) research calculated cost-benefits of green

roofs by reducing air pollution (Rosenzweig et al. 2006). The GRNY report estimated that 1 m2 of

green roofs can reduce 0.44 pounds of airborne particles per year and each one pound reduction of

airborne particle has cost-benefits of $2.20. Based on these data, annual cost-benefits of green

roofs from reducing air pollution were calculated with the following equation.

Annual cost-benefits of green roofs from mitigating air pollution

= Roof area (m2) × 0.44 pounds × $2.20/pounds (4)

12

Calculations for Total Cost-Benefits of Green Roofs

Four cost-benefits from energy savings, the UHI reduction, stormwater runoff reduction,

and air pollution reduction, were calculated based on a one-year time period. Excel was used for

the NPV assessment to calculate total cost-benefits of green roofs after 50 years. Interest rates and

roof areas to install green roofs were needed to calculate the NPV. Roof areas to install green roofs

can have variable values depending on city plans. This study assumed that the roof area of 100

km2 in each city will be the planning area to install green roofs because the calculated developed

area in Philadelphia was 314 km2, which is the lowest developed area in the ten cities. Interest

rates were estimated to be 4.4% based on mortgage interest rates on September 26th, 2014. With

the interest rates and roof areas for green roofs, the NPVs of cost-benefits of green roofs in the ten

cities were calculated from energy savings, the UHI reduction, stormwater runoff reduction, and

air pollution reduction.

Population Density and Cost-Benefits of Green Roofs

The number of people within developed areas of 100 km2 was estimated to assess effects

of population density on cost-benefits of green roofs. This study assumed that people have the

same cost-benefits from installing green roofs. For example, if the areas within 100 km2 have cost-

benefits of $1,000 from energy savings, every person who live in this area has cost-benefits of

$1,000 from energy savings. This method was calculated by the following equation.

Cost-benefits of green roofs depending on population density

= Cost-benefits of green roofs without a consideration of population density × The number of

people within developed areas of 100 km2 in the city (5)

13

Population Demographics and City Structures to Install Green Roofs

Although population density and climate characteristics in the ten cities affect cost-benefits

of green roofs, the calculation with these considerations has limitations regarding detailed cost-

benefit calculations because of variable population demographics and city structures. Thus, this

study recalculated cost-benefits of green roofs with considering population demographics and city

structures in the ten cities. The same calculation process was used to conduct recalculations. This

study used three factors of population demographics: people who are 18 years of age and older,

percentages of house owners, and average income of people in cities (Table 5).

Table 5. Population demographics and city structures in the ten cities.

Source: American Fact Finder 2014

City 18 Years of Age and

Older (%) House Owner (%) Average Income ($)

New York, NY 85.1 53.7 67,099

Philadelphia, PA 77.5 59.2 35,386

Chicago, IL 77.4 67.5 45,214

Los Angeles, CA 76.3 67.7 53,001

San Jose, CA 76.4 55.2 91,425

San Diego, CA 77.1 63.6 60,330

Phoenix, AZ 74.3 45.8 51,432

Dallas, TX 72.7 50.4 47,301

Houston, TX 79.9 50.7 31,529

San Antonio, TX 73.5 39.6 49,874

Blackhurst et al. (2010) suggested that building structures for multi-families have more

cost-benefits than the single-families from installing green roofs. City structures to install green

roofs were considered as multi-family structures because multi-family structures that are over 5

units are effective to install green roofs (Blackhurst et al. 2010). Table 6 shows changed areas to

install green roofs in 100 km2. For example, Philadelphia has the effective area to install green

roofs in the area in 18% of 100 km2. This study also assumed that people who are house owners

14

and 18 years of age and older have cost-benefits of green roofs. With these considerations of ages,

house owners, and city structures, cost-benefits of green roofs in the ten cities were calculated by

the following equations.

