2010-sustainable urban energy

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Sustainable urban energy: Development of a mesoscale assessment model for

solar reflective roof technologies

J.H. Jo a, J. Carlson b, J.S. Golden c,n, H. Bryan d

a School of Technology, Illinois State University, USAb School of Sustainable Engineering and the Built Environment, Arizona State University, USAc Nicholas Institute for Environmental Policy Solutions, Pratt School of Engineering, Duke University, USAd School of Architecture, Arizona State University, USA

a r t i c l e i n f o

Article history:

Received 3 February 2010

Accepted 14 September 2010

Keywords:

Cool roof systems

Remote sensing

Energy modeling

a b s t r a c t

Buildings and other engineered structures that form cities are responsible for a significant portion of the

global and local impacts of climate change. Consequently, the incorporation of building design

strategies and materials such as the use of reflective roof materials, or ‘cool’ roofs, are being widely

investigated. However, although their benefits for individual buildings have been studied, as yet there is

little understanding of the potential benefits of urban scale implementation of such systems. Here we

report the development of a new methodology for assessing the potential capacity and benefits of

installing reflective roofs in an urbanized area. The new methodology combines remote sensing image

data with a building energy computer simulation to quantify the current rooftop reflectivity and predict

the potential benefits of albedo improvement. In addition to the direct electricity savings, cool roof

systems reduce peak electrical demand in the month of August when the peak demand is at its highest

in the case study area. Environmental benefits associated with lowering greenhouse-gas emissions are

also substantial. The new methodology allows the calculation of payback periods to assist planners to

evaluate the potential economic benefits of the widespread installation of cool roof systems.

& 2010 Elsevier Ltd. All rights reserved.

1. Introduction

For the first time in history more than half of the planet’s

population lives in cities and by the year 2030 this will have

climbed to 60% (UN, 2008). The expansion of cities resulting from

population and economic growth will have a major impact on

global and local climates due to the increased energy use and

emissions associated with urban environments (Yang et al., 2005).

The buildings and other structures that form cities are responsible

for a significant portion of these impacts. The location, material

selection and spacing of buildings directly affect local climates at

the neighborhood and street level, while the emissions associated

with energy consumed in building operations contribute to global

warming (Graham, 2002). Building design strategies and materi-

als that address both the energy use and microclimate altering

effects of buildings thus have the potential to achieve a significant

reduction in a building’s overall environmental impact ( Jo et al.,

2010). Reflective roof materials, also known as ‘cool’ roofs, are

widely recommended as a design feature that can address both

the energy use and microclimate impacts of building. The

potential benefits of cool roofing product applications for at a

single building are well documented (Akbari, 2003; Jo et al.,

2010). Determining the potential application and aggregated

benefits of implementing and incentivizing certain building

technologies is a growing concern for power utilities providers

and urban planners. There is a need for improved analysis

methods that provide fast, reliable and relative inexpensive

results for cool roof application at the city scale. Here we report

the development and validation of a new methodology for

assessing the potential capacity and benefits of installing

reflective roofs in an urbanized area and provide a real-world

case study demonstrating its use.

1.1. Energy and buildings

Buildings are responsible for 72% of the electrical energy used

in the US every year (EIA, 2007). Of the many sources of electrical

energy demand within buildings, the electricity used for cooling

and lighting is among the largest. Although an average building in

the US consumes 14% of its energy in cooling ( EIA, 2007), the

energy demand for cooling is inevitably higher in warmer

climates such as that in Phoenix, Arizona (Michael, 2008).

The cooling load within a building is influenced by its total

heat gain, a large portion of which comes from solar heat gain

Contents lists available at ScienceDirect

journal homepage: www.elsevier.com/locate/enpol

Energy Policy

0301-4215/$- see front matter & 2010 Elsevier Ltd. All rights reserved.

doi:10.1016/j.enpol.2010.09.016

n Corresponding author. Tel.: +1 919 613 3646; fax: +1 919 613 8712.

E-mail address: [email protected] (J.S. Golden).

Please cite this article as: Jo, J.H., et al., Sustainable urban energy: Development of a mesoscale assessment model for solar reflectiveroof technologies. Energy Policy (2010), doi:10.1016/j.enpol.2010.09.016

Energy Policy ] (]]]]) ]]]–]]]

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through building surfaces. Especially in hot climates, the solar

heat gain through walls, floors and ceilings is significant. Some

commonly used asphalt-based roofing materials readily absorb

solar radiation and reflect only a small portion of the incident

energy. During clear sky conditions up to about 1 kW/m2 of solar

radiation can be incident on a roof surface, and between 20% and

95% of this radiation is typically absorbed (Suehrcke et al., 2008).

Dark roofs can reach temperatures of over 140 1F (60 1C) on sunny

summer days in many urban regions of the US, resulting insignificant heat conduction into the building through the roof

surface ( Jo et al., 2010).

