The focus of my dissertation is in two areas: modeling recreation behavior with limited information and the interaction between two greenhouse mitigation instruments in the power market.

My first dissertation chapter, entitled "Modeling Recreation Demand when the Access Point is Unknown", seeks to use the aggregation technique to model Iowan's riverine recreation behavior without knowing their detailed access points. The task of modeling the recreation demand for geographically large sites, such as rivers and beaches or large parks with multiple entrances, is often challenged by incomplete information regarding the access point used by the individual. Traditionally, analysts have relied upon convenient approximations, defining travel time and travel distances on the basis of the midpoint of a river or beach segment or on the basis of the nearest access point to the site for each individual. In this paper, we instead treat the problem as one of aggregation, drawing upon and generalizing results from the aggregation literature. The resulting model yields a consistent framework for incorporating information on site characteristics and travel costs gathered at a finer level than that used to obtain trip counts. We use a series of Monte Carlo experiments to illustrate the performance of the traditional mid-point and nearest access point approximations. Our results suggest that, while the nearest access point approach provides a relatively good approximation to underlying preferences for a wide range of parameter specifications, use of the midpoint approach to calculating travel cost can lead to significant bias in the travel cost parameter and corresponding welfare calculations. Finally, we use our approach in modeling recreation demand for the major river systems in Iowa using data from the 2009 Iowa Rivers and River Corridors Survey.

The second paper in my dissertation, entitled "Modeling Recreation with Partial Trip Information", tries to use the same aggregation technique in another set of situations with partial information about residents' visitation patterns. Full information about visitation pattern to all the related recreational sites is unavailable with surveys yielding trip information to a subset of possible sites. Conventional methods tend to focus on the sites with trip information and discard the sites with partial trip information. In this paper, we treat the partial information as an aggregation choice for this group of sites. In doing so, a similar aggregation modeling technique is proposed then, under some circumstances, allows one to recover preference parameters and avoid the possible bias caused by the conventional methods. A series of Monte Carlo simulations are conducted to study the possible bias caused by conventional methods and the performance of the aggregation model when the application is possible. The results show that the aggregation model performs quite well in recovery of preference and subsequent welfare analysis. Both methods are applied to data from 2009 Iowa lake and river projects. The results show that both methods give qualitatively similar preference parameters but produce significant differences in terms of the welfare measures.

The third paper in my dissertation, entitled "Carbon Tax, Wind Energy and GHG reduction - ERCOT as an Example", seeks to evaluate the performance of two greenhouse gas intervention policies in the exas ERCOT, power market.In the battle to control the greenhouse gas (GHG) emission, a prominent component contributing to the climate change, there are several schemes already taken by governments. Direct targeted policies, such as cap-and-trade program or a potential carbon tax, and indirect policies, such as promotion of renewable energies are receiving governments endorsements worldwide. With data from the Texas ERCOT power market, we develop a simple electricity generation dispatch model to analyze the relative performance in emission reduction when a carbon tax and significant amount of wind generation co-exist in the power grid. The simulation results show that during the research period, both policies have significant effects on reduction of carbon dioxide emission under hypothetical policy scenarios. The combination of a carbon tax policy and the promotion of wind energy seems more effective to achieve big reduction targets in the short run.