Effective areas to install green roofs

= Planned areas to install green roofs × Percent of building structures for multi-families over 5

units × Percentage of people who are 18 years of age and older × Percentage of house owners (6)

Table 6. The percentage of multi-families over 5 units and effective areas to install green roofs in

100 km2 in ten cities. Source: American Fact Finder 2014

City Multi-family over 5 Units

(%)

Effective Areas to Install in 100 km2

(km2)

New York, NY 95.6 43.7

Los Angeles, CA 34.4 17.8

Chicago, IL 33.1 17.3

Houston, TX 2.2 0.9

Philadelphia, PA 18.1 8.3

Phoenix, AZ 20.1 6.8

San Antonio, TX 23.1 6.7

San Diego, CA 28.7 14.1

Dallas, TX 34 12.5

San Jose, CA 26 11.0

Results and Discussion

Developed Areas and City Density Rankings

Developed areas represent impervious areas in cities (Nowak and Greenfield 2012). This

study assumed developed areas to be the most practical for the installation of green roofs. Although

the 2011 Land Cover GIS data contained four classes of developed areas including developed

(open space), developed (low intensity), developed (medium intensity), and developed (high

intensity), this study reclassified these four developed areas into one land cover class that included

all developed land covers

15

Table 7 represents city density based on city boundary areas, developed areas, and

developed areas per one person in the ten cities. New York was the densest city with developed

areas of 83.16 m2 per one person, which is 2.4 times higher than city density in Philadelphia and

7.4 times higher than San Antonio. Table 7 shows interesting results of cities. One person in the

ten cities had an average developed area of 380 m2. New York has a similar city boundary area

with the average city boundary area, but the population is almost 6,000,000 people more than the

average population. Philadelphia has the lowest city boundary area, but the city is the second-

densest city. These results (Table 7) are similar to those of Nowak and Greenfield (2012) who

analyzed land covers of 20 cities in the Unites States, including New York, Chicago, Los Angeles,

and Houston. Their results show that New York has a higher percentage of impervious areas than

the other three cities. Chicago has a higher percentage of impervious areas than Los Angeles and

Houston. Los Angeles has a higher percentage of impervious areas than Houston. These results

correspond to the results found in Table 7.

Table 7. Rankings of city density based on population, city boundary areas, developed areas, and

developed areas per on people in the ten cities.

Ranking City Population City Boundary

Area (km2)

Developed

Area (km2)

Developed Area per

One Person (m2)

1 New York, NY 8,405,837 1,216 699 83.16 2 Philadelphia, PA 1,553,165 369 314 202.17

3 Chicago, IL 2,718,782 601 565 207.81

4 Los Angeles, CA 3,884,307 1,239 1,019 262.34 5 San Jose, CA 998,537 467 345 345.51

6 San Diego, CA 1,355,896 836 575 424.07

7 Phoenix, AZ 1,513,367 1,361 760 502.19

8 Dallas, TX 1,257,676 1,013 699 555.79 9 Houston, TX 2,195,914 1,727 1,337 608.86

10 San Antonio, TX 1,409,019 1,259 862 611.77

Average 2,529,250 1,009 718 380.37

16

Cost-Benefits of Green Roofs in One Year

The assessment of cost-benefits in one year shows variable cost-benefits depending on

electricity prices and climate characteristics of the ten cities. Figure 2 shows cost-benefits of the

ten cities from replacing 100 km2 of conventional roof areas with green roofs in a one year time

period.

Figure 2. One-year cost-benefits of the ten cities with replacing conventional roof areas of 100

km2 with green roofs.

Benefits from reducing air pollution in the ten cities, unlike other cost-benefits, had the

same cost-benefit of $96,800,000 because cost-benefits from air pollution were calculated by roof

17

areas without considerations of climate characteristics or electricity prices of cities. For this reason,

four graphs are shown in Figure 2: (1) total cost-benefits, (2) cost-benefits from energy savings,

