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This study addresses a classic sustainability challenge—the tradeoff between water conservation and temperature amelioration in rapidly growing cities, using Phoenix, Arizona and Portland, Oregon as case studies. An urban energy balance model— LUMPS (Local-Scale Urban Meteorological Parameterization Scheme)—is used to represent the tradeoff between outdoor water use and nighttime cooling during hot, dry summer months. Tradeoffs were characterized under three scenarios of land use change and three climate-change assumptions. Decreasing vegetation density reduced outdoor water use but sacrificed nighttime cooling. Increasing vegetated surfaces accelerated nighttime cooling, but increased outdoor water use by ~20%. Replacing impervious surfaces with buildings achieved similar improvements in nighttime cooling with minimal increases in outdoor water use; it was the most water-efficient cooling strategy. The fact that nighttime cooling rates and outdoor water use were more sensitive to land use scenarios than climate-change simulations suggested that cities can adapt to a warmer climate by manipulating land use.
In order to determine what characteristics influence participation in this program I gathered data from multiple sources. I use a panel dataset of household water consumption that spans 12 years of approximately 300,000 homes. I merged this dataset with home structural characteristics, geographical, and demographic context. I then use these characteristics in a linear probability model, with school enrollment zone fixed effects to determine their influence on a household’s probability of participation. School zones are used to control for unobserved characteristics, such as demographics, which are not at a household level. I then utilize these school zone fixed effects in a 2nd stage regression to decompose these elements and analyze their effect on participation.
I find that a household’s water costs, as reflected in the marginal price faced in the summer and the differential between summer and winter water bills, as well as yard size are primary factors that influence participation. I also show that changes in rebate value and water rates can affect different types of households. There is also evidence to support that neighborhood characteristics affect a household’s likelihood of participating.