198 Laband and Sophocleus: Impact of Tree Shade on Electricity Consumption If the amount of shade cast is a significant predictor of shade- related energy savings, a logical implication is that type of tree (species, shape, height) will be an important consideration with respect to determining the extent and density of shade cast and therefore the extent of possible energy savings. The relationship between species composition and shade-related energy savings has been investigated by DeWalle et al. (1983), who measured energy used for heating/cooling in mobile homes surrounded by deciduous trees versus pine trees in the state of Pennsylvania. They found that deciduous trees contributed significant energy savings for summertime cooling while the presence of pine trees increased wintertime heating costs more than the presence of deciduous trees. However, their analysis focused exclusively on mobile homes, where the relatively poor insulation should imply relatively large energy savings from the presence of shade trees. McPherson and Dougherty (1989) investigated both en- ergy savings from shade-related cooling of houses and water use for several species of trees in Tucson, Arizo- na. They report that both tree shape and crown density have significant impacts on shade-related energy savings. Jensen et al. (2003) used remote sensing data to mea- sure Leaf Area Index (LAI) at 118 randomly-selected loca- tions in Terre Haute, Indiana. They regressed residential en- ergy consumption against LAI values and failed to find a statistically significant relationship. These results are in con- trast to the strong and significant impact of shade trees on residential energy consumption identified by other studies. METHODS The general methodology for this study was to compare elec- tricity consumption used to maintain a constant tempera- ture in two otherwise identical buildings, situated in differ- ent shade conditions. This would permit one to estimate with a high degree of accuracy the cooling impact of shade trees, untainted by confounding effects. Specifically, testing wheth- er the electricity required to cool a building situated in full sun was significantly greater than the electricity required to cool an otherwise similar building located in heavy shade. The study authors acquired two 10 ft x 16 ft (3 m x 4.9 m) storage sheds with identical construction specifications. Each building was manufactured with a dark gray, shingled roof that included a ridge vent and six windows. Once on-site, both buildings were fully insulated with R-13 batting. In addition, linoleum was installed over the plywood floors and the cor- ners were secured with quarter-round molding. The intent was to create, in effect, miniature residences. Finally, each build- ing was wired with electrical current with installed outlets. These buildings were located on a property in Beauregard, Alabama, U.S. at 32.534283°N Latitude and 85.356333°W Lon- gitude. One building was located in full sunshine (Apollo); the other was located approximately 130 (40 m) feet away in dense shade (Hades). Specific information with respect to the light con- ditions on each building is presented in Appendix 1. In such close proximity, it seems unlikely that the differences in cooling effort reported result from significant differences in local climatic con- ditions (e.g., cloud cover, thermals). The two buildings were situ- ated in the same spatial configuration relative to the arc of the sun. Identical window air conditioning units were installed in each building. The air conditioners were Sears—Model number: ©2009 International Society of Arboriculture 580.75051, Cooling capacity: 5,300 BTU, Watts: 490, EER: 10.8 BTU/hr, Volts: 115; 60Hz, Amps: 4.6 (single phase). The thermostat on each was set to 72°F (22°C). These AC units were plugged directly into data loggers that record electricity use; readings were taken daily. This was the only draw on the electrical current supplied to each building. In addition, daily information was recorded on the minimum and maximum in- terior temperature using battery-operated recorders. These recorders were located slightly above and to the right of each AC unit, at a height of 5 ft (1.5 m) from ground level. Finally, the study used Hobo weather stations located 6 ft (1.8 m) due south of each building to collect information at 15-minute in- tervals on outside temperature, humidity, and light conditions. Independently of compressor-active cooling, the AC units used identical amounts of electricity per day to run their respec- tive fan motors and display the thermostat setting. Consistency in this regard was checked by observing the current draws in each building on a day when the temperature never exceeded 70°F (21°C) (thus, there was no compressor-driven cooling). Because the fan motors on the AC units ran continuously, the study au- thors identified the daily current draw for noncooling purposes (1.04 kW) and subtracted this number from the recorded dai- ly usage to derive the daily power used to cool each building. In terms of light conditions, the Hobo weather stations record- ed Photosynthetically Active Radiation (PAR) values, which is the spectral range of solar light from 400 to 700 nm that is needed by plants for photosynthesis (actinic UVA to infrared). Mean PAR at each building was calculated from the nonnighttime readings. Data collection was from April 1, 2008–September 17, 2008 (Appendix 1). Unfortunately, the temperature sensor on the Hobo station positioned next to Hades started malfunctioning in mid- July; as a result, the study does not have a complete series of temperature readings for said building. Because the study au- thors had incomplete data for Hades on external temperatures, there was an estimation of the statistical relationship between the external temperatures recorded at Apollo and those recorded at Hades. The study authors then used a regression model to assign estimated values for mean outside temperature and outside high temperature at Hades for the period July 18, 2008–September 17, 2008. For mean outside temperature, the estimated predic- tive model was: y = 2.089951 + 0.965314 Apollo temp. The re- gression R2 For mean high temperature, the estimated predictive model was: y = 4.0775544 + 0.912341 Apollo temp. The regression R2 value is 0.99869; the model F-statistic is 77,004.32. val- ue is 0.98235; the model F-statistic is 5620.895. Judging both by the R2 values and model F-statistics, these simple models have very high predictive ability. As a result, the study authors are confident that the imputed values for Hades are sufficiently accurate as to not compromise the integrity of their analysis. RESULTS It is quite evident that light conditions varied substantially be- tween Apollo and Hades. The t-tests of differences in mean PAR for each month were reported for the study period; all show that mean PAR was significantly higher in Apollo than Hades. Additional reporting noted t-tests of differences in elec- tricity used to cool each building for each month in our study period (Table 1). Again, consistent evidence was found, by month, that electricity consumption per day was significantly
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