| My dissertation seeks to analyze environmental policy using theoretical, computational, and empirical methods. In the first chapter I develop a Bayesian learning framework for damage functions in integrated assessment models that mimics how modelers have historically updated damage functions. To allow for the model to be solved in a reasonable timeframe I must use sparse grid methods for dynamic programming which are new to climate economics. I also use robust control techniques from the macroeconomics literature to capture concerns that there are errors in integrated assessment models that we will not be able to resolve in a timely fashion. Using these methodological advances, I demonstrate that the convention of updating the calibration of damage functions while maintaining a fixed functional form can backfire and reduce ex-post welfare if the damage function is misspecified like many economists believe. Moreover, accounting for misspecification concerns with robust control can exacerbate the backfire and further reduce ex-post welfare. In my second chapter I analyze the impacts of credit trading under renewable portfolio standards. Specifically, I look at how a change in one state's renewable portfolio standard can propagate through this credit channel and result in reductions in fossil fuel usage in another state. I find that a 1 MWh increase in extra-jurisdictional demand for renewable energy credits leads to a reduction in energy production derived from coal usage by 2 mmbtus and a reduction in CO2 emissions by 0.285 metric tons. In my last chapter I develop an analytic model for renewable energy credit trading to investigate why states have peculiar trading rules for the credits. I find that, counter to conventional economic wisdom, states may actually not want to engage in credit trading. Credit trading may in fact worsen in state pollution to an extent that completely offsets any gains from trade. |
