The first two essays in this Dissertation suggest and demonstrate state-of-the-art econometric methods for forecasting of carbon dioxide emissions using a large panel data set covering the fifty U.S. States and Washington D.C. from 1960--2001. Forecasts of CO₂ emissions are an important input into climate science and policy, yet there are a wide variety of models and forecasts existing in the literature. The essay in Chapter 2 tests the performance of existing models against a large universe of potential reduced form models. Unlike the existing literature, our statistical tests of model superiority account for the model search or 'snooping' involved in identifying a preferred model. Our objective measure of model performance is the out-of-sample prediction of actual CO₂ emissions. We find that leading models in the literature, as well as models selected from our universe based on in-sample or per capita emissions criteria perform significantly worse compared to the best model chosen based directly on the out-of-sample total emission loss measure. Our forecasts of aggregate CO₂ emissions for the United States are 100 to 150 million tons of carbon lower than the average results of models in the IPCC's Emission Scenario Database.

The essay in Chapter 3 provides comparisons of a variety of time series methods for short run forecasts of carbon dioxide for the United States. We test the out-of-sample performance of univariate and multivariate forecasting models by aggregating state level forecasts versus forecasting the aggregate directly. We find evidence that forecasting the disaggregate series and accounting for spatial effects drastically improves forecasting performance under Root Mean Squared Forecast Error Loss. Based on the in-sample observations we attempt to explain the emergence of voluntary efforts by states to reduce greenhouse gas emissions. We find evidence that states with decreasing per capita emissions and a "greener" median voter are more likely to push toward voluntary cutbacks in emissions.

The final essay in this dissertation expands on the theme of voluntary environmental and socially responsible behavior. In contrast to Chapter 3 where we attempt to explain the actions of states, the focus in Chapter 4 is on the voluntary actions of corporate managers. The separation of ownership and control in corporations opens up the potential for moral hazard. Thus it is conventional wisdom that managers who are not closely monitored pursue personal goals rather then maximize shareholder wealth. Yet little is known about what these goals are, despite the importance of understanding manager behavior when designing corporate governance rules. This chapter provides new insights into managers' personal preferences by studying the variations in corporate environmental and social performance associated with different corporate governance provisions. We employ a unique dataset on corporate governance and corporate social responsibility and exploit variations in takeover defenses to analyze differences in managers' behavior. We find that with weaker governance, more resources are allocated into environmentally and socially responsible objectives and away from core responsibilities. These findings support a theory that ethical principles are important for the subjective well-being of managers. Findings in this essay are relevant to the current policy debate on stronger corporate governance rules.