Reducing the heat gain through roofs not only has a significant

effect on energy costs for the building owners but also leads to

decreased production needs by power utilities if implemented

throughout a service area. The roof heat gain can be reduced in

the following ways: (1) increase the insulation between the

exterior and interior of the building, (2) shade the roof surface

from direct sun exposure, and (3) use reflective roofing materials,

also known as ‘cool roof systems’, that reflect ambient solar

radiation more efficiently than conventional roofing materials

(Golden et al., 2007).

Most US cities have significant opportunities to utilize cool

roof systems and thus gain substantial benefits from the resulting

reduction in energy demands and the mitigation of the UHI effect,

since in urban regions roofs account for 20–25% of all urban land

cover (Rose et al., 2003). This strategy is supported by many state

and federal government organizations. For example, President

Obama used the example of ‘reflective and vegetated roofs’ as one

cost-effective strategy that could be implemented to reduce

energy use in federal buildings in a recent Executive Order 13514

(Federal Register, 2009). Many states actively support cool roof

programs by offering voluntary incentives and/or by imposing

mandatory requirements, such as the programs in Georgia and

California (US EPA, 2009). Many city governments have also

required the use of cool roof system as part of their building and

planning regulations. For example, Chicago amended its energy

code requirements for low-sloped roofs to include cool roof

systems in 2003 (US EPA, 2009).

1.2. Research objective

Although a series of research studies have examined the

benefits of cool roof systems for individual buildings, few have

examined the potential benefits on an urban scale. Simpson and

McPherson (1997) reported air conditioning energy savings in the

range of 5–28% in several 1/4-scale models in Tucson, AZ, while Jo

et al. (2010) quantified the financial and environmental benefits

of a cool roof application at a large commercial facility with

experimental results for real-world cool roof applications. Jo et al.

reported that cool roof applications can reduce electricity demand

by up to 5% with a payback period in 8–9 years, and went on toevaluate the potential reduction of GHG emissions and financial

benefits from environmental cost avoided.

A few studies have attempted to evaluate the urban scale

benefits of sustainable technologies applicable on urban surfaces.

Konopacki et al. (1997) developed a model to simulate the effects

of cool roof system applications to compute potential energy

savings in 11 metropolitan areas across the US, predicting savings

of $37 million (M) for Phoenix and $35 M in Los Angeles. Rose

et al. (2003) stressed the need to apply urban fabric data to

evaluate the benefits of albedo improvement in urban areas. They

visually inspected aerial orthophotos and grouped the types of

surface materials, and then extrapolated the collected sampling

data to quantify potential city wide implementation benefits.

Sailor and Dietsch (2007) developed a web-based screening tool,

the mitigation impact screening tool (MIST), to assess the

potential of urban heat island mitigation strategies such as albedo

and vegetation modification. The results presented by MIST

include a high degree of uncertainty and are intended only as a

first-order estimate that urban planners can use to assess the

viability of heat island mitigation strategies for their city (Sailor

and Dietsch, 2007). Akbari et al. (2009) estimated that increasing

the world-wide albedos of urban roofs and paved surfaces will

induce a negative radiative forcing on the earth equivalent tooffsetting about 44 Gt of CO2 emissions on a global basis. This

estimated CO2 offsets from albedo modifications are dependent

on assumptions used in their study, but nevertheless demonstrate

remarkable global cooling potentials that may be obtained from

cooler roofs and pavements. Lemp and Weidner (2005) presented

a method for roof surface classification using hyperspectral and

laser scanning data, analyzing slope, exposition, size and surface

material of urban roof surfaces to examine the ecological impacts

of rain run-offs’ first flush. None of these studies, however, has

developed a method that adequately quantifies the effect of the

actual surface reflectivity of rooftop surfaces or that evaluates

both the direct and indirect benefits that could be gained due to

reflectivity improvement.

Our preliminary analysis found that these prior works can be

improved to give more accurate results. Konopacki et al. (1997)

attempted to quantify the energy savings from cool roof

applications based on simulation results for the number of kWh

per 1000 ft2 (92.9 m2), which were then multiplied by the total

building rooftop area to calculate the total electricity consump-

tion of the entire building stock. However, the use of standardized

energy consumption per square area is incorrect because it

assumes the energy per square area to be uniform, which is rarely

the case. This assumption increases the uncertainty of the

aggregated results because it fails to recognize that the energy

consumption of buildings varies depending on the building use

and size.

The objective of this research is therefore to develop and

validate a new methodology to assess city scale cool roof

application feasibility and potential energy saving benefits using

remote sensing, geographical information systems (GIS) and

building energy simulation modeling tools. This study developed

a method to address uncertainties in the energy savings

achievable, specifically the quantification of current building

rooftop reflectivity as well as reflectivity improvement potential,

in order to gain a more reliable estimate of the potential energy

savings from urban scale cool roof applications.

2. Experimental site description

The site selected for this case study was a four-square-mile

block (10.36 km2) located in Chandler, Arizona (latitude 3311802400

and longitude 11115003000), part of the Phoenix Metropolitan Area(PMA). Electricity is provided by a single utility company and the

electricity consumption data were collected for the three year

period from 2006 to 2008. The total electricity consumption

figures were 183, 198, and 182 GWh for 2006, 2007, and 2008,

respectively.

