Arboriculture & Urban Forestry 36(3): March 2010 and density of tree shade at each property. The extent of tree- cast shade on the roof of each house was recorded on a decile percentage scale (0%, 1 to 10 = 10%, 11 to 20 = 20%…, 91 to 100 = 100%). Similarly, shade density type was recorded in one of four density classes: heavy, moderate, light, and no shade. Heavy shade density refers to shade characterized by few-to-no patches of sunlight, light shade density refers to shade that allows most of the sunlight shine onto the structure, and moderate shade density is characterized by roughly equal amounts of sunlight and shade upon the dwelling. The same researcher monitored the extent and density of shade cast on each house every time to ensure consistency and uniformity with respect to the data. Communication for Monthly Electricity Usage Information on monthly electricity usage from each partici- pating house was collected through e-mail and/or phone com- munication. An e-mail or telephone reminder was sent to each participant every month coinciding with their power bill ar- rival, requesting information on monthly power consumption. Data Description Questionnaire Survey Data Data on characteristics of the dwelling and the occupants were collected at the onset of the study using the survey questionnaire. The building characteristics included: age of house (years), liv- ing space (square feet), number of stories/levels, cooling system (central air or window unit), cooking/heating and hot water sys- tems (electricity, natural gas, or others), exterior construction ma- terials, presence or absence of swimming pool, and presence of an additional freezer. The occupant(s) characteristics included: total number of family members by age and gender, and average quantity of laundry run per week. In addition, information on the daytime and nighttime inside house temperature maintained by the residents both in summer and winter months was collected. In conjunction with information about exterior temperatures, this provided a measure of the intensity of the cooling (heating) regime at each residence across different seasons and months. Weather Data Assuming homogeneous outside weather conditions across sample households, the study authors collected daily tempera- ture and humidity data for the city of Auburn from the National Weather Service reporting station located at the Auburn air- port. Daily maximum and minimum readings for temperature and humidity were collected for 12 months and were used to compute daily averages. Then, periodic averages for tempera- ture and humidity were computed based on electricity service periods (i.e., the period between start and end dates of electric services mentioned in each utility bill), for each participating household. Service periods vary across the neighborhoods, even within the neighborhood if it is a large one, creating different start and end dates of the billing cycles among neighborhoods. For example, a house in the Twin Creeks neighborhood had a service period running from August 7 to September 8, whereas a house in the Willow Creek neighborhood had a service period that ran from August 12 to September 12. These weather-relat- ed variables serve as the basis to compare electricity consump- tion across sample households with varying service periods. 75 The intensity of artificial cooling (in summer) depends on the difference between inside and outside temperature of the house during the day or at night. The daytime, outside tempera- ture is relevant for summer months when cooling the house is done mostly during the day (when temperatures peak). The to- tal amount of electricity consumed for cooling depends on the magnitude of the difference between outside and inside tem- peratures (tempdiff), as this difference reflects the intensity of the cooling effort needed for a house. The higher the positive temperature difference during summer months, the greater the amount of electricity consumed to cool the house and vice-versa. ElectricityData Starting with August 2007, monthly electricity usage data were collected from each participating household for a year. Specifi- cally, information on dates of current service, number of days in service period, and the amount of electricity consumed during the specified period. Even though electricity usage data for 12 months (August 2007 – August 2008) were collected, the study authors were able to develop the power usage data for the 24-month pe- riod (August 2006 – August 2008). In each bill, the power com- panies report three sets of power usage information to the cus- tomers: the current month, the previous month, and previous year by number of days in the billing period and kilowatt hours (kWh) of consumption. Using this information a dependent variable (power consumption per day) for each household for both the current period and previous-year service periods was generated. Tree Shade Data Monthly data on the extent and density of tree-cast shade was recorded through field visits. The extent of shade estimated in decile percentages three times a day was averaged to obtain a mean percentage of shade on each house. Similarly, a single measure of shade density was constructed from the three den- sity observations taken at different times of the day. It was de- rived using a weighted scheme reflecting the extent and density of shade. For example, if a house received 15% heavy shade in the late morning, 5% moderate shade in early afternoon, and 55% heavy shade in the late afternoon, then the mean shade extent for this house was assigned at 25% and shade den- sity assigned was heavy. The shade density type was specified based on the frequency and extent of its periodic occurrence. For this study it was assumed there was no variation on extent and density of tree-cast shade on each home between current year and previous year. This assumption was cross-checked with par- ticipants by asking them to report any landscaping changes in their yard/lot. Participant responses indicated that this assumption was realistic, which, in turn, allowed the study authors to double the sample size by using the previous-year electricity usage informa- tion with the tree shade conditions measured during the current year. Several attributes of the residences in the sample and the oc- cupants of those residences are invariant across the entire year of the study period. Sample statistics for these variables are reported in Table 1a. Sample statistics for time-dependent vari- ables are reported in Table 1b. Sample statistics for categorical variables by utility or structural types are reported in Table 1c. Figure 1 shows average daily electricity consumption over the period May–September. It will come as no sur- prise that electricity consumption rises throughout June and ©2010 International Society of Arboriculture
March 2010
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