Arboriculture & Urban Forestry 36(5): September 2010 231 world. In the meantime, users will obtain the best results from i-Tree Streets by selecting the refer- ence city that best matches their local conditions. The objective of this paper is to describe and demonstrate a systematic process for select- ing the “best fit” reference city. The paper be- gins with a background to the approach and de- scribes the selection criteria. This analysis will conclude with an illustration of the reference city selection process applied to Lisbon, Portugal. APPROACH Background Figure 1. i-Tree Streets climate zones were aggregated from 45 Sunset climate zones into sixteen zones. Each zone has a reference city where geographic and tree growth data were collected. million in new funds for urban forestry over the next ten years (Kling 2008). The National Tree Benefit Calculator (2009) com- bines tree benefit data from i-Tree Streets with a user-friendly interface to display the value of individual street trees (www. treebenefits.com). i-Tree Streets is being used to demonstrate the social, environmental, and economic value of investing in “green infrastructure,” and is generating many requests to expand ap- plication to Asian, European, Canadian, and Australian cities. One of the first questions i-Tree Streets users face is choice of climate zone. Once a zone is selected, the software loads a list of common tree species and benefit and cost data based on research conducted in that zone’s reference city. Other than dis- playing a map of the U.S. showing the sixteen climate zones, the interface does not provide guidance for determining which reference city to select. For example, what criteria should be used to select a climate zone when the subject city rests on the border of two zones? Which climate zone and reference city is the best match if the subject city has a substantially dif- ferent climate than the reference city because of elevation, lo- cation near a large water body, or other geographic features? It is recognized that relying on reference city data is a poor substitute for applying local data. Results are, at best, first-order approximations due to extrapolation of data from reference city to subject city. Inaccuracies can be magnified when i-Tree Streets is applied outside the U.S. However, the cost of conducting a ref- erence city analysis, an estimated $250,000 per city, makes it im- possible for every city to afford the accuracy obtained with the in- tensive reference city analysis. It is proposed that within the next few years i-Tree Streets and i-Tree Eco, a software application within i-Tree Streets, will be integrated into a single, turn-key program that contains geographic data for major cities around the The primary goal behind finding the best match is to produce benefit estimates that are as accurate as pos- sible. In other words, total benefits obtained using reference city X are closer to actual than obtained using reference city Y or Z. Because i-Tree Streets is a simulation model, its results can only approximate reality. Determining the magnitude and source of er- rors is difficult due to the complex collection of input data and simulation models. A systematic analysis of the sensitivity of i-Tree Streets’ output to the proba- bilistic range of possible input values has not been undertaken. However, all model inputs have been identified with errors grouped into six categories. 1. Sampling error. A sampling error expresses how well a tree sample reflects the actual tree population. There is no sampling error for a complete inventory. 2. Formulaic error. Errors of this type are related primarily to formulation and application of tree growth models. Within-class errors result from using dbh class midpoints to quantify benefits, when in reality tree sizes may be distributed throughout each size class. Tree size and growth errors are confidence intervals for each dimension that depict increasing variability with size. Species assignment errors result from matching species not sam- pled to one of the 22 species sampled in the reference city. The magnitude of this error depends on the proportion of population assigned, as well as goodness of fit in terms of matching sizes and annual growth for leaf area, dbh, and other size parameters. 3. Pricing errors. selection of values for These are errors concerning the pricing benefits and costs. 4. Resource unit errors. Resource unit or RU (engineering units such as cubic meters of rainfall intercepted), model errors are relat- ed to selection of input data, parameterization of individual models that produce RUs (e.g., building energy use, pollutant deposition, biomass formulas), choice of adjustment factors (e.g., adjacent shade, VOC emission rates), and their assignment to species. 5. Temporal errors. These are errors related to the selection of a particular year of input data. Differ- ent years may be used for different numerical models. 6. Spatial errors. Errors related to using data separat- ed by some distance from the region of interest, the use of point measurements of air quality concentrations, pre- cipitation, and other meteorological data for a large area. Sources of errors that pertain to reference city selection are formulaic: species assignment errors due to a lack of sampled tree species and inaccurate tree dimensions. Also, spatial errors result ©2010 International Society of Arboriculture
September 2010
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