Government and commercial buildings in the boundary area

were targeted for the study (Fig. 1). Although these 932

governmental and commercial buildings make up 33% of the

total 2800 buildings within the study boundaries, they comprise

68% of the rooftop surface area and 4% of the land cover.

The Phoenix Metropolitan Area (PMA)’s climate is character-

ized by high average temperatures and low humidity throughout

the year (ASMET, 2008). During the summer, the mean daily

maximum temperature ranges from 951F (35

1C) in May to

J.H. Jo et al. / Energy Policy ] (]]]]) ]]]–]]]2






105.8 1F (41 1C) in August; the mean daily maximum relative

humidity over the same period ranges from 59% to 75%. During

the winter, the mean daily minimum temperature ranges from

62.2 1F (17 1C) in January, to 64.4 1F (18 1C) in December; the

mean daily maximum relative humidity over the same period

ranges from 66% to 69%. The daily solar irradiation in the region is

very high, ranging from a minimum of 9.0 MJmÀ2 (day)À1 in

January to a maximum of 30.9 MJ mÀ2 (day)À1 in June. The

average annual wind speed is quite low, at around 0.68 msÀ1.

The majority of the precipitation occurs in the winter and late

summer: 1.1 in (28.7 mm) in January, 0.11 in (2.9 mm), in April,

and 1.35 in (34.2 mm), in July.

3. Methodology

A hierarchical methodology was devised to estimate the

potential benefits of urban scale cool roof applications. The

research method incorporated both remotely sensed image data

and geographical information system (GIS) analyses to measure

building rooftop reflectivity and integrate this information into a

building energy simulation. The US Department of Energy’s

building energy simulation software program, EnergyPlusTM,

was used to evaluate benefits from energy savings when cool

roof technology is applied on those rooftops where reflectivity can

be improved. Fig. 2 presents a flow diagram of the methodological

approach used to model the urban scale application benefits of

cool roof technology.

3.1. Albedo measurement through remote sensing data analysis

Surface reflectivity, commonly referred to as albedo, can be

measured either directly in the field with a pyranometer or

indirectly with remotely sensed image data appropriately pro-

cessed for reflectivity analysis. The solar reflectivity of surface

material can be measured using a pyranometer according to

ASTM E1918-06 Standard Test Method for Measuring Solar

Reflectance of Horizontal and Low-Sloped Surfaces in the Field

(ASTM, 2006). Jo et al. (2010) measured the solar reflectivity of

roof surfaces that were both dark and cool with a pyranometer,

Hukseflux NR01 4 component net-radiation sensor. This type of

device has a published error of 72.5% in the solar wavelength

range. Other sources of uncertainty in albedo measurement

include the time of day, the time of year and sky conditions. By

following the ASTM method, these sources of uncertainty can beminimized ( Jo et al., 2010).

Solar reflectance for large areas is often estimated using

remotely sensed orthophotos captured from satellites. Remotely

sensed image data sets from the Advanced Spaceborne Thermal

Emission and Reflection Radiometer (ASTER) and the Moderate

Resolution Imaging Spectroradiometer (MODIS) can be processed

to estimate albedo (USGS, 2009). Albedo is defined as the ratio of

the amount of electromagnetic radiation reflected by an object to

the amount of radiation incident upon it (USGS, 2009). Its value is

between 0 and 1, where 0 indicates NO reflectivity and higher

values of albedo represent a higher fraction of reflected solar

energy.

An object that has a low albedo is likely to have higher heat

absorption capacities and retain higher temperatures. Using

Fig. 1. Commercial and government buildings in the case study area.

Albedo calculation on

the rooftops

(Definiens Developer)

Quickbird

image with

spectral

information

(RGB+NIR)

GIS data

Building

shpefile

Building rooftops

indentified within GIS

building shapefile

Energy and environmental

benefits in the region

Building energy

modeled with albedo

improvement potential(EnergyPlus)

Building Information:

use, size, and year built

Fig. 2. Methodology applied to estimate the urban area benefits of cool roof

systems.

J.H. Jo et al. / Energy Policy ] (]]]]) ]]]–]]] 3






brightly colored material or coating the surface with a white

finish, will decrease its surface temperature significantly. When

white paint is applied to a rooftop, this will help to reduce the

cooling demand of the entire building structure ( Jo et al., 2010).

Surface emissivity is another factor which can affect the ability of

a material to hold or release heat. For example, a material with

low emittance ($0.1) traps the heat in, while a material with high

emittance ($0.9) allows the heat to escape.