(3) cost-benefits from reducing the UHI effect, and (4) cost-benefits from reducing stormwater

runoff. San Jose had the highest cost-benefits, whereas Chicago had the lowest cost-benefits from

installing green roofs. Different electricity prices in the ten cities caused different cost-benefits

from energy savings with installing green roofs. New York had the highest cost-benefits from

energy savings because of their higher electricity price. The three cities in California had the

highest cost-benefits from reducing the UHI effect. The cities that have higher air temperature

have more cost-benefits from reducing the UHI effect (Taha et al. 1999). Variable precipitation of

the ten cities caused different cost-benefits from reducing stormwater runoff with installing green

roofs. The cities in Texas had higher cost-benefits from reducing stormwater runoff, whereas the

cities in California had lower cost-benefits from reducing stormwater runoff because of their low

precipitation. These results show that electricity prices and climate characteristics of cities affect

cost-benefits of green roofs.

Cost-Benefits after 50 Years

The NPV assessment for a 50-year time period was used to estimate cost-benefits with

replacing conventional roof areas of 100 km2 in the ten cities with green roofs. The NPVs of four

cost-benefits were calculated based on cost-benefits in a one-year period. Figure 3 shows the NPVs

of total cost-benefits after 50 years with replacing conventional roof areas of 100 km2 in the ten

cities with green roofs. San Jose has the highest NPV of total cost-benefits of $2,749,507,735 after

50 years, and Chicago has the lowest NPV of total cost-benefits of $1,136,273,690 (Figure 3).

However, the NPV of conventional roofs of 100 km2 has -$1,458,198,088 because of their

18

maintenance and renovation costs. Green roof installation in Chicago is more active than San Jose

(GRHC 2014), but cost-benefits of installing green roofs in San Jose were two times higher than

those of Chicago (Figure 3). These results indicate that it would be more economical for San Jose

to install green roofs and the city should consider installing green roofs.

Figure 3. The NPVs of total cost-benefits after 50 years with replacing conventional roof areas of

100 km2 in the ten cities with green roofs.

Figure 4. The years when the NPVs of cost-benefits of green roofs in the ten cities have positive

values and higher the NPV of maintenance and renovation costs of conventional roofs.

19

Figure 4 shows the years when cost-benefits of green roofs have positive NPVs. The results

of this study are similar to those of Clark et al. (2008), who suggested that the NPVs of cost-

benefits of green roofs become positive values after a period of 10 to 22 years. The NPVs of San

Jose, Los Angeles, New York, and San Diego have positive NPVs after 16 years, and Chicago has

a positive NPV after 25 years. The years when the NPVs of cost-benefits of green roofs are higher

than the NPVs of maintenance and renovation costs of conventional roofs were calculated. The ten

cities except Chicago have higher NPVs of green roofs than conventional roofs after 15 years, and

only Chicago has higher NPVs of cost-benefit of green roofs than conventional roofs after 16 years.

These years can be a good suggestion for city planners in their decision for green roof installation

because the years in Figure 4 indicate when green roofs can be economical systems in comparison

with conventional roofs.

A Relationship between Population Density and Cost-Benefits of Green Roofs

Two analyses for developed areas and cost-benefits of green roofs were used to assess city

density and effectiveness of green roofs. Developed areas per one person represented population

density in the ten cities. The cost-benefit assessment showed which cities have more cost-benefits

from installing green roofs. These two analyses were combined to compare which cities have more

cost-benefits from installing green roofs depending on population density.

Table 8 shows three rankings of the ten cities. San Jose has the highest cost-benefits with

green roofs installation in a one year period and ranks fifth in the number of people within

developed area of 100 km2. Chicago has the lowest cost-benefit of the ten cities from installing

green roofs because of the city’s relatively low electricity price and climate characteristic.

However, Chicago has a higher population density than other cities. With these data, this study

20

assessed impacts of population density on cost-benefits from installing green roofs in cities.

Table 8. Comparison of three rankings of the ten cities: (1) total one year cost-benefits with

replacing conventional roof areas of 100 km2 with green roofs, (2) developed areas per one person,

and (3) the number of people within developed areas of 100 km2.

