Arboriculture & Urban Forestry 38(3): May 2012 85 Table 4. Model parameters and probability distributions used in the sensitivity analysis of EAB economic impacts. Currency is expressed in 2010 Canadian dollars. Parameter name Total trees/km % Ash % Ash - small % Ash - medium % Ash - large Small ash/km Medium ash/km Large ash/km Removal - small ($) Removal - medium ($) Removal - large ($) Replacement ($) Replacement rate (%) Treatment - large ($) Treatment - medium ($) Community cost ($ per household) Detection lag (years) Region Eastern Eastern Eastern Eastern Eastern Western Western Western Canada Canada Canada Canada Canada Canada Canada Canada Canada Distribution type Gaussian Gaussian Gaussian Gaussian Gaussian Gaussian Gaussian Gaussian Triangular Triangular Triangular Triangular Triangular Triangular Triangular Triangular Discrete uniform foresters, it was estimated that these costs would be approximately $0.40/household – applied in each year that an outbreak was on- going in a given city. The number of households in each commun- ity was obtained from Statistics Canada (Statistics Canada 2007). Three different positive discount rates were employed: 2%, 4%, and 10%. These rates reflect different perspectives on the value of delaying payment for incurred costs. In addition, re- sults are presented with no discounting (a zero discount rate), to demonstrate the effect of discounting. Some economists provide theoretical arguments that very low discount rates are justifiable when significant intergenerational outcomes are at stake; species losses could arguably be taken as one such out- come (Weitzman 1994; Portney and Weyant 1999). For the positive discount rates, the authors also report the cost esti- mates in equivalent annual dollars (see Boardman et al. 2001). Model Scenarios and Sensitivity Analysis The model was run for 36 different combinations of spread rate (slow, medium, and fast), treatment rate (0%, 10%, and 50%), and discount rate (0%, 2%, 4%, and 10%). As with any model, there was uncertainty in the input parameters; to address this, 100 Monte Carlo simulations were run for each of the 36 scenario combinations using the @Risk software package (Pallisade Cor- poration 2002). During each simulation, the value for each input parameter was drawn from a user-defined distribution of possible values. Since there were multiple estimates of the tree composi- tion parameters for eastern and western Canada, a Gaussian dis- tribution for each parameter was defined using mean and standard deviation values calculated from the data (Table 4). Due to the rel- atively small amount of empirical data behind the remaining input parameters, they were assigned a triangular distribution for the Monte Carlo simulations. This distribution requires knowledge of mean, min, and max values, and assumes only a simple triangular shape. Plots of cumulative mean cost against simulation number indicated that 100 replications were adequate for this analysis. Distribution parameters Mean = 108; S.D. = 30 Mean = 0.06; S.D. = 0.04 Mean = 0.1; S.D. = 0.1 Mean = 0.35; S.D. = 0.3 Mean = 0.6; S.D. = 0.3 Mean = 9; S.D. = 7 Mean = 20; S.D. = 12 Mean = 33; S.D. = 19 Mean = 150; Min = 50; Max = 250 Mean = 500; Min = 300; Max = 700 Mean = 1000; Min = 700; Max = 1300 Mean = 400; Min = 250; Max = 550 Mean = 0.5; Min = 0.2; Max = 0.8 Mean = 165; Min = 115; Max = 215 Mean = 110; Min = 60; Max = 160 Mean = 0.4; Min = 0.2; Max = 0.6 Uniform (2,3,4) The influence of each input parameter listed in Table 4 on regional and total EAB economic impact was estimated us- ing a regression approach (Pallisade Corporation 2002). For this analysis, each iteration of the simulation produced an ob- servation for a multiple regression model with cost as the de- pendent variable and the input parameters as the independent variables. The standardized slope coefficient associated with each input parameter was taken as its measure of influence. RESULTS Overall Economic Impact Approximately 545,000 and 684,000 ash street trees were esti- mated in eastern and western Canada, respectively, for a total of ~1.2 million ash street trees across the 641 communities included in the study area (Table 1). Estimated impacts for the 30-year time horizon ranged from $265 million to $1,177 million depending on the combination of spread, treatment, and discount rates (Ta- ble 5). The low estimate resulted from the slow spread rate, 10% discount rate, and 50% treatment rate; the high estimate resulted from the fast spread rate, 0% discount rate and 50% treatment rate. Figure 2 shows cost accumulation through time for selected spread and treatment rates. These estimates are for street trees only; the inclusion of expenses associated with backyard trees can be roughly estimated by multiplying the values in Table 5 by a factor of 1.7, bringing the range to $451 million to $2,001 mil- lion. Total costs associated with a "middle-of-the-road" scenario (i.e., medium spread rate, 10% treatment rate, and 4% discount rate) were $524 million; this would increase to roughly $890.8 million if expenses related to backyard trees were included. As would be expected, faster spread rates were associated with higher economic impacts (Table 5). For example, total street tree costs ranged from $265 million to $506 million (at posi- tive discount rates) for the slow spread rate compared, to $371 million to $820 million for the fast spread rate (Table 5). These ©2012 International Society of Arboriculture
May 2012
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