The albedo measurements given in prior works such as Goldenet al.’s (2007) studies in the Phoenix Metropolitan Area in Arizona

and Jo et al.’s (2010) study of the Seoul Metropolitan Area in South

Korea, both of which were derived from data captured by ASTER,

provide useful insights into the relationship between the albedo

and the surface temperature, as well as the resulting impact

on the UHI effect. The resolution of the data images from ASTER

(15 m for visible bands to near infra red), however, cannot

accurately capture albedo on rooftops where the rooftop area

is smaller than the resolution of the data image. Even where

it is possible to capture the surface reflectivity on relatively

large rooftops, currently ASTER data must be processed manually.

When dealing with a large number of buildings, it is processed

through visual inspection of each individual building, requiring

a great deal of human intervention and imposing practical

limits on the number of building rooftops that can be included

in a study.

Consequently, none of these prior methods can be utilized to

rapidly quantify urban scale building rooftop surface albedo,

especially when dealing with the large number of buildings in

urban areas. To measure the albedos of the 932 commercial and

governmental buildings in the case study area, the GIS building

shapefile of the city was integrated into high resolution

QuickbirdTM imagery in the Definiens DeveloperTM software

package to quantify reflectivity inside the building shapefile,

which is confined to the building rooftop areas as shown in Fig. 3.

Quickbird remote sensing data images were processed by

Definiens Developer software to identify reflectivity on the

rooftops and the results were exported into ArcGIS (geographical

information system) to recognize building rooftop availability for

cool roof system applications based on the current reflectivity of

the exiting rooftops. Incorporating remote sensing data derived

from satellite platforms with software such as Definiens Devel-

oper that can analyze an image not by single pixels but through

image objects and their mutual relations and attributes, makes it

possible to derive a higher quality urban land cover classification

compared to single-pixel approaches (Digital Globe, 2010).

The remotely sensed data used was obtained by the Quickbird

satellite on September 28, 2008. Imagery is recorded in four

spectral bands: blue (0.45–0.52 mm), green (0.52–0.60 mm), red

(0.63–0.69 mm), and near-infrared (0.76–0.90mm) by a multi-

spectral scanner with a spatial resolution of 2.4 m per pixel. In

addition, a panchromatic image was recorded within the wave-

length range of 0.44–0.90 mm with a spatial resolution of 0.6 m/pixel (Digital Globe, 2010). These four pixel values (red, green, blue,

and near infrared) represent about 50% of the solar energy over the

entire wavelength of the solar spectrum that reaches the Earth’s

surface according to ASTM G173standard (ASTM, 2008).

The spectral data from the Quickbird images, including RGB

(450–690 nm) and NIR (760–900 nm), were quantified and con-

verted to a number in the range from 0 to 1 that represented the

reflectivity of that surface. It was possible to quantify an individual

building’s rooftop reflectivity using the proposed method, as the

image data was confined within the building shapefile areas, which

excluded the rest of the surface area.

The rooftop albedo measured by the proposed method was

compared to the rooftop albedo processed with ASTER data. The

ASTER data was acquired from National Aeronautics and Space

Administration (NASA) and they report the accuracy of the surface

reflectivity data to be 70.5–5.0% (ASTER, 2010). One hundred and

fifty buildings with rooftops larger than 6500 ft2 (604 m2) were

selected to validate the ranges of reflectivity measurement

generated by the proposed method. Albedo estimates for these

150 buildings calculated using this new approach were compared

to the albedo results for the same rooftops using processed ASTER

image data, which is generally accepted as a proven method for

indirect surface reflectivity measurement. Fig. 4 shows that the

correlation of the proposed method is in an acceptable range, with

a regression data analysis (R2 value of 0.9746) for the albedo

measurements. This indicates that the proposed method based on

Quickbird and GIS data analysis will produce similar albedo

results to those obtained using the conventional ASTER approach,

but with a much higher resolution. The higher resolution made

possible by applying the GIS building shapefiles allows averaging

of only the area within building rooftops. By incorporating GIS

data in Definiens DeveloperTM, the captured rooftop surface

reflectivities can be exported to ArcGISTM for further analysis of

energy and financial benefits.

Fig. 3. Integrating GIS building shapefiles into a Quickbird image.

R2

= 0.9746

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80

QuickBird Albedo

A S T E R A l b e d o

Fig. 4. Comparison of albedo measurements from Quickbird and ASTER data.







3.2. Estimating the available roof area for cool roof systems

More than 70% of the commercial and governmental buildings

in the case study area have an albedo between 0.2 and 0.4 and

could thus be improved by incorporating cool roof systems. The

932 buildings were categorized in 26 groups based on the size of

their rooftop areas, ranging from 1000 SF (93 m2) to 56,000 SF

(5203 m2), as presented in Fig. 5. All the case study buildings were

built between 1985 and 1999. The prototype commercialbuildings were modeled and HVAC systems were selected based

on ASHRAE 90.1-1999 in order to estimate their annual energy

consumption and potential savings from implementing cool roof

systems. We have compared the GIS building information such as

building structure and built date and we addressed the actual

commercial buildings in the case study area in terms of wall/roof

insulation and internal loads as the buildings were built based on

the ASHRAE requirements. In addition, the case study buildings

were categorized into office building (64%), retail store (23%), and

other (13%) including schools, hospitals, and mixed-use buildings

for the building energy simulations.