City

Total One Year Cost-

Benefits

Developed Areas per

One Person (m2)

The Number of People within

Developed Areas of 100 km2

Ranking $ Ranking m2 Ranking Number of People

San Jose, CA 1 323,573,897 6 346 5 289,427

Los Angeles, CA 2 322,374,552 7 262 4 381,185 New York, NY 3 320,009,246 10 83 1 1,202,501

San Diego, CA 4 315,178,483 5 424 6 235,810

Houston, TX 5 294,714,467 2 609 9 164,241

Philadelphia, PA 6 274,069,674 9 202 2 494,633 Dallas, TX 7 268,928,553 3 556 8 179,924

San Antonio, TX 8 255,735,759 1 612 10 163,460

Phoenix, AZ 9 253,702,021 4 502 7 199,128 Chicago, IL 10 243,264,625 8 208 3 481,209

In order to calculate cost-benefits of green roofs depending on population density, this

study assumed that people within developed areas of 100 km2 have the same cost-benefits from

installing green roofs. This means that the 289,427 people in San Jose respectively have cost-

benefits of $ 323,573,897. Figure 5 compares two cost-benefits: (1) cost-benefits with replacing

conventional roof areas of 100 km2 in the ten cities with green roofs and (2) total cost-benefits of

people who live within developed areas in 100 km2. New York has the highest total cost-benefits

of people from installing green roofs within developed areas in 100 km2 because of its high

population density (Figure 5 and Table 8). Although Chicago has the lowest one-year cost-benefits,

the city ranked third in total cost-benefits from installing green roofs with a consideration of

population density. Houston had median one-year cost-benefits, but the city had low total cost-

benefits with a consideration of population density because of low population density of the city.

These results indicate that high population density increase cost-benefits from installing green

21

roofs.

Figure 5. The comparison of one-year cost-benefits and total cost-benefits of people within 100

km2.

Population Demographics and City Structures to Install Green Roofs

Ages, house owners, and city structures of the ten cities were considered to calculate cost-

benefits of green roofs. These considerations were also combined with population density. Figure

6 shows the 50-year NPVs of cost-benefits of effective areas to install green roofs (Table 6) with

considerations of ages, house owners, city structures, and population density. New York had the

highest cost-benefits, whereas Dallas had very low cost-benefits in comparison with other cities

because of the low electricity, low precipitation, low city density, and low percentage of people

who are house owners and 18 years of age and older.

0

50,000

100,000

150,000

200,000

250,000

300,000

350,000

400,000

450,000

0

50,000,000

100,000,000

150,000,000

200,000,000

250,000,000

300,000,000

350,000,000

Co

st-b

enef

its

of

peo

ple

wit

hin

100

km

²($

1,00

0,00

0,00

0)

On

e ye

ar t

ota

l co

st-b

enef

its

($)

City

One year total cost-benefits Cost-benefits of people within 100 km²

22

Figure 6. The NPVs of total cost-benefits of effective areas to install green roofs after 50 year. This

graph shows overall cost-benefits of green roofs with considerations of city structures, population

density and population demographics such as ages and house owners.

Overall Rankings of Ten Cites

Overall rankings of the ten cities were estimated to assess effects of city structures,

population density and population demographics on cost-benefits of green roofs. Table 9 shows

the overall rankings of the NPVs of cost-benefits of green roofs. Rankings were classified into

three categories: (1) the first category of rankings was estimated by only the NPVs of cost-benefits

of green roofs after 50 years with considerations of electricity prices and climate, (2) the second

category of rankings was estimated by the NPVs of cost-benefits of green roofs after 50 years with

a consideration of electricity prices, climate, and population density, and (3) the third category of

rankings was estimated by the NPVs of cost-benefits of green roofs after 50 years with

considerations of electricity prices, climate, population density, population demographics, and city

structures. The rankings of the ten cities changed depending on which factors were applied to

calculations for cost-benefits of green roofs.

