352 Nowak et al.: Assessing Urban Forest Structure and Ecosystem Services The monetary value of pollution removal by trees is estimated using the median externality values for the United States for each pollutant. These values, in dollars per tonne (metric ton [mt]) are: NO2 $6,752 mt–1 ($6,127 t–1), PM10 $4,508 mt–1 ($4,091 t–1), SO2 $1,653 mt–1 ($1,500 t–1), and CO $959 mt–1 ($870 t–1) (Murray et al. 1994). Recently, these values were adjusted to 2007 values based on the producer’s price index (Capital District Planning Commission 2008) and are now (in dollars per metric ton [mt]): NO2 $9,906 mt–1 ($8,989 t–1), PM10$6,614 mt–1 ($6,002 t–1), SO2$2,425 mt–1 ($2,201 t–1), and CO $1,407 mt–1 ($1,277 t–1). Externality values for O3 are set to equal the value for NO2. Building Energy Effects This module estimates the effects of trees on building energy use and consequent emissions of carbon from power plants. Methods for these estimates are based on a report by McPherson and Simpson (1999). Distance and direction to the building is re- corded for each tree within 18.3 m (60 ft) of two- or one-story residential buildings. Any tree that is smaller than 6.1 m (20 ft) in height or farther than 18.3 m (60 ft) from a building is con- sidered to have no effect on building energy use. Using the tree size, distance, direction to building, climate region, leaf type (deciduous or evergreen), and percent cover of buildings and trees on the plot, the amount of carbon avoided from power plants as a result of the presence of trees is calcu- lated. The amount of carbon avoided is categorized into the amount of MWh (cooling) and MBtus and MWh (heating) avoided as a result of tree energy effects. Default energy effects per tree are set for each climate region, vintage building types (period of construction), tree size class, distance from building, energy use (heating or cooling), and/or leaf type (deciduous or evergreen) depending on the energy effect of the tree (tree shade, windbreak effects, and local climate effect) (McPherson and Simpson 1999). Default shading and climate effect values are applied to all trees; heating windbreak energy effects are as- signed to each evergreen tree. Because shading effect default values are given for only one vintage building type (post-1980), vintage adjustment factors (McPherson and Simpson 1999) are applied to obtain shading effect values for all other vintage types. Tree Condition Adjustment The default energy effect values (McPherson and Simpson 1999) are adjusted for the tree condition as follows: Energy adjustment = 0.5 +(0.5 × tree condition) where tree condition1 – % dieback. This adjustment factor is applied to all tree energy effects for cooling, but only evergreen trees for the heating energy use effects because deciduous trees are typically out of leaf during the heating season. Local Climate Effects The individual tree effect on climate diminishes as tree cover increases in an area, although the total effect of all trees can increase. Base climate effect values for a tree are given for plots of 10%, 30%, and 60% cover (McPherson and Simpson 1999). Interpolation formulas (McPherson and Simpson 1999) are used to determine the actual tree value based on the specific plot percent tree and building cover. For plots with less than 10% cover, the slope between the 10% and 30% cover values are used ©2008 International Society of Arboriculture for the interpolation. Plots with percent cover greater than 60% used the slope between 30% and 60% cover with a minimum individual tree climate effect of one-third the effect at 60% cover. This minimum is set to prevent a tree from obtaining a negative effect at high cover. The total shading, windbreak, and climate energy effects re- sulting from trees on a plot are calculated by summing the in- dividual tree’s energy effects for the particular energy use and housing vintage. These values are adjusted for the distribution of the different vintage types within the climate region (McPherson and Simpson 1999). Because the default cooling energy effects are determined based on the climate regions’ electricity emissions factors, it is necessary to convert the cooling energy effects to the state- specific equivalent. This conversion is accomplished by multi- plying the plot cooling energy effects by the ratio of the state- specific electricity emissions factor to the climate region’s elec- tricity emissions factor (McPherson and Simpson 1999). Home heating source distribution (e.g., fuel oil, heat pump, electricity, and natural gas) for the region is used to partition the carbon emissions from heating to the appropriate energy source. Standard conversion factors (t CO2/MWh, t CO2/MBtu) are used to convert the energy effect from t CO2 to units of energy saved (MBtus, MWh). Cooling and heating electricity use (MWh) had state-specific conversion factors; nonelectrical heating fuels (MBtus) used a standard conversion factor because this factor does not vary by region (McPherson and Simpson 1999). Total plot effects are combined to yield the total energy and associated carbon effect resulting from the urban forest. To determine the estimated economic impact of the change in building energy use, state average price per kWh between 1970 and 2002 (Energy Information Administration 2003a) and per MBtu for natural gas, residential fuel, and wood between 1990 and 2002 (Energy Information Administration 2003b, 2003c, 2003d, 2003e, 2003f) are used. All prices are adjusted to 2002 dollars using the consumer price index (U.S. Department of La- bor and Statistics 2003). State prices are used to determine the value of energy effects. Average price for heating change result- ing from trees is based on the average distribution of buildings in the region that heat by natural gas, fuel oil, and other (including wood) (McPherson and Simpson 1999). RESULTS Urban forest structure can vary among cities based on the local environment (e.g., forest versus desert), land use distribution, and population density (Nowak et al. 1996). Based on the analy- ses of 14 cities, the total number of trees in a city varied from 48,000 in Freehold, New Jersey, U.S. to 9.4 million in Atlanta, Georgia, U.S. (Table 4). Because size of city can significantly influence the total number of trees, tree density (trees per hect- are) yields a more standardized index of urban forest structure by which to compare cities. Tree density among the cities varied from 22.5 trees/ha (9.1 trees/ac) in Casper, Wyoming, to 275.8 trees/ha (111.6 trees/ac) in Atlanta (Table 4). Tree cover varied among cities from 8.9% in Casper to 36.7% in Atlanta. The most common species found in the 14 sampled cities include a mix of native and exotic species (Table 4). The estimated city leaf area index (total leaf area [one-sided]/city area) for trees across a city ranged from 0.3 in Casper to 2.2 in Atlanta (Table 4). Model results have also been used to estimate local or national urban tree effects on air pollution removal (Nowak et al. 2006a),
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