This study utilized an EnergyPlusTM simulation to assess

energy consumption, peak-time electricity demand, and the

equivalent GHG emissions of the buildings in the case studyarea. EnergyPlusTM was selected as the simulation platform for

implementing the cool roof model because of its ability to model

the hourly energy consumption of a building given user-specified

construction, internal loads, schedules, and weather (US DOE,

2009). Typical Meteorological Year (TMY3) weather data, includ-

ing temperature, humidity, wind, and solar radiation from the Sky

Harbor Airport weather station in Phoenix was utilized. Although

the building heights in the case study area (84%—one story

buildings and 16%—two or more) were considered in the

simulation modeling, we were unable to characterize each

building’s detailed design characteristics such as glazing fraction

and lighting density. However, after modeling these prototype

buildings, we confirmed the baseline models’ total electrical

consumptions prior to cool roof retrofits stay within 77% as

compared to the actual electrical demand data gathered from the

local utility company. This validates the current electrical loads of

the buildings modeled in EnergyPlusTM and we believe that

subsequent electrical demand reductions after building retrofits

can be accurately quantified as the remaining building character-

istics stay the same but rooftop reflectivity change.

Each building group by size and type was modeled based on

prototype commercial building information, with various albedo

changes from 0.2 to 0.6 in increments of 0.1. The albedo of cool

roofing material varies depending on the type of material and its

age. For example, the albedo measured pre-coating ranges from

0.16 to 0.24 for conventional dark roofing material; the albedo of

newly coated white surfaces can be as high as 0.6; the albedo of older, unwashed white surfaces ranges from 0.47 to 0.56, and the

albedo of washed white surfaces goes up to 0.59 ( Konopacki et al.,

1998). Therefore, an assumption was made to set the highest

average albedo to be 0.6. Additional insulation is often installed at

the same time as a cool roofing project for both new and existing

roofs, which further reduces the building’s cooling demand during

the summer. However, for the purposes of this study, reflectivity

was assumed to be the only change during a cool roof installation

for the buildings in the case study area. Therefore, cool roofing

products that do not change the existing insulation structure of

the building were chosen for this study’s financial analysis, which

primarily consisted of a white coating or spray applied on the

rooftops.

Fig. 5 presents the potential electricity savings and reduction

in cooling demand due to an albedo improvement from 0.2 to 0.6

on the study’s 26 building groups by size. Not surprisingly, larger

rooftops can accrue greater energy savings, both in quantity and

rate. The electrical demand for cooling can be reduced up to 13%

by incorporating cool roof technology with white coating

application. If additional insulation is installed simultaneously,

the cooling demand can be reduced still further. However, it is

important to note that various sizes of buildings must be

considered in order to assess the urban scale energy benefits

that can be achieved by implementing cool roof technology, as the

preliminary results shown in Fig. 5 indicate. Estimating the

potential energy savings through modeling a prototype building

of a particular size and then simply multiplying this by the size of

each building to obtain the electricity saving, as was done in

several prior studies such as Akbari and Konopacki (2004), is

likely to produce very misleading results as this approach neglects

the non-linear relationship between energy consumption and

building size.

3.3. Financial analysis

3.3.1. Electricity charges and utility cost savings

Most power utilities consider On-Peak demand to be between

11 am and 9 pm, and Off-Peak demand from 9 pm to 11 am.

Implementing cool roof systems in the case study area is likely to

have an impact on the total demand throughout the day,

especially in summer when the cooling demand during On-Peakhours is very high. Peak energy demand is also likely to decrease

significantly, where maximum On-Peak demand refers to the

highest power demand over a 15 min interval during the month.

In order to evaluate the financial benefits of this energy reduction,

we applied the net energy balance results to Arizona Public

Service (APS) Rate Schedule E-32 Time of Use (TOU). This is the

rate schedule used for large commercial customers with greater

than 20 kW of demand (APS, 2009). The rate schedule is broken

into Energy Charge and Demand Charge; Energy Charge is applied

to the total kWh used consumed each month, while Demand

Charge is applied to the maximum demand for periods lasting

longer than 15 min in the month. In the TOU applied here, there

are two maximum peak demands, one of which occurs during On-

Peak hours and the other during Off-Peak hours (Table 1).

0

20

40

60

80

100

120

140

1 k

2 k

3 k

4 k

5 k

6 k

7 k

8 k

9 k

1 0 k

1 1 k

1 2 k

1 3 k

1 4 k

1 5

k

1 6 k

1 7

k

1 9 k

2 3 k

2 6 k

3 2 k

3 4 k

3 8 k

4 0 k

4 5

k

5 6 k

Building Rooftop Area (SF)

E l e c t r i c i t y R e d u

c t i o n ( M W h )

0.00%

2.00%

4.00%

6.00%

8.00%

10.00%

12.00%

14.00%

Electricity Reduction (MWh)

% of Cooling Demand Reduction

Fig. 5. Reduction in electrical energy (MWh) and percentage of cooling demand as

a result of cool roof application (albedo change 0.2–0.6) using EnergyPlusTM

.