0

500,000,000

1,000,000,000

1,500,000,000

2,000,000,000

2,500,000,000

3,000,000,000

Co

st-b

enef

its

($)

City

23

Table 9 shows which factors affect cost-benefits of green roofs. Cost-benefits of green roofs

of cities in the state of California are highly affected by factors of population density, population

demographics, and city structures. Although Chicago had low cost-benefits of green roofs in the

calculation with considerations of electricity prices and climate, the city has higher cost-benefits

in the calculation with considerations of population density, population demographics, and city

structures. These results represent that cost-benefits of green roofs were affected by variable

factors such as electricity price, climate characteristics, population density, population

demographics, and city structures.

Table 9. Rankings of three categories: (1) A is the NPVs of cost-benefits after 50 years with

replacing conventional roof areas of 100 km2 in the ten cities with green roofs with considerations

of electricity price and climate, (2) B is the NPVs of cost-benefits of 100 km2 green roof areas after

50 years with considerations of population density, and (3) C is the NPVs of cost-benefits of

effective areas to install green roofs after 50 years with considerations of population density,

population demographics, and city structures.

Existing Green Roofs in the Ten Cities

The areas of existing green roofs and the calculated cost-benefits were compared to assess

and suggest green roof installation. Income of people also shows the potential possibility to install

green roofs. Potential cost-benefits from installing green roofs were estimated by the NPVs of

24

cost-benefits of green roofs after 50 years with considerations of population density, population

demographics, and city structures. This study assumed that cities that have population with higher

income have the higher possibility to install green roofs. In the results of this study, New York had

the highest cost-benefits from installing green roofs. Table 10 shows that New York has second-

highest incomes and area of existing green roof areas in the ten cities. This result indicates that

New York is the most appropriate city to install green roofs and has the appropriate trend to install

green roofs. Chicago, Philadelphia, San Diego, and San Jose had interesting results. Chicago and

Philadelphia have the higher areas of installed green roofs even though they have lower cost-

benefits and incomes when compared with other cities. However, San Diego and San Jose have

low areas of installed green roof despite of their higher cost-benefits and incomes. These results

indicates that green roof installations are required to San Diego and San Jose than other cities

because of their higher incomes and potential cost-benefits from installing green roofs than other

cities.

Table 10. Rankings of areas of existing green roofs, potential cost-benefits from installing green

roofs, and income of people in the ten cities.

City

Ranking

Areas of Existing

Green Roofs

Potential Cost-Benefits

from Installing Green Roofs

Income

Chicago, IL 1 8 8

New York, NY 2 1 2

Philadelphia, PA 3 6 9

Houston, TX 4 3 10

Los Angeles, CA 5 5 4

San Diego, CA 6 2 3

San Jose, CA 7 4 1

Phoenix, AZ 8 7 5

San Antonio, TX 8 9 6

Dallas, TX 8 10 7

25

Conclusion

The impacts of impervious areas associated with urbanization have been a major

consideration in metropolitan cities. Green roofs can mitigate these impacts by replacing

impervious covers with green covers that provide many environmental benefits. This study

calculated cost-benefits in ten cities with replacing conventional roof areas with green roofs in

their 100 km2 developed areas. In order to calculate cost-benefits of green roofs, developed areas

in the ten cities were extracted by using GIS data. Developed areas were the potential area

considered as the most appropriate area to install green roofs and were used to calculate population

density in cities. Cost-benefits of green roofs in a one-year period showed cost-benefits depending

on electric prices and climate characteristics of the ten cities. The NPV assessment was used for

decisions to install green roofs. The years when green roofs have more cost-benefits than

conventional roofs were also calculated with the NPV assessment. This study recalculated cost-

benefits of green roofs with considerations of specific characteristics of cities such as population

size and density, population demographics, and city structures to assess effects of these factors on

cost-benefits of green roofs. With these calculations, this study estimated rankings of the ten cities

with three categories: (1) the NPVs of general cost-benefits with considerations of electricity price

and climate, (2) the NPVs of cost-benefits with a consideration of population density, and (3) the

NPVs of cost-benefits with considerations of population density, population demographics, and

city structures. Overall rankings of these three categories were the most interesting results in this

study. These three types of rankings showed that cost-benefits of green roofs change with

applications of variable factors of population density, population demographics, and city structures.

