74 Pandit and Laband: Impact of Tree Shade on Summertime Residential Energy Consumption with no shade/trees] to analyze energy savings as a result of tree shade. Their findings for different shade categories indi- cated that the amount of shade, roof color, and wall color were significant determinants of residential energy consumption. However, Jensen et al. (2003) found a different impact of tree shade on residential energy consumption using a Leaf Area Index (LAI) measure derived from remote sensing data. In their study, Jensen’s team randomly selected 118 LAI points to represent Terre Haute, IN and regressed the residential energy consumption against LAI values. The regression estimation produced statistically insig- nificant results, contradicting the strong and significant role of shade trees on residential energy consumption revealed by other studies. Drawing from a sample of 160 residences in Auburn, Alabama, a statistical model was developed to produce specific estimates of the electricity savings generated by shade-producing trees in a suburban environment. This empirical model links residential energy consumption to hedonic characteristics of the structures, characteristics/behaviors of the occupants, and the extent and density of shade cast on the structures at different times of the day. METHODOLOGY Study Area The study authors selected the city of Auburn, Alabama, locat- ed at 32°37’N latitude and 85°29’W longitude, to conduct this study for both climatic and demographic reasons. The climatic conditions of Auburn are characteristic of the Gulf Coast region generally, where winters are cool but short, and summers are long, hot, and humid. Lechner (1991) concluded that 12% of the days each year are regarded as ‘comfortable,’ 36% of the days are regarded as ‘too cold,’ and 52% of the days are regarded as ‘too hot.’ The average annual heating and cooling degree-days for this region are 1,490 and 2,686, respectively (Lechner 1991). Consequently, in Auburn, cooling energy needed during the sum- mer is far greater than heating energy needed during the winter. The changing demography of Auburn coupled with its fast- paced economic growth in recent years also fits the study’s needs in two respects. First, the findings may be useful to home builders in terms of integrating shade trees in the planning process for new residential construction in order to reduce the energy requirement for new homes, particularly during summer months. In January 2008, Forbes Magazine (2008) named the Auburn-Opelika Met- ropolitan Statistical Area (MSA) the sixth fastest-growing MSA in the U.S. for small metros and projected Auburn’s 2007 popu- lation (54,348) would grow by more than 17% and its current Gross Metropolitan Product (GMP), the market value of all final goods and services produced within a metropolitan area, would increase by more than 24% by the year 2012. Second, the mix of recently developed neighborhoods and older neighborhoods within the city of Auburn provides the range of shade conditions needed for this study—houses with little-to-no tree canopy to houses with substantial tree canopy. Camden Ridge, New Grove Hill, Ogletree Village, and North Donahue are the recently de- veloped neighborhoods in the city, which either have no trees or have relatively few and small trees per lot that cast little, if any, shade on residences. In contrast, Willow Creek, Twin Creeks, Sugar Creek, Moore’s Mill, Old Grove Hill, and Cary Woods are older neighborhoods, which have many and large trees that cast substantial shade on residences. The study included houses ©2010 International Society of Arboriculture from all of these neighborhoods, to ensure that a wide range of tree shade conditions are represented in the sample. Even within each neighborhood, there is substantial variation between resi- dences with respect to the extent and density of tree-cast shade. Data Collection The study authors used a hedonic model as the analytical frame- work. Specifically they employed multivariate regression esti- mation techniques to estimate the impact of shade conditions on monthly electricity consumption, controlling for a variety of other factors that also affect monthly electricity usage such as: size of family, gender and age distribution of occupants, loads of laundry per week, dwelling characteristics (e.g., age, square footage, num- ber of floors), and energy sources for cooking and water heating. The information needed came from two sources: 1) the resi- dents themselves (in response to a survey questionnaire and through submission of monthly electric bills), and 2) direct ob- servation of shade conditions on the properties in the sample. Survey Questionnaire A two step approach was adopted in identifying sample partici- pants. First, an invitation letter for participation and a question- naire was personally delivered to the door knobs or mailboxes of semi-randomly selected potential homeowners. The selection of neighborhoods was deliberate, in order to reflect substantial varia- tion in tree shade conditions. Within each neighborhood, however, distribution of invitations was random, whereas every other home received an invitation. The invitation letters explained the nature and scope of the study and provided relevant contact information for the respondent to use to indicate their willingness to participate. Second, based on the email or telephone contact from the participants, who indicated their willingness to participate in the study, a meeting with each of the potential participants was scheduled at their convenience to: 1) indicate the range of in- formation required from the participant, including information from their monthly electricity bill, 2) have them approve ac- cess by the researchers to their property for shade monitoring every month, 3) address any questions/concerns they had about the study, and finally, 4) collect the completed questionnaire that contained information on occupants (number, age, gender), structural characteristics of the house (e.g., age, living area, story, swim pool, outside wall color, and materials), and utility types for heating/cooling their house (e.g., electricity, natural gas, others). The final sample of 160 homeowners located in 11 Auburn City neighborhoods represented 8.3% of the homeowners initially contacted. This sample size was sufficient to reflect the complete range of shade conditions (both extent and density) on properties, as well as the other explanatory variables in the model and to pro- vide a statistically valid sample size for a regression estimation. Monthly Field Visit for Shade Characteristics Each sample house was visited each month to record the extent and density of shade cast on each house by nearby tree canopy. Information on the extent and density of shade on each house was collected at three different times in a sunny day—late a.m. (9:00–11:00 a.m.), early p.m. (noon–2:00 p.m.), and late p.m. (3:00–5:00 p.m.)—around the middle of each month throughout the year to explore the timing effect of shade on power consump- tion. An ocular observation was used to estimate both the extent
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