These numbers were then applied to the utility energy rate

section in the EnergyPlusTM simulation model for two cases:

(1) BASE CASE: Total buildings’ electricity consumption with

existing roof condition

(2) PROPOSED COOL ROOF APPLICATIONS: Total buildings’ elec-tricity consumption with cool roof applications.

The results of these simulation models reveal that the

implementation of cool roof systems has the potential to save

annual energy costs, considering both electricity for cooling and

gas for heating, by up to 4.5% of annual energy costs simply by

improving the reflectivity on rooftops. It is clear that a cool roof

system can reduce the utility costs, especially for regions where

cooling demand is dominant. Indirect benefits from shaving the

peak demand include reduced water consumption and GHG

emissions associated with the operation of conventional thermo-

dynamic power plants, and these are also quantified and

discussed below.

3.3.2. Construction cost of cool roof application

Lawrence Berkeley National Laboratory (LBNL, 2009) provides

a comprehensive database for cool roofing materials that includes

detailed technical information. However, it does not provide cost

information due to the uncertainties associated with product

pricing in different regions. We contacted 20 roofing contractors,

all of whom were members of the Arizona Cool Roofing Council, to

estimate the product and installation costs of various cool roofing

systems based on the availability of cool roofing systems in the

region. We then selected high-end and low-end prices for the

installation of white coating cool roof systems for the existing

buildings and these costs were incorporated as inputs into the

two cost benefit analysis (CBA) scenarios in the next section. As

mentioned in the technical potential assessment method section,we selected a white coating cool roof system that is not associated

with additional insulation in order to highlight the impact of

albedo change alone on the rooftops to avoid the added

complexity involved when cool roofing is applied with additional

insulation structure to the existing rooftops (Table 2).

3.3.3. Cost benefit analysis

A 20-year cost benefit analysis (CBA) was conducted to

determine the return on investment (ROI) for the new cool roof

construction on the commercial and governmental buildings in

the case study area. Electricity cost information from the local

utility provider and cool roofing system costs from roofing

contractors in the region were collected for the CBA inputs, as

presented in Table 3.

The CBA method applied here was adopted from that used in

the Life-Cycle Cost Analysis published by US Department of

Commerce (US DOC, 2008) and modified for buildings thatemploy cool roof systems. Software provided by the DOE Federal

Energy Management Program (FEMP) was used to calculate the

current nominal energy escalation rate (d) (US DOE, 2008). The

base-year energy costs were escalated from year to year at rates

projected by the Energy Information Administration (EIA, 2007) to

arrive at the total energy cost over a given period.

Two CBAs were conducted based on the low-end and high-end

costs for the cool roof system installation of white coating

material for this study.

It is important to note that several factors could change the

financial analysis over time. The cost of cool roofing systems ($/

SF) varies depending on the types of materials used, such as white

coating, form roofing, and single-ply membrane, as well as the

size of the project. There is also a tendency for the reflectivity of these roofs to degrade over time due to dirt build up and sun

damage, thus affecting the efficiency of the cool roof system.

Research, however, has shown that reflectivity can be restored up

to 90–100% of its original value by regular washing ( Akbari and

Konopacki, 2004), and this was therefore included as a main-

tenance cost in the CBA conducted for this study.

3.4. Evaluating indirect benefits related to urban sustainability

issues

The indirect benefits of cool roof regional application have

been extensively presented in prior works. Akbari and Konopacki

(2004) quantified the contribution of cool roof systems to UHI

mitigation, modeling changes in the air temperature at both the

Table 1

Time of use (TOU) utility electricity rate for large commercial buildings in the

study area.

Energy charge

On-Peak 11 am–9 pm Off-Peak 9 pm–11 am

Summer Winter Summer Winter

$0.063 per kWh $0.048 per kWh $0.050 per kWh $0.035 per kWh

Demand charge

$12.00 per kW for the first 100 On-Peak kW, plus

$8.17 per kW for all additional On-Peak kW

$4.26 per kW for first 100 Off-Peak kW, plus

$2.49 per kW for additional Off-Peak kW

Table 2

Typical cost of installing cool roof systems in Phoenix, Arizona.

Type of cool roof system System costs ($ per SF)a

White roof coatings 1.50–2.50

White roofing membranes 2.75–3.75

White asphalt shingles 3.00–4.50

White roofing tiles 5.50–7.50

a 1 SF¼0.093 m2.

Table 3

Cost benefit analysis inputs and variables.

