We study the impacts of bankruptcy risk on the performance of market-based pollution control policies. In chapter one, we concentrate on emissions trading markets. We find that firms that risk bankruptcy demand more permits than if they were financially secure. Thus, bankruptcy risk in a competitive market for tradable permits causes an inefficient distribution of these permits among firms. Moreover, the equilibrium distribution of permits is dependent on the initial allocation of permits. Thus, the main reasons for implementing emissions trading markets do not hold when some firms are financially insecure. In fact, the inefficiency that is associated with bankruptcy risk is worsened if financially insecure firms are given a smaller share of the initial allocation of permits. In chapter two, we investigate the influences of bankruptcy risk on imperfectly enforced emissions taxes. Under favorable, but not unrealistic conditions, an imperfectly enforced emissions tax produces an efficient allocation of individual emissions control; the aggregate level of control is the same whether enforcement of a tax is sufficient to induce the full compliance of firms or not, and differences in individual violations are independent of firm-level differences. All of these desirable characteristics disappear when some firms under an emissions tax risk bankruptcy—the allocation of emissions control is inefficient, imperfect enforcement causes higher aggregate emissions, and financially insecure firms choose higher violations.

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published_or_final_version / Speech and Hearing Sciences / Master / Master of Science in Audiology

Traditionally, transportation road networks have been designed for minimal congestion. Unfortunately, such approaches do not guarantee minimal vehicle emissions. Given the negative impacts of vehicle pollutants as well as tighter national air quality standards, it is critical for regions to be able to identify capacity modifications to road networks such that vehicle emissions are minimal. This ability combined with land use changes and opportunities for non-auto travel are paramount in helping regions improve air quality. However, network design research has yet to directly address this topic. To fill this apparent gap in network design research, an emissions network design problem and solution method are proposed in this thesis. Three air pollutants are considered: hydrocarbons, nitrogen oxides, and carbon monoxide. The proposed model is applied to two road networks: Sioux Falls, ND and Anaheim, CA. The model is a bi-level optimization problem solved using a genetic algorithm and incorporates the influence of demand uncertainty. Findings indicate designing for minimal congestion tends to increase emissions of criteria air pollutants. However, not adding capacity to a road network also increases emissions of pollutants. Therefore, an optimization problem and solution method, such as the model presented here, is useful for identifying capacity additions that reduce vehicle emissions. It is also useful for understanding the tradeoffs between designing a network for minimal congestion versus minimal vehicle emissions. / text

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