Lastly, the areas of existing green roofs and the calculated cost-benefits were compared to analyze

the trend of green roof installation of the ten cities and to suggest which cities need to install green

26

roofs. New York had the most appropriate trend of green roof installation. Although Chicago did

not have good conditions to install green roofs, the city had higher green roof installation. However,

San Diego and San Jose did not have an appropriate trend of green roof installation, although they

have more potential for higher cost-benefits from installing green roofs compared to other cities.

With these results, this study clearly shows the overall cost-benefits of green roofs and suggests

the value of green roof installation for the ten cities.

27

REFERENCES

Akbari H, Konopacki SJ. 1998. The impact of reflectivity and emissivity of roofs on building

cooling and heating energy use. In: The thermal performance of the exterior envelopes of building

VII; 1998 Dec 6-10; Clearwater Beach (FL); American Society of Heating, Refrigeration, and Air-

Conditioning Engineers, Inc. p. 29-39.

Blackhurst M, Hendrickson C, and Matthews S. 2010. Cost-effectiveness of green roofs.

Architectural Engineering. 16(4):136-143.

City of Dallas GIS Services [Internet]. City of Dallas Shapefiles; [cited 2014 Sep 28] Available

from http://gis.dallascityhall.com/EnterpriseGIS/shapezip.htm.

City of Los Angeles [Internet]. GIS data sets; [cited 2014 Sep 28] Available from

http://planning.lacity.org/MapGallery/MapGallery_GISdata/MapGalleryData.htm.

City of San Antonio [Internet]. GIS Data; [cited 2014 Sep 28] Available from

http://www.sanantonio.gov/GIS/GISData.aspx.

City of San Jose [Internet]. Maps & Apps; [cited 2014 Sep 28] Available from

http://www.sanjoseca.gov/index.aspx?NID=3308.

Clark C, Adriaens P, Talbot F. 2008. Green roof valuation: A probabilistic economic analysis of

environmental benefits. Environmental Science & Technology 42(6):2155-2161.

David J N and Eric J G. Tree and impervious cover change in U.S. Urban Forestry & Urban

Greening 11(1):21-30.

Doraszelski U, 2001. The net present value method versus the option value of waiting: A note on

Farzin, Huisman and Kort (1998). Journal of Economic Dynamics and Control. 25(8):1109-1115.

Dunnett N, Kingsbury N. 2008. Planting Green Roofs and Living Walls (PDX): Timber Press, p.

14-79.

Fisher DC, Whitehead CD., and Melody M. 2008. National and regional water and wastewater

rates for use in cost-benefit models and evaluations of water efficiency programs. Lawrence

Berkeley National Laboratory. [cited 2014 August 31] Available from

https://publications.lbl.gov/islandora/object/ir%3A128721

Gedge D, Kadas G. 2005. Green roofs and biodiversity. Biologist 52(3):161-169.

Getter K and Rowe D. 2006. The role of extensive green roofs in sustainable development.

HortScience 41(5):1276-1285.

Green Roofs for Healthy Cities (GRHC). [Internet]. Annual green roof industry survey; [cited

2014 Sep 28]. Available from

http://www.greenroofs.org/resources/GreenRoofIndustrySurveyReport2013.pdf.

Liu K., Baskaran A. 2005. Using garden roof systems to achieve sustainable building envelopes.

Construction Technology 65:1-6.

28

Lua D and Wengb Q. 2006. Use of impervious surface in urban land-use classification. Remote

Sensing of Environment 102(1-2):146-160.

National Assessment Synthesis Team (NAST). 2001. Climate change impacts on the United States.

The potential consequences of climate variability and change. US Global Change Research

Program. [cited 2014 Sep 28] Available from

http://www.globalchange.gov/browse/reports/climate-change-impacts-united-states-potential-

consequences-climate-variability-and

New York State [Internet]. NYSGIS Clearinghouse; [cited 2014 Sep 28] Available from

http://gis.ny.gov/gisdata/inventories/details.cfm?DSID=927.