Low end High end

Cost of cool roof retrofit ($/SF) $3,500,000

($1.50/SF)a$5,900,000

($2.50/SF)a

Retrofit area, 1000 SF

(92.9 m2)

2399 2399

Energy saved (MWh) 7830 7830

Operating weeks/year 52 52

Days/week 7 7

Hours/day 12 12

Annual savings $165,100 $165,100

Discount rate (DR)b b 0.03 0.03General inflations rate (GIR)c g 0.03 0.03

Energy inflation rate (EIR)b d 0.065 0.065

Study period (Years) 20 20

Annual maintenance (%) 1 1

a 1 SF¼0.093 m2.b Discount rate and energy escalation rate were calculated based on the

energy price indices and discount factors published by US Department of

Commerce (US DOC, 2008). Nominal escalation rate was used in the study, which

includes inflation. dnominal¼(1+ dreal)(1+ginflation)À1.c General inflation rate was adopted by the latest Consumer Price Index (CPI)

data published by the Bureau of Labor Statistics (US BLS, 2009).



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building and the regional level. Akbari et al. (2009) also examined

the effect of surface reflectivity change on global warming by

converting the energy savings into a reduction in carbon

emissions. Although our study focused primarily on the quanti-

fication of current albedo and the electricity savings that can be

achieved by installing a cool roof system for the buildings in the

case study area, the indirect benefits of a cool roof system such as

a decrease in the peak-time electricity demand and consequent

mitigation of environmental impacts such as GHG and reducedwater use were quantified based on the simulation results,

building on the findings in our prior cool roof study on a single

commercial building ( Jo et al., 2010).

4. Results

4.1. Current albedo and improvement potential

Reflectivity on the 932 building rooftops in the study area was

measured through the proposed method by integrating the

QuickBird image data with the building shapefile of the city. As

presented in Fig. 6, 677 buildings (73%) currently have rooftop

reflectivities below 0.4, and this can be improved to 0.6 by

applying a reflective roof coating product.

4.2. Energy reduction from cool roof applications

Fig. 5 shows the results of the EnergyPlusTM simulations of the

electricity reduction potential of the buildings in different size

groups by increasing roof surface albedo from 0.2 to 0.6. These

simulation results were used to quantify the energy savings that

could be achieved for 677 of the buildings in the study area by

applying reflective coating on their rooftops, and the results of

this calculation revealed a reduction in the total electricity

consumption of 7830 MWh. This amounts to a 4.3% decrease in

electricity demand in the case study area compared to the average

annual average electricity demand from 2006 to 2008 of 182,230 MWh.

4.3. Cost benefit analysis

By applying the estimated energy cost savings obtained from

the EnergyPlusTM simulation and the cool roof construction costs

provided by the roofing contractors in the study area, we

calculated the return on investment (ROI) based on the net

present value (NPV), taking into account the general inflation rate

(GIR), the energy escalation rate (EER), and the depreciation rate

(DR). The effect of electricity rate inflation was assumed to

encompass future price rate increases by including the energy

inflation rate (Black, 2008). Although annual savings can be

calculated from the EnergyPlusTM simulation results using current

electricity costs, the NPV was necessary in order to consider

actual savings in the future as it is a standard method for taking

into account the time value of money to assess long-term projects(US DOC, 2008). As shown in Table 4, the NPV will exceed the cost

for the white coating on the rooftops of the commercial and

governmental buildings in the case study area in 7 years with a

low-end cool roof cost assumption. This rises to 11 years with a

high-end cool roof cost assumption, which is still significantly less

than the 15–20 year life expectancy of a conventional roof

membrane.

4.4. Indirect benefits

In addition to the direct benefit of electricity savings, several

indirect benefits result from the urban scale installation of cool

roof systems, including reductions in peak electrical demand and

GHG emissions, reduced water consumption at thermoelectric

power plants, and a prevention of new power plant need.

Fig. 7 represents the potential reductions in peak electrical

power demand of buildings grouped according to their different

sizes, ranging from 1000 SF (93 m2) to 56,000 SF (5203 m2).

Improving albedo from 0.2 to 0.6 can reduce peak electrical

demand by 0.3, 2.6, and 10.5 kW for buildings with footprints of

1000, 10,000, and 40,000 SF, respectively. When cool roof systems

are applied to the 677 commercial and governmental buildings

that have albedo improvement potential in the case study area,

peak-time electrical demand can be reduced by up to 555 kW.

In arid regions such as the desert Southwest in the US and in

regions across the globe that are facing multi-year droughts, an

assured water supply and good water quality are a high priority

(Golden et al., 2006). Based on actual figures for industryelectricity generation by energy source in Arizona (Sailor and

Pavlova, 2003) and Arizona thermoelectric mean water balance

data (Golden et al., 2006), implementation of the cool roof

Number of Buildings by Albedo

26 buildings(3%)49 buildings

(5%)

125 builidngs

(13%) 301 buildings(32%)

81 buildings(9%)

350 buildings(38%)

Below 0.2

0.2-0.3

0.3-0.4

0.4-0.5

0.5-0.6

Above 0.6

Fig. 6. Number of buildings by albedo classification in the case study area.

Table 4

Cost benefit analysis of cool roof construction with a low-end cost assumption.

