VARIABLE RANKINGS IN COST-BENEFITS FROM INSTALLING GREEN ROOFS IN TEN CITIES IN THE UNITED STATES

Transcription

1 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 2014

2 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 i

3 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 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 ii

4 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 costbenefits 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. iii

5 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 ft 2 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 costbenefits 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. 1

6 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, 2

7 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%. 3

8 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 40,000m 2 of conventional roofs with green roofs. They expected that green roofs of 40,000m 2 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 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 $/m² Repairs (Interval in Years) Every 10 years Renovations Costs during 90 years ($/m²) Reconstruction Costs ($/m²) Disposal and Recycling Costs ($/m²) 20 0 Sum ($/m²) 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 4

9 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 = T Bt t=0 (1+r) t (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 5

10 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 m 2 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. 6

11 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 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 7

12 and Join tools were used to calculate developed areas within a city. Cost-Benefits of Green roofs Electric costs for cooling energy per m 2 Electric costs for cooling energy per m 2 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 m 2. 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 trillion kwh, and annual total cooling consumption was trillion kwh in 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 m 2. Annual total electric consumption per m 2 was kwh/m 2. Thus, 13.5% of kwh/m 2 was estimated as annual cooling consumption per m 2. Table 2. Electricity prices and annual electric costs for cooling energy of the ten cities. Source: EIA 2014 City Electricity Price ($/m 2 ) Electric Costs for Cooling ($/m 2 ) New York, NY Los Angeles, CA Chicago, IL Houston, TX Philadelphia, PA Phoenix, AZ San Antonio, TX San Diego, CA Dallas, TX San Jose, CA

13 Annual cooling consumption per m 2 in the United Sates was calculated as kwh/m 2, and this electric consumption and electricity prices of ten city were applied to calculate annual cooling consumption per m 2 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 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 (m 2 ) Electric costs for cooling energy per m 2 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, 9

14 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/m 2 ) from reducing the UHI by installing green roofs. Source: Taha et al City Annual Electric Savings Annual Electric Savings (kwh/m 2 City ) (kwh/m 2 ) 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 (m 2 ) Annual Electric Savings (kwh/m 2 ) 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 10

15 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 / m³) (3) In this equation, m³indicates 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 m 2 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) 11

16 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 km 2 in each city will be the planning area to install green roofs because the calculated developed area in Philadelphia was 314 km 2, 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, 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 km 2 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 km 2 have costbenefits 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 km 2 in the city (5) 12

17 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 costbenefit 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 ,099 Philadelphia, PA ,386 Chicago, IL ,214 Los Angeles, CA ,001 San Jose, CA ,425 San Diego, CA ,330 Phoenix, AZ ,432 Dallas, TX ,301 Houston, TX ,529 San Antonio, TX ,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 km 2. For example, Philadelphia has the effective area to install green roofs in the area in 18% of 100 km 2. This study also assumed that people who are house owners 13

18 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 km 2 in ten cities. Source: American Fact Finder 2014 City Multi-family over 5 Units Effective Areas to Install in 100 km 2 (%) (km 2 ) New York, NY Los Angeles, CA Chicago, IL Houston, TX Philadelphia, PA Phoenix, AZ San Antonio, TX San Diego, CA Dallas, TX San Jose, CA 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 14

19 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 m 2 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 m 2. 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 seconddensest 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 Developed Developed Area per Area (km 2 ) Area (km 2 ) One Person (m 2 ) 1 New York, NY 8,405,837 1, Philadelphia, PA 1,553, Chicago, IL 2,718, Los Angeles, CA 3,884,307 1,239 1, San Jose, CA 998, San Diego, CA 1,355, Phoenix, AZ 1,513,367 1, Dallas, TX 1,257,676 1, Houston, TX 2,195,914 1,727 1, San Antonio, TX 1,409,019 1, Average 2,529,250 1,

20 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 km 2 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 km 2 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 16

21 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 km 2 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 km 2 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 km 2 has -$1,458,198,088 because of their 17

22 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 km 2 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. 18

23 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 costbenefits 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 km 2. 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 19

24 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 km 2 with green roofs, (2) developed areas per one person, and (3) the number of people within developed areas of 100 km 2. City Total One Year Cost- Developed Areas per The Number of People within Benefits One Person (m 2 ) Developed Areas of 100 km 2 Ranking $ Ranking m 2 Ranking Number of People San Jose, CA 1 323,573, ,427 Los Angeles, CA 2 322,374, ,185 New York, NY 3 320,009, ,202,501 San Diego, CA 4 315,178, ,810 Houston, TX 5 294,714, ,241 Philadelphia, PA 6 274,069, ,633 Dallas, TX 7 268,928, ,924 San Antonio, TX 8 255,735, ,460 Phoenix, AZ 9 253,702, ,128 Chicago, IL ,264, ,209 In order to calculate cost-benefits of green roofs depending on population density, this study assumed that people within developed areas of 100 km 2 have the same cost-benefits from installing green roofs. This means that the 289,427 people in San Jose respectively have costbenefits of $ 323,573,897. Figure 5 compares two cost-benefits: (1) cost-benefits with replacing conventional roof areas of 100 km 2 in the ten cities with green roofs and (2) total cost-benefits of people who live within developed areas in 100 km 2. New York has the highest total cost-benefits of people from installing green roofs within developed areas in 100 km 2 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 costbenefits 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 20

25 One year total cost-benefits ($) Cost-benefits of people within 100 km² ($1,000,000,000) roofs. One year total cost-benefits Cost-benefits of people within 100 km² 350,000, ,000, ,000, ,000, ,000, ,000,000 50,000, , , , , , , , ,000 50,000 0 City Figure 5. The comparison of one-year cost-benefits and total cost-benefits of people within 100 km 2. Population Demographics and City Structures to Install Green Roofs Ages, house owners, and city structures of the ten cities were considered to calculate costbenefits 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. 21

26 Cost-benefits ($) 3,000,000,000 2,500,000,000 2,000,000,000 1,500,000,000 1,000,000, ,000,000 0 City 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. 22

27 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 km 2 in the ten cities with green roofs with considerations of electricity price and climate, (2) B is the NPVs of cost-benefits of 100 km 2 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 23

28 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 secondhighest 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 costbenefits 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. Ranking City Areas of Existing Potential Cost-Benefits Income Green Roofs from Installing Green Roofs Chicago, IL New York, NY Philadelphia, PA Houston, TX Los Angeles, CA San Diego, CA San Jose, CA Phoenix, AZ San Antonio, TX Dallas, TX

29 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 km 2 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 costbenefits 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 25

30 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. 26

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