Nowak DJ, Greenfield EJ.2012. Tree and impervious cover change in U.S. cities. Urban Forestry

& Urban Greening. 11(1): 21-30.

McKinney M. 2002. Urbanization, biodiversity, and conservation. BioScience 52(10): 883-890.

Multi-Resolution Land Characteristics Consortium (MRLC) [Internet]. National Land Cover

Database 2011; [cited 2014 Sep 28] Available from http://www.mrlc.gov/nlcd11_data.php.

Oberndorfer E, Lundholm J, Bass B, Coffman RR, Doshi H, Dunnett N, Gaffin S, Kohler M, Liu

KKY, Rowe B. 2007. Green roofs as urban ecosystems: Ecological structures, functions, and

services. BioScience. 57(10):823-833.

Oehmke JF. 2000. Anomalies in net present value calculations. Economics Letters 67(3):349-351.

Pennsylvania Spatial Data Access [Internet]. Philadelphia city limits; [cited 2014 Sep 28]

Available from

http://www.pasda.psu.edu/uci/PhiladelphiaAgreement.asp?File=http://www.pasda.psu.edu/philac

ity/data/phila-city_limits_shp.zip.

Porsche U, Köhler M. 2003. Life cycle costs of green roofs. In: RIO, Brazil, World Climate &

Energy Event; 2003 Dec 1-5; Rio de Janeiro, Brazil: p. 461-467.

Rosenzweig C., Gaffin S, and Parshall L. 2006. Green roofs in the New York metropolitan region.

Columbia University Center for Climate Systems Research and NASA Goddard Institute for Space

Studies.

Ross SA. 1995. Uses, abuses, and alternatives to the net-present-value rule. The Journal of the

Financial Management Association. 24(3):96-102.

Saiz S, Kennedy C, Bass B, Pressnai K. 2006. Comparative life cycle assessment of standard and

green roofs. Environ. Sci. Technol. 40(13): 4312-4316.

SanGIS [Internet]. Region Data Warehouse; [cited 2014 Sep 28] Available from

http://www.sangis.org/

Taha H, Konopacki S, Gabersek S, 1999. Impacts of large scale modifications on meteorological

conditions and energy use: A 10-region modeling study. Theoretical and Applied Climatology, 62:

175–185.

29

The California Energy Commission (CEC) [Internet]. Energy consumption data management

system; [cited 2014 Oct 21] Available from http://ecdms.energy.ca.gov/elecbycounty.aspx

The City of Houston [Internet]. My City Maps & Apps; [cited 2014 Sep 28] Available from

http://mycity.houstontx.gov/home/cohgis.html.

The City of Portland. 2008. Portland (OR): Cost benefit evaluation of ecoroofs; [cited 2014 Sep

28] Available from http://www.portlandoregon.gov/bes/article/261053.

The Weather Channel [Internet]. [cited 2014 Sep 28] Available from http://www.

http://www.weather.com/

U.S. Census Bureau (USCB) [Internet]. Harrisburg (city), Pennsylvania; [cited 2014 Sep 28]

Available from http://quickfacts.census.gov/qfd/states/42/4232800.html

U.S. Census Bureau American Fact Finder [Internet]. [cited 2014 Sep 28] Available from

http://quickfacts.census.gov/qfd/states/42/4232800.html

U.S. Energy Information Administration, U.S. Department of Energy (EIA). 2012. Annual energy

review 2011; [cited 2014 Sep 28]. Available from

http://www.eia.gov/totalenergy/data/annual/archive/038411.pdf.

Yang J, Yu Q, Gong P. 2008. Quantifying air pollution removal by green roofs in Chicago.

Atmospheric Environment 42(31): 7266-7273.

Yuana F and Bauerb M. 2007. Comparison of impervious surface area and normalized difference

vegetation index as indicators of surface urban heat island effects in Landsat imagery. Remote

Sensing of Environment 106(3):375-386.