This dissertation consists of three separate papers that either derive optimal management strategies for environmental and natural resource management, develop theoretical models explaining observed effects government management of environmental and natural resources or both. Chapter 1 shows that sharing arrangements between capital owners and labor in renewable resource industries have substantial implications for the industry's profits, optimal resource management, and the resource's ecological state. Effectively, sharing agreements can interact with fluctuations in natural capital to cause inefficient investment levels and skew industry rents toward labor. As a consequence, optimal regulatory policy for such industries must account for the implications of such sharing arrangements. The model demonstrates why management tools like individual transferable quotas in fisheries, have had unexpected ecological benefits in terms of increasing and stabilizing fishery stocks. Chapter 2 extends the research joint venture (RJV) literature to cover government funded energy-related collaborations between private firms and national laboratories. It uses a game theoretic framework to explain why a RJV including a national lab will tend to have significantly more participants than a private RJV. The model predicts that regulatory capture is likely to occur from firms that work with national labs in RJVs and receive exogenous funding, such as federal grants, to perform RJV research. Further, it is possible that RJVs including national labs that do not receive exogenous funding are more likely to consist of more heterogeneous firms. Chapter 3 considers the implications on optimal pollution control when ambient levels of pollution are known but all emission sources are not. The model shows that if the dispersion models are misspecified due to incomplete inventories of emissions, optimal ambient pollution levels can actually increase. In this case, if R &D can increase the set of known emitters, a regulator may actually choose not to spend any resources to do as it can cause a decrease in expected welfare

Ecological systems are dynamic and policies to manage them need to respond to that variation. However, policy adjustments will sometimes be costly, which means that fine-tuning a policy to track variability in the environment very tightly will only sometimes be worthwhile. We use a classic fisheries management problem, how to manage a stochastically varying population using annually varying quotas in order to maximize profit, to examine how costs of policy adjustment change optimal management recommendations. Costs of policy adjustment (changes in fishing quotas through time) could take different forms. For example, these costs may respond to the size of the change being implemented, or there could be a fixed cost any time a quota change is made. We show how different forms of policy costs have contrasting implications for optimal policies. Though it is frequently assumed that costs to adjusting policies will dampen variation in the policy, we show that certain cost structures can actually increase variation through time. We further show that failing to account for adjustment costs has a consistently worse economic impact than would assuming these costs are present when they are not.

Environmental and resource economists often use models that include uncertainty and ways to reduce that uncertainty through learning. Using a standard environmental and resource economics framework, this article parses several different forms of uncertainty and learning that are commonly considered in the literature. We then review the applied theory literature using that framework to assess whether there is support for four hypotheses associated with uncertainty and learning in environmental management that have been raised in policy circles. We find that these hypotheses are often true for one type of uncertainty or learning but not another, highlighting how a lack of clarity can lead to confusion among researchers and policymakers.