Essays on environmental and energy economics
Keywords: Economics.

This dissertation studies the household-level demand model for water and electricity, with the three chapters focusing on different aspects of the demand model. The first chapter, co-authored with my advisor, Frank Wolak, formulates and estimates a household-level, billing-cycle water demand model under increasing block prices that accounts for the impact of monthly weather variation, the amount of vegetation on the household's property, and customer-level heterogeneity in demand due to household demographics. The model utilizes US Census data on the distribution of household demographics in the utility's service territory to recover the impact of these factors on water demand. An index of the amount of vegetation on the household's property is obtained from NASA satellite data. The household-level demand models are used to compute the distribution of utility-level water demand and revenues for any possible price schedule. It can be used to design nonlinear pricing plans that achieve competing revenue or water conservation goals, which is crucial for water utilities to manage increasingly uncertain water availability yet still remain financially viable. Knowledge of how these demands differ across customers based on observable household characteristics can allow the utility to reduce the utility-wide revenue or sales risk it faces for any pricing plan. Knowledge of how the structure of demand varies across customers can be used to design personalized (based on observable household demographic characteristics) increasing block price schedules to further reduce the risk the utility faces on a system-wide basis. For the utilities considered, knowledge of the customer-level demographics that predict demand differences across households reduces the uncertainty in the utility's system-wide revenues from 22 to 84 percent. Further reductions in the uncertainty in the utility's system-wide revenues, in the range of 10 to 79 percent, are possible by re-designing the utility's nonlinear price schedules to minimize the revenue risk it faces given the distribution of household-level demand in its service territory. The second chapter, co-authored with Frank again, estimates a model of the household-level demand for electricity services such as lighting, heating and cooling, home appliances, and business use in the Indian state of Rajasthan using a combination of household-level survey data and administrative data. This model incorporates customer-level demographic characteristics, billing cycle-level weather variables, and the fact that households are subject to electricity outages and face increasing block price schedules for their electricity consumption. We estimate two versions of the model that differ in how the relationship between electricity use and consumption of each electricity service is modeled. The first model uses a shape-constrained kernel regression and the second model uses a customer-level constant elasticity of electricity consumption with respect to energy service model. Both energy service demand models produce estimates of the response of each of the above four categories of energy services to changes in the price of each energy service. Both versions of the model also produce estimates of the marginal willingness to pay for an additional hour of each of the four categories of energy services. The mean marginal willingness to pay across customers for an additional hour an energy service is the smallest for lighting and the largest for home appliance services. The third chapter studies whether consumers respond to increasing block tariffs. Although increasing block tariffs have been widely adopted by water and electricity utilities, some previous literature claims that consumers only respond to the average price, rather than the increasing block tariffs or the marginal price. In this chapter, we examine the empirical strategies proposed by previous literature, and test whether they are sufficient to conclude if consumers respond to the increasing block tariffs or other perceived prices. We utilize the household-level demand model in the first chapter that responds to the entire price schedule, including all price tiers and quantity cutoffs. We construct a dataset with consumption data simulated using this model. Applying empirical strategies proposed by previous literature to the simulated dataset fails to identify the underlying demand model, and still concludes that consumers respond to the average price. This suggests that current empirical evidences are not sufficient to exclude that consumers respond to the increasing block tariffs. Further investigations are needed to understand the water/electricity consumption decision