Ozone formation is a complex, non-linear process that depends on the atmospheric concentrations of its precursors, nitrogen oxide (NOx) and Volatile Organic Compounds (VOC), as well as on temperature and the available amount of sunlight. The dependence of ozone formation on meteorology makes the timing of emissions important, suggesting the need for a temporally differentiated regulation for NOx emissions. Such a flexible NOx regulation policy, so-called "smart trading", which is designed to target ozone episodes by reducing NOx emissions prior to and during forecasted episodes, has the potential for reducing the compliance cost and helping to solve the persistent ozone non-attainment problem in the Eastern United States. However, the total compliance cost of this new policy depends critically on the accuracy of ozone forecasting.

This thesis aims to address the question of whether a NOx trading program that differentiates across emissions by time could reduce ozone concentrations more cost-effectively than the conventional command-and-control method in the Eastern U.S. given the uncertainty in ozone forecasting. A cost-effectiveness analysis is conducted to compare the average cost for achieving a certain amount of ozone reduction under the proposed smart trading plan and the command-and-control policy. The probability distribution of the compliance cost under a smart trading policy is simulated using a stochastic decision model based on the simulated electricity generation and air quality data. This study demonstrates the scientific and economic feasibility of a time-differentiated trading scheme. I explore whether such a regulatory design is justifiable with respect to ozone forecast accuracy by conducting sensitivity analysis of ozone prediction errors and discover that uncertainty in ozone forecasting may not be a major limiting factor for the feasibility of a time-differentiated NOx cap-and-trade program.