Year Maint. Energy savings Net benefit NPVa

0

1 ($35,875) $506,791 $470,916 $457,200

2 ($36,951) $539,732 $502,781 $931,119

3 ($38,060) $574,815 $536,755 $1,422,326

4 ($39,202) $612,178 $572,976 $1,931,408

5 ($40,378) $651,969 $611,592 $2,458,973

6 ($41,589) $694,347 $652,758 $3,005,6487 ($42,837) $739,480 $696,643 $3,572,083

8 ($44,122) $787,546 $743,425 $4,158,949

9 ($45,445) $838,737 $793,291 $4,766,940

10 ($46,809) $893,255 $846,446 $5,396,776

11 ($48,213) $951,316 $903,103 $6,049,197

12 ($49,659) $1,013,152 $963,492 $6,724,971

13 ($51,149) $1,079,007 $1,027,857 $7,424,892

14 ($52,684) $1,149,142 $1,096,458 $8,149,780

15 ($54,264) $1,223,836 $1,169,572 $8,900,484

16 ($55,892) $1,303,386 $1,247,494 $9,677,880

17 ($57,569) $1,388,106 $1,330,537 $10,482,877

18 ($59,296) $1,478,333 $1,419,037 $11,316,412

19 ($61,075) $1,574,424 $1,513,349 $12,179,454

20 ($62,907) $1,676,762 $1,613,855 $13,073,006

a NPV (net present value) was calculated base on general inflation rate (GIR),

energy inflation rate (EIR), and discount rate (DR).



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systems in the study area would reduce power plant waterconsumption by up to 4908 kgal/year.

The resultant polluting emissions due to electrical power

generation for the case study buildings, as well as the potential

reduction due to the cool roof application, were calculated based on

the modeling results. As shown in Table 5, implementing the cool

roof systems in the case study area would result in reductions of

3823 tons of carbon dioxide (CO2), 5.29 tons of nitrogen oxide (NO x),

and 3.52 tons of sulfur dioxide (SO2) emissions annually. By

incorporating the proposed method here, we can thus estimate the

potential energy savings and other indirect benefits that can be

achieved by adopting cool roof systems for the entire region.

The cool roof strategy has additional implications. Energy

savings from the cool roof applications offer not only GHG-

reduction opportunities in the city, but also new ways to address

the UHI effect and energy security problems ( Jo et al., 2009). The

use of cool roof strategy reduces solar heat gain in building roofs

which can decrease energy needed to meet cooling demand. In

addition, these roofing systems have shown to decrease air

temperatures near the roof surface which lowers their contribu-

tion to the UHI effect (Levinson et al., 2005), which eventually will

reduce human health vulnerability from excessive heat stress on

the building structure.

5. Conclusions and future research

The benefits of cool roof systems are well understood in the

industry and a handful of cool roofing materials are available, but

as yet few methods are available to assess urban scale impacts of

cool roof applications. This study therefore developed a new

research methodology that combines the use of remote sensing

image data and a building energy computer simulation to

quantify the current rooftop reflectivity and the potential benefits

of albedo improvement of the 932 commercial and government

buildings in the study area. The results of this analysis showed

that 73% (677 buildings) have reflectivities below 0.4 where cool

roof systems can be applied to improve energy efficiency. The coolroof system applications will result in an electricity reduction of

4.3% (7830 MWh) annually in the case study area. In addition to

the direct electricity savings, cool roof systems can shave peak

electrical demand by 555 kW in August when the peak demand is

the highest in the case study area. Although this number is

relatively small compared to typical power plant capacities, which

range from 340 to 4000 MW, if cool roof systems are incorporated

over the entire metropolitan area, this will help reduce the load

requirement of the power plants in the region, especially at

periods of peak demand. The study also assessed the environ-

mental benefits associated with the GHG emissions, and the cool

roof system applications in the case study area will result in

reductions of 3823 tons of carbon dioxide (CO2), 5.29 tons of

nitrogen oxide (NO x), and 3.52 tons of sulfur dioxide (SO2

)

emissions annually.

Based on the CBA performed for this study based on an annual

electricity reduction of 7830 MWh, the payback period for the

low-end cost scenario is 7 and 11 years with the high-end cost

scenario, both considerably less than the 15–20 year life

expectancy of a conventional roof membrane. Cool roof systems

may also further extend this life expectancy (Akbari and

Konopacki, 2004).

This study focused primarily on the direct and indirect benefits

of urban scale cool roof system applications. Further research,

however, is required to evaluate other environmental benefits of

the widespread use of cool roof technology throughout the region,

including the accompanying reductions in the UHI effect and the

benefits of reduced human health vulnerability from excessive

heat stress on the building structure, as was amply demonstrated

in the 1995 Chicago heat wave incident (Klinenberg, 2002).

Acknowledgements

This work was supported by the Center for Renewable Energy

at Illinois State University and the National Center of Excellence

on SMART Innovations for Urban Climate and Energy at Arizona

State University.

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1 k 3 k 5 k 7 k 9 k 1 1 k

1 3 k

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